From 1a497f174c21993e2464725d96482e2a4a52758b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Sep 2024 10:08:02 +0200 Subject: [PATCH 01/17] Bump slackapi/slack-github-action from 1.26.0 to 1.27.0 in /.github/workflows (#15945) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yaml | 2 +- .github/workflows/docker.yaml | 2 +- .github/workflows/publish.yml | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index c079a5a075..11bc4c7771 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -344,7 +344,7 @@ jobs: steps: - name: Check for failure and notify if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.GPU.result == 'failure' || needs.RaspberryPi.result == 'failure' || needs.Conda.result == 'failure' ) && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push') - uses: slackapi/slack-github-action@v1.26.0 + uses: slackapi/slack-github-action@v1.27.0 with: payload: | {"text": " GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"} diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml index 6cd7b5ca46..5242d9efa6 100644 --- a/.github/workflows/docker.yaml +++ b/.github/workflows/docker.yaml @@ -175,7 +175,7 @@ jobs: - name: Notify on failure if: github.event_name == 'push' && failure() # do not notify on cancelled() as cancelling is performed by hand - uses: slackapi/slack-github-action@v1.26.0 + uses: slackapi/slack-github-action@v1.27.0 with: payload: | {"text": " GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"} diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index e0bb3fa38e..3cb83f27fd 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -127,7 +127,7 @@ jobs: echo "PR_TITLE=$PR_TITLE" >> $GITHUB_ENV - name: Notify on Slack (Success) if: success() && github.event_name == 'push' && steps.check_pypi.outputs.increment == 'True' - uses: slackapi/slack-github-action@v1.26.0 + uses: slackapi/slack-github-action@v1.27.0 with: payload: | {"text": " GitHub Actions success for ${{ github.workflow }} ✅\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* NEW '${{ github.repository }} ${{ steps.check_pypi.outputs.current_tag }}' pip package published 😃\n*Job Status:* ${{ job.status }}\n*Pull Request:* ${{ env.PR_TITLE }}\n"} @@ -135,7 +135,7 @@ jobs: SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }} - name: Notify on Slack (Failure) if: failure() - uses: slackapi/slack-github-action@v1.26.0 + uses: slackapi/slack-github-action@v1.27.0 with: payload: | {"text": " GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n*Job Status:* ${{ job.status }}\n*Pull Request:* ${{ env.PR_TITLE }}\n"} From 5b3e60b4525f1b757bf4b1b8d78a273f7133e358 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 2 Sep 2024 20:00:02 +0200 Subject: [PATCH 02/17] Continue on Conda CI error (#15958) --- .github/workflows/ci.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 11bc4c7771..0ea3a5a1ec 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -269,6 +269,7 @@ jobs: Conda: if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true') + continue-on-error: true runs-on: ${{ matrix.os }} strategy: fail-fast: false From 5a6db149e45a4ff4efef6d970a87e0693b8271e2 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 3 Sep 2024 11:10:09 +0200 Subject: [PATCH 03/17] Update `test_workflow` to ONNX (#15974) --- tests/test_python.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_python.py b/tests/test_python.py index f15dd48eff..8fbab54cf2 100644 --- a/tests/test_python.py +++ b/tests/test_python.py @@ -202,7 +202,7 @@ def test_workflow(): model.train(data="coco8.yaml", epochs=1, imgsz=32, optimizer="SGD") model.val(imgsz=32) model.predict(SOURCE, imgsz=32) - model.export(format="torchscript") + model.export(format="onnx") def test_predict_callback_and_setup(): From 7c7f456710d8f4c538453fda96fb0ea1d4e84d7a Mon Sep 17 00:00:00 2001 From: Laughing <61612323+Laughing-q@users.noreply.github.com> Date: Tue, 3 Sep 2024 18:03:01 +0800 Subject: [PATCH 04/17] Fix `torch.cuda.amp.GradScaler` warning (#15978) Co-authored-by: UltralyticsAssistant --- ultralytics/engine/trainer.py | 5 ++++- ultralytics/utils/torch_utils.py | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py index c5b8a13f30..2d5fc62461 100644 --- a/ultralytics/engine/trainer.py +++ b/ultralytics/engine/trainer.py @@ -42,6 +42,7 @@ from ultralytics.utils.checks import check_amp, check_file, check_imgsz, check_m from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command from ultralytics.utils.files import get_latest_run from ultralytics.utils.torch_utils import ( + TORCH_2_4, EarlyStopping, ModelEMA, autocast, @@ -265,7 +266,9 @@ class BaseTrainer: if RANK > -1 and world_size > 1: # DDP dist.broadcast(self.amp, src=0) # broadcast the tensor from rank 0 to all other ranks (returns None) self.amp = bool(self.amp) # as boolean - self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + self.scaler = ( + torch.amp.GradScaler("cuda", enabled=self.amp) if TORCH_2_4 else torch.cuda.amp.GradScaler(enabled=self.amp) + ) if world_size > 1: self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[RANK], find_unused_parameters=True) diff --git a/ultralytics/utils/torch_utils.py b/ultralytics/utils/torch_utils.py index c2338e184b..16bcddadd0 100644 --- a/ultralytics/utils/torch_utils.py +++ b/ultralytics/utils/torch_utils.py @@ -40,6 +40,7 @@ except ImportError: TORCH_1_9 = check_version(torch.__version__, "1.9.0") TORCH_1_13 = check_version(torch.__version__, "1.13.0") TORCH_2_0 = check_version(torch.__version__, "2.0.0") +TORCH_2_4 = check_version(torch.__version__, "2.4.0") TORCHVISION_0_10 = check_version(TORCHVISION_VERSION, "0.10.0") TORCHVISION_0_11 = check_version(TORCHVISION_VERSION, "0.11.0") TORCHVISION_0_13 = check_version(TORCHVISION_VERSION, "0.13.0") From b2ab667f0086c501a5fc4620049c8bf3c9646e85 Mon Sep 17 00:00:00 2001 From: TechWolf21 Date: Tue, 3 Sep 2024 16:08:23 +0530 Subject: [PATCH 05/17] Fix queue `counts` (#15971) Co-authored-by: UltralyticsAssistant Co-authored-by: Muhammad Rizwan Munawar Co-authored-by: RizwanMunawar Co-authored-by: Glenn Jocher --- ultralytics/solutions/queue_management.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ultralytics/solutions/queue_management.py b/ultralytics/solutions/queue_management.py index f3536d30eb..16df28b1bb 100644 --- a/ultralytics/solutions/queue_management.py +++ b/ultralytics/solutions/queue_management.py @@ -89,7 +89,7 @@ class QueueManager: """Extracts and processes tracks for queue management in a video stream.""" # Initialize annotator and draw the queue region self.annotator = Annotator(self.im0, self.tf, self.names) - + self.counts = 0 # Reset counts every frame if tracks[0].boxes.id is not None: boxes = tracks[0].boxes.xyxy.cpu() clss = tracks[0].boxes.cls.cpu().tolist() @@ -132,7 +132,6 @@ class QueueManager: txt_color=self.count_txt_color, ) - self.counts = 0 # Reset counts after displaying self.display_frames() def display_frames(self): From 3fc14b5fec93f365e7a2318e410b79f879cf5004 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 3 Sep 2024 12:43:48 +0200 Subject: [PATCH 06/17] `ultralytics 8.2.87` Ray `shutdown()` workers after tuning (#15976) --- tests/test_python.py | 2 +- ultralytics/__init__.py | 2 +- ultralytics/utils/tuner.py | 9 +++++++-- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/tests/test_python.py b/tests/test_python.py index 8fbab54cf2..f15dd48eff 100644 --- a/tests/test_python.py +++ b/tests/test_python.py @@ -202,7 +202,7 @@ def test_workflow(): model.train(data="coco8.yaml", epochs=1, imgsz=32, optimizer="SGD") model.val(imgsz=32) model.predict(SOURCE, imgsz=32) - model.export(format="onnx") + model.export(format="torchscript") def test_predict_callback_and_setup(): diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index e1bbd65619..37925ac9e7 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -__version__ = "8.2.86" +__version__ = "8.2.87" import os diff --git a/ultralytics/utils/tuner.py b/ultralytics/utils/tuner.py index 1d52e3e0b4..1329bfe6ec 100644 --- a/ultralytics/utils/tuner.py +++ b/ultralytics/utils/tuner.py @@ -143,5 +143,10 @@ def run_ray_tune( # Run the hyperparameter search tuner.fit() - # Return the results of the hyperparameter search - return tuner.get_results() + # Get the results of the hyperparameter search + results = tuner.get_results() + + # Shut down Ray to clean up workers + ray.shutdown() + + return results From 782d5bddc65a53aaeab0ee6d83f4ee5d7590cc46 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 3 Sep 2024 22:00:52 +0200 Subject: [PATCH 07/17] Update `mkdocs-ultralytics-plugin>=0.1.8` (#15991) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 9fae825d80..00366df58f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ dev = [ "mkdocstrings[python]", "mkdocs-jupyter", # notebooks "mkdocs-redirects", # 301 redirects - "mkdocs-ultralytics-plugin>=0.1.6", # for meta descriptions and images, dates and authors + "mkdocs-ultralytics-plugin>=0.1.8", # for meta descriptions and images, dates and authors "mkdocs-macros-plugin>=1.0.5" # duplicating content (i.e. export tables) in multiple places ] export = [ From 5dce4917e0ce01f18e25256c8082b7baac05120f Mon Sep 17 00:00:00 2001 From: Lakshantha Dissanayake Date: Tue, 3 Sep 2024 13:15:06 -0700 Subject: [PATCH 08/17] Remove Raspberry Pi CI reboot (#15987) Co-authored-by: Glenn Jocher --- .github/workflows/ci.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 0ea3a5a1ec..b25f89790e 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -264,8 +264,9 @@ jobs: run: | cat benchmarks.log echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY - - name: Reboot # run a reboot command in the background to free resources for next run and not crash main thread - run: sudo bash -c "sleep 10; reboot" & + # The below is fixed in: https://github.com/ultralytics/ultralytics/pull/15987 + # - name: Reboot # run a reboot command in the background to free resources for next run and not crash main thread + # run: sudo bash -c "sleep 10; reboot" & Conda: if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true') From 7a7ff4ccab06604c2e101eefdd1c7c1644132dac Mon Sep 17 00:00:00 2001 From: Lorna Date: Wed, 4 Sep 2024 16:22:16 +0800 Subject: [PATCH 09/17] Fix 3 `Objects365.yaml` class names (#16002) --- ultralytics/cfg/datasets/Objects365.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ultralytics/cfg/datasets/Objects365.yaml b/ultralytics/cfg/datasets/Objects365.yaml index 9b117206f0..4994fd5f29 100644 --- a/ultralytics/cfg/datasets/Objects365.yaml +++ b/ultralytics/cfg/datasets/Objects365.yaml @@ -113,7 +113,7 @@ names: 95: Pot 96: Cow 97: Cake - 98: Dinning Table + 98: Dining Table 99: Sheep 100: Hanger 101: Blackboard/Whiteboard @@ -304,7 +304,7 @@ names: 286: Hammer 287: Cue 288: Avocado - 289: Hamimelon + 289: Hami melon 290: Flask 291: Mushroom 292: Screwdriver @@ -328,7 +328,7 @@ names: 310: Dishwasher 311: Crab 312: Hoverboard - 313: Meat ball + 313: Meatball 314: Rice Cooker 315: Tuba 316: Calculator From fcb73595eca116cbcdd4823bbbeabd16181b3314 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 4 Sep 2024 11:18:27 +0200 Subject: [PATCH 10/17] Add UltralyticsAssistant to mkdocs_github_authors.yaml (#15992) Co-authored-by: UltralyticsAssistant --- docs/mkdocs_github_authors.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml index 6453f86569..b323b4a39e 100644 --- a/docs/mkdocs_github_authors.yaml +++ b/docs/mkdocs_github_authors.yaml @@ -115,6 +115,9 @@ sometimesocrazy@gmail.com: stormsson@users.noreply.github.com: avatar: https://avatars.githubusercontent.com/u/1133032?v=4 username: stormsson +web@ultralytics.com: + avatar: https://avatars.githubusercontent.com/u/135830346?v=4 + username: UltralyticsAssistant xinwang614@gmail.com: avatar: https://avatars.githubusercontent.com/u/17264618?v=4 username: GreatV From 88102eb5085a668fff0171fdaa6297af65c2d010 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 4 Sep 2024 14:14:06 +0200 Subject: [PATCH 11/17] Skip `test_workflow` on Windows CI (#16003) Signed-off-by: UltralyticsAssistant Co-authored-by: UltralyticsAssistant --- tests/test_python.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_python.py b/tests/test_python.py index f15dd48eff..aa18029d75 100644 --- a/tests/test_python.py +++ b/tests/test_python.py @@ -196,13 +196,14 @@ def test_all_model_yamls(): YOLO(m.name) +@pytest.mark.skipif(WINDOWS, reason="Windows slow CI export bug https://github.com/ultralytics/ultralytics/pull/16003") def test_workflow(): """Test the complete workflow including training, validation, prediction, and exporting.""" model = YOLO(MODEL) model.train(data="coco8.yaml", epochs=1, imgsz=32, optimizer="SGD") model.val(imgsz=32) model.predict(SOURCE, imgsz=32) - model.export(format="torchscript") + model.export(format="torchscript") # WARNING: Windows slow CI export bug def test_predict_callback_and_setup(): From 1e604e0d1c74863001debdb1992471af4b030868 Mon Sep 17 00:00:00 2001 From: Lakshantha Dissanayake Date: Thu, 5 Sep 2024 05:18:01 -0700 Subject: [PATCH 12/17] Update TFLite > LiteRT docs links (#16020) Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- docs/en/integrations/tflite.md | 14 +++++++------- docs/mkdocs_github_authors.yaml | 3 +++ 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/docs/en/integrations/tflite.md b/docs/en/integrations/tflite.md index d1bba90346..01c40df2e2 100644 --- a/docs/en/integrations/tflite.md +++ b/docs/en/integrations/tflite.md @@ -16,7 +16,7 @@ The TensorFlow Lite or TFLite export format allows you to optimize your [Ultraly ## Why should you export to TFLite? -Introduced by Google in May 2017 as part of their TensorFlow framework, [TensorFlow Lite](https://www.tensorflow.org/lite/guide), or TFLite for short, is an open-source deep learning framework designed for on-device inference, also known as edge computing. It gives developers the necessary tools to execute their trained models on mobile, embedded, and IoT devices, as well as traditional computers. +Introduced by Google in May 2017 as part of their TensorFlow framework, [TensorFlow Lite](https://ai.google.dev/edge/litert), or TFLite for short, is an open-source deep learning framework designed for on-device inference, also known as edge computing. It gives developers the necessary tools to execute their trained models on mobile, embedded, and IoT devices, as well as traditional computers. TensorFlow Lite is compatible with a wide range of platforms, including embedded Linux, Android, iOS, and MCU. Exporting your model to TFLite makes your applications faster, more reliable, and capable of running offline. @@ -107,17 +107,17 @@ For more details about the export process, visit the [Ultralytics documentation After successfully exporting your Ultralytics YOLOv8 models to TFLite format, you can now deploy them. The primary and recommended first step for running a TFLite model is to utilize the YOLO("model.tflite") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your TFLite models in various other settings, take a look at the following resources: -- **[Android](https://www.tensorflow.org/lite/android/quickstart)**: A quick start guide for integrating TensorFlow Lite into Android applications, providing easy-to-follow steps for setting up and running machine learning models. +- **[Android](https://ai.google.dev/edge/litert/android)**: A quick start guide for integrating TensorFlow Lite into Android applications, providing easy-to-follow steps for setting up and running machine learning models. -- **[iOS](https://www.tensorflow.org/lite/guide/ios)**: Check out this detailed guide for developers on integrating and deploying TensorFlow Lite models in iOS applications, offering step-by-step instructions and resources. +- **[iOS](https://ai.google.dev/edge/litert/ios/quickstart)**: Check out this detailed guide for developers on integrating and deploying TensorFlow Lite models in iOS applications, offering step-by-step instructions and resources. -- **[End-To-End Examples](https://www.tensorflow.org/lite/examples)**: This page provides an overview of various TensorFlow Lite examples, showcasing practical applications and tutorials designed to help developers implement TensorFlow Lite in their machine learning projects on mobile and edge devices. +- **[End-To-End Examples](https://github.com/tensorflow/examples/tree/master/lite/examples)**: This page provides an overview of various TensorFlow Lite examples, showcasing practical applications and tutorials designed to help developers implement TensorFlow Lite in their machine learning projects on mobile and edge devices. ## Summary In this guide, we focused on how to export to TFLite format. By converting your Ultralytics YOLOv8 models to TFLite model format, you can improve the efficiency and speed of YOLOv8 models, making them more effective and suitable for edge computing environments. -For further details on usage, visit the [TFLite official documentation](https://www.tensorflow.org/lite/guide). +For further details on usage, visit the [TFLite official documentation](https://ai.google.dev/edge/litert). Also, if you're curious about other Ultralytics YOLOv8 integrations, make sure to check out our [integration guide page](../integrations/index.md). You'll find tons of helpful info and insights waiting for you there. @@ -159,7 +159,7 @@ TensorFlow Lite (TFLite) is an open-source deep learning framework designed for - **Platform compatibility**: Supports Android, iOS, embedded Linux, and MCU. - **Performance**: Utilizes hardware acceleration to optimize model speed and efficiency. -To learn more, check out the [TFLite guide](https://www.tensorflow.org/lite/guide). +To learn more, check out the [TFLite guide](https://ai.google.dev/edge/litert). ### Is it possible to run YOLOv8 TFLite models on Raspberry Pi? @@ -171,7 +171,7 @@ For further optimizations, you might consider using [Coral Edge TPU](https://cor Yes, TFLite supports deployment on microcontrollers with limited resources. TFLite's core runtime requires only 16 KB of memory on an Arm Cortex M3 and can run basic YOLOv8 models. This makes it suitable for deployment on devices with minimal computational power and memory. -To get started, visit the [TFLite Micro for Microcontrollers guide](https://www.tensorflow.org/lite/microcontrollers). +To get started, visit the [TFLite Micro for Microcontrollers guide](https://ai.google.dev/edge/litert/microcontrollers/overview). ### What platforms are compatible with TFLite exported YOLOv8 models? diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml index b323b4a39e..33ed82983b 100644 --- a/docs/mkdocs_github_authors.yaml +++ b/docs/mkdocs_github_authors.yaml @@ -91,6 +91,9 @@ jpedrofonseca_94@hotmail.com: k-2feng@hotmail.com: avatar: null username: null +lakshantha@ultralytics.com: + avatar: https://avatars.githubusercontent.com/u/20147381?v=4 + username: lakshanthad lakshanthad@yahoo.com: avatar: https://avatars.githubusercontent.com/u/20147381?v=4 username: lakshanthad From 95d54828bbd3fd3ba7df9ff7c075568f0b668506 Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Thu, 5 Sep 2024 17:34:40 +0500 Subject: [PATCH 13/17] Add https://youtu.be/w4yHORvDBw0 to docs (#16031) Co-authored-by: Glenn Jocher --- docs/en/guides/coral-edge-tpu-on-raspberry-pi.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md index 21b9589d5c..4e5a02d3f7 100644 --- a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md +++ b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md @@ -14,6 +14,17 @@ keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your system. It enables low-power, high-performance ML inference for TensorFlow Lite models. Read more at the [Coral Edge TPU home page](https://coral.ai/products/accelerator). +

+
+ +
+ Watch: How to Run Inference on Raspberry Pi using Google Coral Edge TPU +

+ ## Boost Raspberry Pi Model Performance with Coral Edge TPU Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [onnx](../integrations/onnx.md) or [openvino](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly. From ac2c2be8f34b0e75077866dfdf1a863af7140aad Mon Sep 17 00:00:00 2001 From: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Date: Fri, 6 Sep 2024 03:54:35 +0800 Subject: [PATCH 14/17] Ultralytics Code Refactor https://ultralytics.com/actions (#16047) Co-authored-by: Glenn Jocher --- ultralytics/data/converter.py | 11 +++---- ultralytics/hub/google/__init__.py | 4 +-- ultralytics/hub/session.py | 2 +- ultralytics/models/fastsam/predict.py | 2 +- ultralytics/models/sam/modules/blocks.py | 8 ++--- ultralytics/models/sam/modules/decoders.py | 9 +++--- ultralytics/models/sam/modules/encoders.py | 5 ++- ultralytics/models/sam/modules/sam.py | 36 ++++++++------------- ultralytics/models/yolo/classify/predict.py | 8 ++--- ultralytics/nn/modules/activation.py | 3 +- ultralytics/utils/__init__.py | 6 ++-- ultralytics/utils/checks.py | 13 ++++---- 12 files changed, 45 insertions(+), 62 deletions(-) diff --git a/ultralytics/data/converter.py b/ultralytics/data/converter.py index 400e928bc0..1ba09e0b90 100644 --- a/ultralytics/data/converter.py +++ b/ultralytics/data/converter.py @@ -370,13 +370,10 @@ def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes): ├─ mask_yolo_03.txt └─ mask_yolo_04.txt """ - import os - pixel_to_class_mapping = {i + 1: i for i in range(classes)} - for mask_filename in os.listdir(masks_dir): - if mask_filename.endswith(".png"): - mask_path = os.path.join(masks_dir, mask_filename) - mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) # Read the mask image in grayscale + for mask_path in Path(masks_dir).iterdir(): + if mask_path.suffix == ".png": + mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE) # Read the mask image in grayscale img_height, img_width = mask.shape # Get image dimensions LOGGER.info(f"Processing {mask_path} imgsz = {img_height} x {img_width}") @@ -406,7 +403,7 @@ def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes): yolo_format.append(round(point[1] / img_height, 6)) yolo_format_data.append(yolo_format) # Save Ultralytics YOLO format data to file - output_path = os.path.join(output_dir, os.path.splitext(mask_filename)[0] + ".txt") + output_path = Path(output_dir) / f"{Path(mask_filename).stem}.txt" with open(output_path, "w") as file: for item in yolo_format_data: line = " ".join(map(str, item)) diff --git a/ultralytics/hub/google/__init__.py b/ultralytics/hub/google/__init__.py index 7531b7b575..9090297a71 100644 --- a/ultralytics/hub/google/__init__.py +++ b/ultralytics/hub/google/__init__.py @@ -136,12 +136,12 @@ class GCPRegions: sorted_results = sorted(results, key=lambda x: x[1]) if verbose: - print(f"{'Region':<25} {'Location':<35} {'Tier':<5} {'Latency (ms)'}") + print(f"{'Region':<25} {'Location':<35} {'Tier':<5} Latency (ms)") for region, mean, std, min_, max_ in sorted_results: tier, city, country = self.regions[region] location = f"{city}, {country}" if mean == float("inf"): - print(f"{region:<25} {location:<35} {tier:<5} {'Timeout'}") + print(f"{region:<25} {location:<35} {tier:<5} Timeout") else: print(f"{region:<25} {location:<35} {tier:<5} {mean:.0f} ± {std:.0f} ({min_:.0f} - {max_:.0f})") print(f"\nLowest latency region{'s' if top > 1 else ''}:") diff --git a/ultralytics/hub/session.py b/ultralytics/hub/session.py index d93c962814..b112d29ff1 100644 --- a/ultralytics/hub/session.py +++ b/ultralytics/hub/session.py @@ -346,7 +346,7 @@ class HUBTrainingSession: """ weights = Path(weights) if not weights.is_file(): - last = weights.with_name("last" + weights.suffix) + last = weights.with_name(f"last{weights.suffix}") if final and last.is_file(): LOGGER.warning( f"{PREFIX} WARNING ⚠️ Model 'best.pt' not found, copying 'last.pt' to 'best.pt' and uploading. " diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py index 0dce968a9b..9910237b0f 100644 --- a/ultralytics/models/fastsam/predict.py +++ b/ultralytics/models/fastsam/predict.py @@ -93,7 +93,7 @@ class FastSAMPredictor(SegmentationPredictor): else torch.zeros(len(result), dtype=torch.bool, device=self.device) ) for point, label in zip(points, labels): - point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = True if label else False + point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label) idx |= point_idx if texts is not None: if isinstance(texts, str): diff --git a/ultralytics/models/sam/modules/blocks.py b/ultralytics/models/sam/modules/blocks.py index 0615037467..026443c69f 100644 --- a/ultralytics/models/sam/modules/blocks.py +++ b/ultralytics/models/sam/modules/blocks.py @@ -736,7 +736,7 @@ class PositionEmbeddingSine(nn.Module): self.num_pos_feats = num_pos_feats // 2 self.temperature = temperature self.normalize = normalize - if scale is not None and normalize is False: + if scale is not None and not normalize: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi @@ -763,8 +763,7 @@ class PositionEmbeddingSine(nn.Module): def encode_boxes(self, x, y, w, h): """Encodes box coordinates and dimensions into positional embeddings for detection.""" pos_x, pos_y = self._encode_xy(x, y) - pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) - return pos + return torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) encode = encode_boxes # Backwards compatibility @@ -775,8 +774,7 @@ class PositionEmbeddingSine(nn.Module): assert bx == by and nx == ny and bx == bl and nx == nl pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten()) pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1) - pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) - return pos + return torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) @torch.no_grad() def forward(self, x: torch.Tensor): diff --git a/ultralytics/models/sam/modules/decoders.py b/ultralytics/models/sam/modules/decoders.py index cd4cc0c18c..7c27ca176b 100644 --- a/ultralytics/models/sam/modules/decoders.py +++ b/ultralytics/models/sam/modules/decoders.py @@ -435,9 +435,9 @@ class SAM2MaskDecoder(nn.Module): upscaled_embedding = act1(ln1(dc1(src) + feat_s1)) upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0) - hyper_in_list: List[torch.Tensor] = [] - for i in range(self.num_mask_tokens): - hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])) + hyper_in_list: List[torch.Tensor] = [ + self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens) + ] hyper_in = torch.stack(hyper_in_list, dim=1) b, c, h, w = upscaled_embedding.shape masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) @@ -459,8 +459,7 @@ class SAM2MaskDecoder(nn.Module): stability_delta = self.dynamic_multimask_stability_delta area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() - stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) - return stability_scores + return torch.where(area_u > 0, area_i / area_u, 1.0) def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): """ diff --git a/ultralytics/models/sam/modules/encoders.py b/ultralytics/models/sam/modules/encoders.py index 22934222a1..7fa7b405d3 100644 --- a/ultralytics/models/sam/modules/encoders.py +++ b/ultralytics/models/sam/modules/encoders.py @@ -491,12 +491,11 @@ class ImageEncoder(nn.Module): features, pos = features[: -self.scalp], pos[: -self.scalp] src = features[-1] - output = { + return { "vision_features": src, "vision_pos_enc": pos, "backbone_fpn": features, } - return output class FpnNeck(nn.Module): @@ -577,7 +576,7 @@ class FpnNeck(nn.Module): self.convs.append(current) self.fpn_interp_model = fpn_interp_model - assert fuse_type in ["sum", "avg"] + assert fuse_type in {"sum", "avg"} self.fuse_type = fuse_type # levels to have top-down features in its outputs diff --git a/ultralytics/models/sam/modules/sam.py b/ultralytics/models/sam/modules/sam.py index b638ddc534..c902153f17 100644 --- a/ultralytics/models/sam/modules/sam.py +++ b/ultralytics/models/sam/modules/sam.py @@ -671,26 +671,19 @@ class SAM2Model(torch.nn.Module): t_rel = self.num_maskmem - t_pos # how many frames before current frame if t_rel == 1: # for t_rel == 1, we take the last frame (regardless of r) - if not track_in_reverse: - # the frame immediately before this frame (i.e. frame_idx - 1) - prev_frame_idx = frame_idx - t_rel - else: - # the frame immediately after this frame (i.e. frame_idx + 1) - prev_frame_idx = frame_idx + t_rel + prev_frame_idx = frame_idx + t_rel if track_in_reverse else frame_idx - t_rel + elif not track_in_reverse: + # first find the nearest frame among every r-th frames before this frame + # for r=1, this would be (frame_idx - 2) + prev_frame_idx = ((frame_idx - 2) // r) * r + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx - (t_rel - 2) * r else: - # for t_rel >= 2, we take the memory frame from every r-th frames - if not track_in_reverse: - # first find the nearest frame among every r-th frames before this frame - # for r=1, this would be (frame_idx - 2) - prev_frame_idx = ((frame_idx - 2) // r) * r - # then seek further among every r-th frames - prev_frame_idx = prev_frame_idx - (t_rel - 2) * r - else: - # first find the nearest frame among every r-th frames after this frame - # for r=1, this would be (frame_idx + 2) - prev_frame_idx = -(-(frame_idx + 2) // r) * r - # then seek further among every r-th frames - prev_frame_idx = prev_frame_idx + (t_rel - 2) * r + # first find the nearest frame among every r-th frames after this frame + # for r=1, this would be (frame_idx + 2) + prev_frame_idx = -(-(frame_idx + 2) // r) * r + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx + (t_rel - 2) * r out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None) if out is None: # If an unselected conditioning frame is among the last (self.num_maskmem - 1) @@ -739,7 +732,7 @@ class SAM2Model(torch.nn.Module): if out is not None: pos_and_ptrs.append((t_diff, out["obj_ptr"])) # If we have at least one object pointer, add them to the across attention - if len(pos_and_ptrs) > 0: + if pos_and_ptrs: pos_list, ptrs_list = zip(*pos_and_ptrs) # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape obj_ptrs = torch.stack(ptrs_list, dim=0) @@ -930,12 +923,11 @@ class SAM2Model(torch.nn.Module): def _use_multimask(self, is_init_cond_frame, point_inputs): """Determines whether to use multiple mask outputs in the SAM head based on configuration and inputs.""" num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) - multimask_output = ( + return ( self.multimask_output_in_sam and (is_init_cond_frame or self.multimask_output_for_tracking) and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num) ) - return multimask_output def _apply_non_overlapping_constraints(self, pred_masks): """Applies non-overlapping constraints to masks, keeping highest scoring object per location.""" diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py index 266075c6c1..596931a176 100644 --- a/ultralytics/models/yolo/classify/predict.py +++ b/ultralytics/models/yolo/classify/predict.py @@ -53,7 +53,7 @@ class ClassificationPredictor(BasePredictor): if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) - results = [] - for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]): - results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred)) - return results + return [ + Results(orig_img, path=img_path, names=self.model.names, probs=pred) + for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]) + ] diff --git a/ultralytics/nn/modules/activation.py b/ultralytics/nn/modules/activation.py index 25cca2a508..aaf636e762 100644 --- a/ultralytics/nn/modules/activation.py +++ b/ultralytics/nn/modules/activation.py @@ -18,5 +18,4 @@ class AGLU(nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: """Compute the forward pass of the Unified activation function.""" lam = torch.clamp(self.lambd, min=0.0001) - y = torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam))) - return y # for AGLU simply return y * input + return torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam))) diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py index 616639abbb..7328a5fdb2 100644 --- a/ultralytics/utils/__init__.py +++ b/ultralytics/utils/__init__.py @@ -1160,9 +1160,9 @@ def vscode_msg(ext="ultralytics.ultralytics-snippets") -> str: obs_file = path / ".obsolete" # file tracks uninstalled extensions, while source directory remains installed = any(path.glob(f"{ext}*")) and ext not in (obs_file.read_text("utf-8") if obs_file.exists() else "") return ( - f"{colorstr('VS Code:')} view Ultralytics VS Code Extension ⚡ at https://docs.ultralytics.com/integrations/vscode" - if not installed - else "" + "" + if installed + else f"{colorstr('VS Code:')} view Ultralytics VS Code Extension ⚡ at https://docs.ultralytics.com/integrations/vscode" ) diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py index 80c13ad787..6b308bc146 100644 --- a/ultralytics/utils/checks.py +++ b/ultralytics/utils/checks.py @@ -226,13 +226,12 @@ def check_version( if not required: # if required is '' or None return True - if "sys_platform" in required: # i.e. required='<2.4.0,>=1.8.0; sys_platform == "win32"' - if ( - (WINDOWS and "win32" not in required) - or (LINUX and "linux" not in required) - or (MACOS and "macos" not in required and "darwin" not in required) - ): - return True + if "sys_platform" in required and ( # i.e. required='<2.4.0,>=1.8.0; sys_platform == "win32"' + (WINDOWS and "win32" not in required) + or (LINUX and "linux" not in required) + or (MACOS and "macos" not in required and "darwin" not in required) + ): + return True op = "" version = "" From 2a73bf7046d1c8eb37a51c52b1db2aaee2a09120 Mon Sep 17 00:00:00 2001 From: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Date: Fri, 6 Sep 2024 04:47:15 +0800 Subject: [PATCH 15/17] Update URLs to redirects (#16048) --- CONTRIBUTING.md | 4 +-- README.md | 24 ++++++++-------- README.zh-CN.md | 24 ++++++++-------- docs/README.md | 8 +++--- docs/coming_soon_template.md | 8 +++--- docs/en/datasets/classify/imagenet10.md | 2 +- docs/en/datasets/classify/index.md | 2 +- docs/en/datasets/detect/coco8.md | 6 ++-- docs/en/datasets/detect/roboflow-100.md | 4 +-- docs/en/datasets/obb/dota8.md | 4 +-- docs/en/datasets/pose/coco8-pose.md | 4 +-- docs/en/datasets/pose/index.md | 4 +-- docs/en/datasets/pose/tiger-pose.md | 8 +++--- docs/en/datasets/segment/carparts-seg.md | 8 +++--- docs/en/datasets/segment/coco8-seg.md | 6 ++-- docs/en/datasets/segment/crack-seg.md | 6 ++-- docs/en/datasets/segment/package-seg.md | 6 ++-- .../guides/data-collection-and-annotation.md | 2 +- docs/en/guides/defining-project-goals.md | 2 +- docs/en/guides/docker-quickstart.md | 4 +-- docs/en/guides/hyperparameter-tuning.md | 2 +- docs/en/guides/model-deployment-options.md | 2 +- docs/en/guides/model-deployment-practices.md | 2 +- docs/en/guides/model-evaluation-insights.md | 2 +- .../model-monitoring-and-maintenance.md | 2 +- docs/en/guides/model-testing.md | 2 +- docs/en/guides/model-training-tips.md | 2 +- docs/en/guides/nvidia-jetson.md | 2 +- ...ng-openvino-latency-vs-throughput-modes.md | 2 +- .../en/guides/preprocessing_annotated_data.md | 2 +- docs/en/guides/raspberry-pi.md | 2 +- docs/en/guides/steps-of-a-cv-project.md | 2 +- docs/en/guides/streamlit-live-inference.md | 2 +- docs/en/guides/triton-inference-server.md | 10 +++---- docs/en/guides/yolo-common-issues.md | 6 ++-- docs/en/guides/yolo-performance-metrics.md | 2 +- docs/en/help/CI.md | 6 ++-- docs/en/help/FAQ.md | 4 +-- docs/en/help/code_of_conduct.md | 4 +-- docs/en/help/contributing.md | 4 +-- docs/en/help/minimum_reproducible_example.md | 2 +- docs/en/help/privacy.md | 6 ++-- docs/en/help/security.md | 10 +++---- docs/en/hub/api/index.md | 6 ++-- docs/en/hub/app/android.md | 16 +++++------ docs/en/hub/cloud-training.md | 4 +-- docs/en/hub/datasets.md | 14 +++++----- docs/en/hub/index.md | 10 +++---- docs/en/hub/inference-api.md | 14 +++++----- docs/en/hub/integrations.md | 24 ++++++++-------- docs/en/hub/models.md | 24 ++++++++-------- docs/en/hub/pro.md | 2 +- docs/en/hub/projects.md | 6 ++-- docs/en/hub/quickstart.md | 6 ++-- docs/en/hub/teams.md | 2 +- docs/en/index.md | 10 +++---- docs/en/integrations/clearml.md | 2 +- docs/en/integrations/comet.md | 4 +-- docs/en/integrations/coreml.md | 6 ++-- docs/en/integrations/dvc.md | 2 +- docs/en/integrations/edge-tpu.md | 2 +- docs/en/integrations/index.md | 16 +++++------ docs/en/integrations/mlflow.md | 2 +- docs/en/integrations/neural-magic.md | 2 +- docs/en/integrations/ray-tune.md | 2 +- docs/en/integrations/roboflow.md | 28 +++++++++---------- docs/en/integrations/tensorboard.md | 2 +- docs/en/models/sam.md | 2 +- docs/en/models/yolo-world.md | 4 +-- docs/en/models/yolov10.md | 4 +-- docs/en/models/yolov5.md | 2 +- docs/en/models/yolov8.md | 2 +- docs/en/models/yolov9.md | 2 +- docs/en/modes/train.md | 2 +- docs/en/quickstart.md | 2 +- docs/en/tasks/detect.md | 2 +- docs/en/tasks/pose.md | 2 +- docs/en/tasks/segment.md | 2 +- .../docker_image_quickstart_tutorial.md | 2 +- .../google_cloud_quickstart_tutorial.md | 2 +- docs/en/yolov5/index.md | 4 +-- .../tutorials/hyperparameter_evolution.md | 2 +- docs/en/yolov5/tutorials/model_ensembling.md | 2 +- docs/en/yolov5/tutorials/model_export.md | 6 ++-- .../tutorials/model_pruning_and_sparsity.md | 2 +- .../en/yolov5/tutorials/multi_gpu_training.md | 2 +- .../tutorials/pytorch_hub_model_loading.md | 4 +-- .../roboflow_datasets_integration.md | 12 ++++---- .../tutorials/test_time_augmentation.md | 4 +-- docs/en/yolov5/tutorials/train_custom_data.md | 12 ++++---- .../transfer_learning_with_frozen_layers.md | 2 +- ultralytics/cfg/models/README.md | 2 +- 92 files changed, 253 insertions(+), 253 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 0c564dadef..d884e43b4a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, open-source, contribution, pull request, code of co # Contributing to Ultralytics Open-Source Projects -Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://ultralytics.com) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire community. This guide provides clear guidelines and best practices to help you get started. +Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire community. This guide provides clear guidelines and best practices to help you get started. Ultralytics open-source contributors @@ -131,7 +131,7 @@ We encourage all contributors to familiarize themselves with the terms of the AG ## Conclusion -Thank you for your interest in contributing to [Ultralytics](https://ultralytics.com) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable. +Thank you for your interest in contributing to [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable. We're excited to see your ideas come to life and appreciate your commitment to advancing object detection technology. Together, let's continue to grow and innovate in this exciting open-source journey. Happy coding! 🚀🌟 diff --git a/README.md b/README.md index 0505701dd8..1cc1686e67 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ YOLO Vision banner

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+[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
Ultralytics CI @@ -20,11 +20,11 @@

-[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. +[Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, questions, or discussions, become a member of the Ultralytics Discord, Reddit and Forums! -To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license). +To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license). YOLOv8 performance plots @@ -103,7 +103,7 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa ### Notebooks -Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics?sub_confirmation=1) tutorial, making it easy to learn and implement advanced YOLOv8 features. +Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://www.youtube.com/ultralytics?sub_confirmation=1) tutorial, making it easy to learn and implement advanced YOLOv8 features. | Docs | Notebook | YouTube | | ---------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | @@ -134,7 +134,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | -- **mAPval** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset.
Reproduce by `yolo val detect data=coco.yaml device=0` +- **mAPval** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val detect data=coco.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu` @@ -168,7 +168,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | -- **mAPval** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset.
Reproduce by `yolo val segment data=coco-seg.yaml device=0` +- **mAPval** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val segment data=coco-seg.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` @@ -186,7 +186,7 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 | | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 | -- **mAPval** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org) dataset.
Reproduce by `yolo val pose data=coco-pose.yaml device=0` +- **mAPval** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val pose data=coco-pose.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` @@ -255,14 +255,14 @@ Our key integrations with leading AI platforms extend the functionality of Ultra ##
Ultralytics HUB
-Experience seamless AI with [Ultralytics HUB](https://ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now! +Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now! Ultralytics HUB preview image ##
Contribute
-We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors! +We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors! @@ -273,12 +273,12 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou Ultralytics offers two licensing options to accommodate diverse use cases: -- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details. -- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license). +- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details. +- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license). ##
Contact
-For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). Become a member of the Ultralytics [Discord](https://ultralytics.com/discord), [Reddit](https://reddit.com/r/ultralytics), or [Forums](https://community.ultralytics.com) for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics! +For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). Become a member of the Ultralytics [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), or [Forums](https://community.ultralytics.com/) for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics!
diff --git a/README.zh-CN.md b/README.zh-CN.md index 5673a627fd..79de5de2bc 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -4,7 +4,7 @@ YOLO Vision banner

-[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)
+[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
Ultralytics CI @@ -20,11 +20,11 @@

-[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。 +[Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。 我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 的文档了解详情，如需支持、提问或讨论，请在 GitHub 上提出问题，成为 Ultralytics Discord、Reddit 和 论坛 的一员！ -如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 +如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格 YOLOv8 performance plots @@ -105,7 +105,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 ### 笔记本 -Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://youtube.com/ultralytics?sub_confirmation=1) 教程，使学习和实现高级 YOLOv8 功能变得简单。 +Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://www.youtube.com/ultralytics?sub_confirmation=1) 教程，使学习和实现高级 YOLOv8 功能变得简单。 | 文档 | 笔记本 | YouTube | | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | @@ -136,7 +136,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟 | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | -- **mAPval** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。
通过 `yolo val detect data=coco.yaml device=0` 复现 +- **mAPval** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上的结果。
通过 `yolo val detect data=coco.yaml device=0` 复现 - **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现 @@ -170,7 +170,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟 | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | -- **mAPval** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。
通过 `yolo val segment data=coco-seg.yaml device=0` 复现 +- **mAPval** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上的结果。
通过 `yolo val segment data=coco-seg.yaml device=0` 复现 - **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现 @@ -188,7 +188,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟 | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 | | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 | -- **mAPval** 值是基于单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org) 数据集上的结果。
通过 `yolo val pose data=coco-pose.yaml device=0` 复现 +- **mAPval** 值是基于单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org/) 数据集上的结果。
通过 `yolo val pose data=coco-pose.yaml device=0` 复现 - **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现 @@ -257,14 +257,14 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟 ##
Ultralytics HUB
-体验 [Ultralytics HUB](https://ultralytics.com/hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！ +体验 [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://www.ultralytics.com/app-install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！ Ultralytics HUB preview image ##
贡献
-我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏 +我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing)以开始使用，并填写我们的[调查问卷](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏 @@ -275,12 +275,12 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟 Ultralytics 提供两种许可证选项以适应各种使用场景： -- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。 -- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。 +- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。 +- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。 ##
联系方式
-有关 Ultralytics 错误报告和功能请求，请访问 [GitHub 问题](https://github.com/ultralytics/ultralytics/issues)。成为 Ultralytics [Discord](https://ultralytics.com/discord)、[Reddit](https://reddit.com/r/ultralytics) 或 [论坛](https://community.ultralytics.com) 的成员 用于提出问题、共享项目、学习讨论或寻求有关 Ultralytics 的所有帮助！ +有关 Ultralytics 错误报告和功能请求，请访问 [GitHub 问题](https://github.com/ultralytics/ultralytics/issues)。成为 Ultralytics [Discord](https://discord.com/invite/ultralytics)、[Reddit](https://www.reddit.com/r/ultralytics/) 或 [论坛](https://community.ultralytics.com/) 的成员 用于提出问题、共享项目、学习讨论或寻求有关 Ultralytics 的所有帮助！
diff --git a/docs/README.md b/docs/README.md index b3766abe9e..296b5d5a07 100644 --- a/docs/README.md +++ b/docs/README.md @@ -3,7 +3,7 @@ # 📚 Ultralytics Docs -[Ultralytics](https://ultralytics.com) Docs are the gateway to understanding and utilizing our cutting-edge machine learning tools. These documents are deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com) for your convenience. +[Ultralytics](https://www.ultralytics.com/) Docs are the gateway to understanding and utilizing our cutting-edge machine learning tools. These documents are deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com) for your convenience. [![pages-build-deployment](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment) [![Check Broken links](https://github.com/ultralytics/docs/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/links.yml) @@ -113,7 +113,7 @@ Choose a hosting provider and deployment method for your MkDocs documentation: ## 💡 Contribute -We cherish the community's input as it drives Ultralytics open-source initiatives. Dive into the [Contributing Guide](https://docs.ultralytics.com/help/contributing) and share your thoughts via our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to each contributor! +We cherish the community's input as it drives Ultralytics open-source initiatives. Dive into the [Contributing Guide](https://docs.ultralytics.com/help/contributing) and share your thoughts via our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to each contributor! ![Ultralytics open-source contributors](https://github.com/ultralytics/docs/releases/download/0/ultralytics-open-source-contributors.avif) @@ -122,11 +122,11 @@ We cherish the community's input as it drives Ultralytics open-source initiative Ultralytics Docs presents two licensing options: - **AGPL-3.0 License**: Perfect for academia and open collaboration. Details are in the [LICENSE](https://github.com/ultralytics/docs/blob/main/LICENSE) file. -- **Enterprise License**: Tailored for commercial usage, offering a seamless blend of Ultralytics technology in your products. Learn more at [Ultralytics Licensing](https://ultralytics.com/license). +- **Enterprise License**: Tailored for commercial usage, offering a seamless blend of Ultralytics technology in your products. Learn more at [Ultralytics Licensing](https://www.ultralytics.com/license). ## ✉️ Contact -For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). Become a member of the Ultralytics [Discord](https://ultralytics.com/discord), [Reddit](https://reddit.com/r/ultralytics), or [Forums](https://community.ultralytics.com) for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics! +For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). Become a member of the Ultralytics [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), or [Forums](https://community.ultralytics.com/) for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics!
diff --git a/docs/coming_soon_template.md b/docs/coming_soon_template.md index 1b610d4d5c..3f0840d453 100644 --- a/docs/coming_soon_template.md +++ b/docs/coming_soon_template.md @@ -5,7 +5,7 @@ keywords: Ultralytics, coming soon, under construction, new features, AI updates # Under Construction 🏗️🌟 -Welcome to the [Ultralytics](https://ultralytics.com) "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you! +Welcome to the [Ultralytics](https://www.ultralytics.com/) "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you! ## Exciting New Features on the Way 🎉 @@ -17,13 +17,13 @@ Welcome to the [Ultralytics](https://ultralytics.com) "Under Construction" page! This placeholder page is your first stop for upcoming developments. Keep an eye out for: -- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news. +- **Newsletter:** Subscribe [here](https://www.ultralytics.com/#newsletter) for the latest news. - **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers. -- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights. +- **Blog:** Visit our [blog](https://www.ultralytics.com/blog) for detailed insights. ## We Value Your Input 🗣️ -Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/survey). +Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://www.ultralytics.com/survey). ## Thank You, Community! 🌍 diff --git a/docs/en/datasets/classify/imagenet10.md b/docs/en/datasets/classify/imagenet10.md index cc9c9ec7e6..38764c89ec 100644 --- a/docs/en/datasets/classify/imagenet10.md +++ b/docs/en/datasets/classify/imagenet10.md @@ -6,7 +6,7 @@ keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training p # ImageNet10 Dataset -The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://ultralytics.com) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset. +The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://www.ultralytics.com/) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset. ## Key Features diff --git a/docs/en/datasets/classify/index.md b/docs/en/datasets/classify/index.md index 357138759f..58aaaecd0c 100644 --- a/docs/en/datasets/classify/index.md +++ b/docs/en/datasets/classify/index.md @@ -8,7 +8,7 @@ keywords: YOLO, image classification, dataset structure, CIFAR-10, Ultralytics, ### Dataset Structure for YOLO Classification Tasks -For [Ultralytics](https://ultralytics.com) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`). +For [Ultralytics](https://www.ultralytics.com/) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`). Each of these directories should contain one subdirectory for each class in the dataset. The subdirectories are named after the corresponding class and contain all the images for that class. Ensure that each image file is named uniquely and stored in a common format such as JPEG or PNG. diff --git a/docs/en/datasets/detect/coco8.md b/docs/en/datasets/detect/coco8.md index cae9e673d6..c1df16ee22 100644 --- a/docs/en/datasets/detect/coco8.md +++ b/docs/en/datasets/detect/coco8.md @@ -8,7 +8,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid ## Introduction -[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. +[Ultralytics](https://www.ultralytics.com/) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.


@@ -21,7 +21,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid Watch: Ultralytics COCO Dataset Overview

-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics). ## Dataset YAML @@ -124,7 +124,7 @@ For a comprehensive list of available arguments, refer to the model [Training](. ### Why should I use Ultralytics HUB for managing my COCO8 training? -Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com) and its benefits. +Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits. ### What are the benefits of using mosaic augmentation in training with the COCO8 dataset? diff --git a/docs/en/datasets/detect/roboflow-100.md b/docs/en/datasets/detect/roboflow-100.md index f27591232d..253b640f4b 100644 --- a/docs/en/datasets/detect/roboflow-100.md +++ b/docs/en/datasets/detect/roboflow-100.md @@ -95,7 +95,7 @@ For more ideas and inspiration on real-world applications, be sure to check out ## Usage -The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100). +The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100?ref=ultralytics). You can access it directly from the Roboflow 100 GitHub repository. In addition, on Roboflow Universe, you have the flexibility to download individual datasets by simply clicking the export button within each dataset. @@ -197,7 +197,7 @@ This setup allows for extensive and varied testing of models across different re ### How do I access and download the Roboflow 100 dataset? -The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100). You can download the entire dataset from GitHub or select individual datasets on Roboflow Universe using the export button. +The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100?ref=ultralytics). You can download the entire dataset from GitHub or select individual datasets on Roboflow Universe using the export button. ### What should I include when citing the Roboflow 100 dataset in my research? diff --git a/docs/en/datasets/obb/dota8.md b/docs/en/datasets/obb/dota8.md index 2271978e11..0bfa723ae8 100644 --- a/docs/en/datasets/obb/dota8.md +++ b/docs/en/datasets/obb/dota8.md @@ -8,9 +8,9 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train ## Introduction -[Ultralytics](https://ultralytics.com) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. +[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. -This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics). ## Dataset YAML diff --git a/docs/en/datasets/pose/coco8-pose.md b/docs/en/datasets/pose/coco8-pose.md index e5b2eb8657..dfa8e30123 100644 --- a/docs/en/datasets/pose/coco8-pose.md +++ b/docs/en/datasets/pose/coco8-pose.md @@ -8,9 +8,9 @@ keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOL ## Introduction -[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. +[Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. -This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics). ## Dataset YAML diff --git a/docs/en/datasets/pose/index.md b/docs/en/datasets/pose/index.md index 57c20dcb7c..a2e7623420 100644 --- a/docs/en/datasets/pose/index.md +++ b/docs/en/datasets/pose/index.md @@ -101,7 +101,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for ### COCO8-Pose -- **Description**: [Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. +- **Description**: [Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. - **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses. - **Number of Classes**: 1 (Human). - **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles. @@ -111,7 +111,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for ### Tiger-Pose -- **Description**: [Ultralytics](https://ultralytics.com) This animal pose dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. +- **Description**: [Ultralytics](https://www.ultralytics.com/) This animal pose dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. - **Label Format**: Same as Ultralytics YOLO format as described above, with 12 keypoints for animal pose and no visible dimension. - **Number of Classes**: 1 (Tiger). - **Keypoints**: 12 keypoints. diff --git a/docs/en/datasets/pose/tiger-pose.md b/docs/en/datasets/pose/tiger-pose.md index 457e8fefe7..3e8b55665a 100644 --- a/docs/en/datasets/pose/tiger-pose.md +++ b/docs/en/datasets/pose/tiger-pose.md @@ -8,11 +8,11 @@ keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training da ## Introduction -[Ultralytics](https://ultralytics.com) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm. +[Ultralytics](https://www.ultralytics.com/) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm. Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation. -This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).


@@ -101,7 +101,7 @@ The dataset has been released available under the [AGPL-3.0 License](https://git ### What is the Ultralytics Tiger-Pose dataset used for? -The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics). ### How do I train a YOLOv8 model on the Tiger-Pose dataset? @@ -161,4 +161,4 @@ To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you ### What are the benefits of using the Tiger-Pose dataset for pose estimation? -The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy. +The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy. diff --git a/docs/en/datasets/segment/carparts-seg.md b/docs/en/datasets/segment/carparts-seg.md index d5799954be..f0d020ff46 100644 --- a/docs/en/datasets/segment/carparts-seg.md +++ b/docs/en/datasets/segment/carparts-seg.md @@ -6,7 +6,7 @@ keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive A # Roboflow Universe Carparts Segmentation Dataset -The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm) is a curated collection of images and videos designed for computer vision applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models. +The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos designed for computer vision applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models. Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects. @@ -100,13 +100,13 @@ If you integrate the Carparts Segmentation dataset into your research or develop } ``` -We extend our thanks to the Roboflow team for their dedication in developing and managing the Carparts Segmentation dataset, a valuable resource for vehicle maintenance and research projects. For additional details about the Carparts Segmentation dataset and its creators, please visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm). +We extend our thanks to the Roboflow team for their dedication in developing and managing the Carparts Segmentation dataset, a valuable resource for vehicle maintenance and research projects. For additional details about the Carparts Segmentation dataset and its creators, please visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics). ## FAQ ### What is the Roboflow Carparts Segmentation Dataset? -The [Roboflow Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm) is a curated collection of images and videos specifically designed for car part segmentation tasks in computer vision. This dataset includes a diverse range of visuals captured from multiple perspectives, making it an invaluable resource for training and testing segmentation models for automotive applications. +The [Roboflow Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos specifically designed for car part segmentation tasks in computer vision. This dataset includes a diverse range of visuals captured from multiple perspectives, making it an invaluable resource for training and testing segmentation models for automotive applications. ### How can I use the Carparts Segmentation Dataset with Ultralytics YOLOv8? @@ -157,4 +157,4 @@ The dataset configuration file for the Carparts Segmentation dataset, `carparts- The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-accuracy segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance. -For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm). +For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics). diff --git a/docs/en/datasets/segment/coco8-seg.md b/docs/en/datasets/segment/coco8-seg.md index f22d6a68a3..e4aa6bef84 100644 --- a/docs/en/datasets/segment/coco8-seg.md +++ b/docs/en/datasets/segment/coco8-seg.md @@ -8,9 +8,9 @@ keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model ## Introduction -[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. +[Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets. -This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics). ## Dataset YAML @@ -82,7 +82,7 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th ### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLOv8? -The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page. +The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page. ### How can I train a YOLOv8n-seg model using the COCO8-Seg dataset? diff --git a/docs/en/datasets/segment/crack-seg.md b/docs/en/datasets/segment/crack-seg.md index 5fa99dfbbf..32113dfc6d 100644 --- a/docs/en/datasets/segment/crack-seg.md +++ b/docs/en/datasets/segment/crack-seg.md @@ -6,7 +6,7 @@ keywords: Roboflow, Crack Segmentation Dataset, Ultralytics, transportation safe # Roboflow Universe Crack Segmentation Dataset -The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes. +The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes. Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the accuracy of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors. @@ -90,13 +90,13 @@ If you incorporate the crack segmentation dataset into your research or developm } ``` -We would like to acknowledge the Roboflow team for creating and maintaining the Crack Segmentation dataset as a valuable resource for the road safety and research projects. For more information about the Crack segmentation dataset and its creators, visit the [Crack Segmentation Dataset Page](https://universe.roboflow.com/university-bswxt/crack-bphdr). +We would like to acknowledge the Roboflow team for creating and maintaining the Crack Segmentation dataset as a valuable resource for the road safety and research projects. For more information about the Crack segmentation dataset and its creators, visit the [Crack Segmentation Dataset Page](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics). ## FAQ ### What is the Roboflow Crack Segmentation Dataset? -The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation. +The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation. ### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLOv8? diff --git a/docs/en/datasets/segment/package-seg.md b/docs/en/datasets/segment/package-seg.md index bf88410fb6..2aec99a21f 100644 --- a/docs/en/datasets/segment/package-seg.md +++ b/docs/en/datasets/segment/package-seg.md @@ -6,7 +6,7 @@ keywords: Roboflow, Package Segmentation Dataset, computer vision, package ident # Roboflow Universe Package Segmentation Dataset -The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling. +The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling. Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms. @@ -89,13 +89,13 @@ If you integrate the crack segmentation dataset into your research or developmen } ``` -We express our gratitude to the Roboflow team for their efforts in creating and maintaining the Package Segmentation dataset, a valuable asset for logistics and research projects. For additional details about the Package Segmentation dataset and its creators, please visit the [Package Segmentation Dataset Page](https://universe.roboflow.com/factorypackage/factory_package). +We express our gratitude to the Roboflow team for their efforts in creating and maintaining the Package Segmentation dataset, a valuable asset for logistics and research projects. For additional details about the Package Segmentation dataset and its creators, please visit the [Package Segmentation Dataset Page](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics). ## FAQ ### What is the Roboflow Package Segmentation Dataset and how can it help in computer vision projects? -The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package) is a curated collection of images tailored for tasks involving package segmentation. It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis. It helps optimize logistics and enhance vision models for accurate package identification and sorting. +The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images tailored for tasks involving package segmentation. It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis. It helps optimize logistics and enhance vision models for accurate package identification and sorting. ### How do I train an Ultralytics YOLOv8 model on the Package Segmentation Dataset? diff --git a/docs/en/guides/data-collection-and-annotation.md b/docs/en/guides/data-collection-and-annotation.md index 2a7cb149f8..7939d12a42 100644 --- a/docs/en/guides/data-collection-and-annotation.md +++ b/docs/en/guides/data-collection-and-annotation.md @@ -137,7 +137,7 @@ Bouncing your ideas and queries off other computer vision enthusiasts can help a ### Where to Find Help and Support - **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. ### Official Documentation diff --git a/docs/en/guides/defining-project-goals.md b/docs/en/guides/defining-project-goals.md index 3282cfe2d5..fcd32f12f2 100644 --- a/docs/en/guides/defining-project-goals.md +++ b/docs/en/guides/defining-project-goals.md @@ -115,7 +115,7 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for ### Community Support Channels - **GitHub Issues:** Head over to the YOLOv8 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter. -- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://ultralytics.com/discord/). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas. +- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://discord.com/invite/ultralytics). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas. ### Comprehensive Guides and Documentation diff --git a/docs/en/guides/docker-quickstart.md b/docs/en/guides/docker-quickstart.md index 90b86ed6d4..6d08fac0b5 100644 --- a/docs/en/guides/docker-quickstart.md +++ b/docs/en/guides/docker-quickstart.md @@ -10,7 +10,7 @@ keywords: Ultralytics, Docker, Quickstart Guide, CPU support, GPU support, NVIDI Ultralytics Docker Package Visual

-This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics). +This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://www.docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics). [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics)](https://hub.docker.com/r/ultralytics/ultralytics) @@ -27,7 +27,7 @@ This guide serves as a comprehensive introduction to setting up a Docker environ ## Prerequisites -- Make sure Docker is installed on your system. If not, you can download and install it from [Docker's website](https://www.docker.com/products/docker-desktop). +- Make sure Docker is installed on your system. If not, you can download and install it from [Docker's website](https://www.docker.com/products/docker-desktop/). - Ensure that your system has an NVIDIA GPU and NVIDIA drivers are installed. --- diff --git a/docs/en/guides/hyperparameter-tuning.md b/docs/en/guides/hyperparameter-tuning.md index 0915956572..ec95d2b8e1 100644 --- a/docs/en/guides/hyperparameter-tuning.md +++ b/docs/en/guides/hyperparameter-tuning.md @@ -204,7 +204,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful 2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md) 3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md) -For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord). +For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://discord.com/invite/ultralytics). ## FAQ diff --git a/docs/en/guides/model-deployment-options.md b/docs/en/guides/model-deployment-options.md index 713a3dabf8..353dcabd68 100644 --- a/docs/en/guides/model-deployment-options.md +++ b/docs/en/guides/model-deployment-options.md @@ -288,7 +288,7 @@ When you're getting started with YOLOv8, having a helpful community and support - **GitHub Discussions:** The YOLOv8 repository on GitHub has a "Discussions" section where you can ask questions, report issues, and suggest improvements. -- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and developers. +- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and developers. ### Official Documentation and Resources diff --git a/docs/en/guides/model-deployment-practices.md b/docs/en/guides/model-deployment-practices.md index fae69cbd44..60d95963be 100644 --- a/docs/en/guides/model-deployment-practices.md +++ b/docs/en/guides/model-deployment-practices.md @@ -122,7 +122,7 @@ Being part of a community of computer vision enthusiasts can help you solve prob ### Community Resources - **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences. ### Official Documentation diff --git a/docs/en/guides/model-evaluation-insights.md b/docs/en/guides/model-evaluation-insights.md index feb31ad353..08c3da64e1 100644 --- a/docs/en/guides/model-evaluation-insights.md +++ b/docs/en/guides/model-evaluation-insights.md @@ -128,7 +128,7 @@ Sharing your ideas and questions with other computer vision enthusiasts can insp ### Finding Help and Support - **GitHub Issues:** Explore the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. ### Official Documentation diff --git a/docs/en/guides/model-monitoring-and-maintenance.md b/docs/en/guides/model-monitoring-and-maintenance.md index 7864c66c98..ab5e417a3b 100644 --- a/docs/en/guides/model-monitoring-and-maintenance.md +++ b/docs/en/guides/model-monitoring-and-maintenance.md @@ -124,7 +124,7 @@ Joining a community of computer vision enthusiasts can help you solve problems a ### Community Resources - **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences. ### Official Documentation diff --git a/docs/en/guides/model-testing.md b/docs/en/guides/model-testing.md index 718d1d1115..71ff69b0be 100644 --- a/docs/en/guides/model-testing.md +++ b/docs/en/guides/model-testing.md @@ -129,7 +129,7 @@ Becoming part of a community of computer vision enthusiasts can aid in solving p ### Community Resources - **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences. ### Official Documentation diff --git a/docs/en/guides/model-training-tips.md b/docs/en/guides/model-training-tips.md index 20aaefa725..2efa53e70d 100644 --- a/docs/en/guides/model-training-tips.md +++ b/docs/en/guides/model-training-tips.md @@ -147,7 +147,7 @@ Being part of a community of computer vision enthusiasts can help you solve prob ### Community Resources - **GitHub Issues:** Visit the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences. ### Official Documentation diff --git a/docs/en/guides/nvidia-jetson.md b/docs/en/guides/nvidia-jetson.md index 504bfa9096..7e6bd41181 100644 --- a/docs/en/guides/nvidia-jetson.md +++ b/docs/en/guides/nvidia-jetson.md @@ -54,7 +54,7 @@ The first step after getting your hands on an NVIDIA Jetson device is to flash N 1. If you own an official NVIDIA Development Kit such as the Jetson Orin Nano Developer Kit, you can [download an image and prepare an SD card with JetPack for booting the device](https://developer.nvidia.com/embedded/learn/get-started-jetson-orin-nano-devkit). 2. If you own any other NVIDIA Development Kit, you can [flash JetPack to the device using SDK Manager](https://docs.nvidia.com/sdk-manager/install-with-sdkm-jetson/index.html). -3. If you own a Seeed Studio reComputer J4012 device, you can [flash JetPack to the included SSD](https://wiki.seeedstudio.com/reComputer_J4012_Flash_Jetpack) and if you own a Seeed Studio reComputer J1020 v2 device, you can [flash JetPack to the eMMC/ SSD](https://wiki.seeedstudio.com/reComputer_J2021_J202_Flash_Jetpack). +3. If you own a Seeed Studio reComputer J4012 device, you can [flash JetPack to the included SSD](https://wiki.seeedstudio.com/reComputer_J4012_Flash_Jetpack/) and if you own a Seeed Studio reComputer J1020 v2 device, you can [flash JetPack to the eMMC/ SSD](https://wiki.seeedstudio.com/reComputer_J2021_J202_Flash_Jetpack/). 4. If you own any other third party device powered by the NVIDIA Jetson module, it is recommended to follow [command-line flashing](https://docs.nvidia.com/jetson/archives/r35.5.0/DeveloperGuide/IN/QuickStart.html). !!! Note diff --git a/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md b/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md index a9acfb123d..92c840f3a4 100644 --- a/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md +++ b/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md @@ -115,7 +115,7 @@ Balancing latency and throughput optimization requires understanding your applic - **Latency Optimization:** Ideal for real-time applications requiring immediate responses (e.g., consumer-grade apps). - **Throughput Optimization:** Best for scenarios with many concurrent inferences, maximizing resource use (e.g., large-scale deployments). -Using OpenVINO's high-level performance hints and multi-device modes can help strike the right balance. Choose the appropriate [OpenVINO Performance hints](https://docs.ultralytics.com/integrations/openvino#openvino-performance-hints) based on your specific requirements. +Using OpenVINO's high-level performance hints and multi-device modes can help strike the right balance. Choose the appropriate [OpenVINO Performance hints](https://docs.ultralytics.com/integrations/openvino/#openvino-performance-hints) based on your specific requirements. ### Can I use Ultralytics YOLO models with other AI frameworks besides OpenVINO? diff --git a/docs/en/guides/preprocessing_annotated_data.md b/docs/en/guides/preprocessing_annotated_data.md index ef771a28ae..36fcf9f9c1 100644 --- a/docs/en/guides/preprocessing_annotated_data.md +++ b/docs/en/guides/preprocessing_annotated_data.md @@ -133,7 +133,7 @@ Having discussions about your project with other computer vision enthusiasts can ### Channels to Connect with the Community - **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. ### Official Documentation diff --git a/docs/en/guides/raspberry-pi.md b/docs/en/guides/raspberry-pi.md index 1d4b1f9dff..997c08547b 100644 --- a/docs/en/guides/raspberry-pi.md +++ b/docs/en/guides/raspberry-pi.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, Raspberry Pi, setup, guide, benchmarks, computer # Quick Start Guide: Raspberry Pi with Ultralytics YOLOv8 -This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [Raspberry Pi](https://www.raspberrypi.com) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices. +This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [Raspberry Pi](https://www.raspberrypi.com/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices.


diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index 3b98171d30..ec9b84ff2f 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -189,7 +189,7 @@ Connecting with a community of computer vision enthusiasts can help you tackle a ### Community Resources - **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The active community and maintainers are there to help with specific issues. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to interact with other users and developers, get support, and share insights. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to interact with other users and developers, get support, and share insights. ### Official Documentation diff --git a/docs/en/guides/streamlit-live-inference.md b/docs/en/guides/streamlit-live-inference.md index d6a356136d..24388eb302 100644 --- a/docs/en/guides/streamlit-live-inference.md +++ b/docs/en/guides/streamlit-live-inference.md @@ -86,7 +86,7 @@ Engage with the community to learn more, troubleshoot issues, and share your pro ### Where to Find Help and Support - **GitHub Issues:** Visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. -- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. +- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas. ### Official Documentation diff --git a/docs/en/guides/triton-inference-server.md b/docs/en/guides/triton-inference-server.md index dc69e9f390..1879bf78f3 100644 --- a/docs/en/guides/triton-inference-server.md +++ b/docs/en/guides/triton-inference-server.md @@ -6,7 +6,7 @@ keywords: Triton Inference Server, YOLOv8, Ultralytics, NVIDIA, deep learning, A # Triton Inference Server with Ultralytics YOLOv8 -The [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance deep learning inference workloads. This guide provides steps to set up and test the integration. +The [Triton Inference Server](https://developer.nvidia.com/triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance deep learning inference workloads. This guide provides steps to set up and test the integration.


@@ -147,7 +147,7 @@ By following the above steps, you can deploy and run Ultralytics YOLOv8 models e ### How do I set up Ultralytics YOLOv8 with NVIDIA Triton Inference Server? -Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) involves a few key steps: +Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps: 1. **Export YOLOv8 to ONNX format**: @@ -213,7 +213,7 @@ This setup can help you efficiently deploy YOLOv8 models at scale on Triton Infe ### What benefits does using Ultralytics YOLOv8 with NVIDIA Triton Inference Server offer? -Integrating [Ultralytics YOLOv8](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) provides several advantages: +Integrating [Ultralytics YOLOv8](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) provides several advantages: - **Scalable AI Inference**: Triton allows serving multiple models from a single server instance, supporting dynamic model loading and unloading, making it highly scalable for diverse AI workloads. - **High Performance**: Optimized for NVIDIA GPUs, Triton Inference Server ensures high-speed inference operations, perfect for real-time applications such as object detection. @@ -223,7 +223,7 @@ For detailed instructions on setting up and running YOLOv8 with Triton, you can ### Why should I export my YOLOv8 model to ONNX format before using Triton Inference Server? -Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLOv8](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) offers several key benefits: +Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLOv8](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) offers several key benefits: - **Interoperability**: ONNX format supports transfer between different deep learning frameworks (such as PyTorch, TensorFlow), ensuring broader compatibility. - **Optimization**: Many deployment environments, including Triton, optimize for ONNX, enabling faster inference and better performance. @@ -242,7 +242,7 @@ You can follow the steps in the [exporting guide](../modes/export.md) to complet ### Can I run inference using the Ultralytics YOLOv8 model on Triton Inference Server? -Yes, you can run inference using the [Ultralytics YOLOv8](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows: +Yes, you can run inference using the [Ultralytics YOLOv8](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows: ```python from ultralytics import YOLO diff --git a/docs/en/guides/yolo-common-issues.md b/docs/en/guides/yolo-common-issues.md index 849b44c42a..77351eaa06 100644 --- a/docs/en/guides/yolo-common-issues.md +++ b/docs/en/guides/yolo-common-issues.md @@ -121,7 +121,7 @@ You can access these metrics from the training logs or by using tools like Tenso - [TensorBoard](https://www.tensorflow.org/tensorboard): TensorBoard is a popular choice for visualizing training metrics, including loss, accuracy, and more. You can integrate it with your YOLOv8 training process. - [Comet](https://bit.ly/yolov8-readme-comet): Comet provides an extensive toolkit for experiment tracking and comparison. It allows you to track metrics, hyperparameters, and even model weights. Integration with YOLO models is also straightforward, providing you with a complete overview of your experiment cycle. -- [Ultralytics HUB](https://hub.ultralytics.com): Ultralytics HUB offers a specialized environment for tracking YOLO models, giving you a one-stop platform to manage metrics, datasets, and even collaborate with your team. Given its tailored focus on YOLO, it offers more customized tracking options. +- [Ultralytics HUB](https://hub.ultralytics.com/): Ultralytics HUB offers a specialized environment for tracking YOLO models, giving you a one-stop platform to manage metrics, datasets, and even collaborate with your team. Given its tailored focus on YOLO, it offers more customized tracking options. Each of these tools offers its own set of advantages, so you may want to consider the specific needs of your project when making a choice. @@ -270,7 +270,7 @@ Engaging with a community of like-minded individuals can significantly enhance y **GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems. -**Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and the developers. +**Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers. ### Official Documentation and Resources @@ -312,7 +312,7 @@ This sets the training process to the first GPU. Consult the `nvidia-smi` comman ### How can I monitor and track my YOLOv8 model training progress? -Tracking and visualizing training progress can be efficiently managed through tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [Comet](https://bit.ly/yolov8-readme-comet), and [Ultralytics HUB](https://hub.ultralytics.com). These tools allow you to log and visualize metrics such as loss, precision, recall, and mAP. Implementing [early stopping](#continuous-monitoring-parameters) based on these metrics can also help achieve better training outcomes. +Tracking and visualizing training progress can be efficiently managed through tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [Comet](https://bit.ly/yolov8-readme-comet), and [Ultralytics HUB](https://hub.ultralytics.com/). These tools allow you to log and visualize metrics such as loss, precision, recall, and mAP. Implementing [early stopping](#continuous-monitoring-parameters) based on these metrics can also help achieve better training outcomes. ### What should I do if YOLOv8 is not recognizing my dataset format? diff --git a/docs/en/guides/yolo-performance-metrics.md b/docs/en/guides/yolo-performance-metrics.md index ad59d4eb9d..d885b9eab3 100644 --- a/docs/en/guides/yolo-performance-metrics.md +++ b/docs/en/guides/yolo-performance-metrics.md @@ -159,7 +159,7 @@ Tapping into a community of enthusiasts and experts can amplify your journey wit - **GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems. -- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and the developers. +- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers. ### Official Documentation and Resources: diff --git a/docs/en/help/CI.md b/docs/en/help/CI.md index 0cea46c329..93b1ad3222 100644 --- a/docs/en/help/CI.md +++ b/docs/en/help/CI.md @@ -40,9 +40,9 @@ Remember, a successful CI test does not mean that everything is perfect. It is a Code coverage is a metric that represents the percentage of your codebase that is executed when your tests run. It provides insight into how well your tests exercise your code and can be crucial in identifying untested parts of your application. A high code coverage percentage is often associated with a lower likelihood of bugs. However, it's essential to understand that code coverage does not guarantee the absence of defects. It merely indicates which parts of the code have been executed by the tests. -### Integration with [codecov.io](https://codecov.io/) +### Integration with [codecov.io](https://about.codecov.io/) -At Ultralytics, we have integrated our repositories with [codecov.io](https://codecov.io/), a popular online platform for measuring and visualizing code coverage. Codecov provides detailed insights, coverage comparisons between commits, and visual overlays directly on your code, indicating which lines were covered. +At Ultralytics, we have integrated our repositories with [codecov.io](https://about.codecov.io/), a popular online platform for measuring and visualizing code coverage. Codecov provides detailed insights, coverage comparisons between commits, and visual overlays directly on your code, indicating which lines were covered. By integrating with Codecov, we aim to maintain and improve the quality of our code by focusing on areas that might be prone to errors or need further testing. @@ -84,4 +84,4 @@ Automated [PyPI publishing](https://github.com/ultralytics/ultralytics/actions/w ### How does Ultralytics measure code coverage and why is it important? -Ultralytics measures code coverage by integrating with [Codecov](https://codecov.io/github/ultralytics/ultralytics), providing insights into how much of the codebase is executed during tests. High code coverage can indicate well-tested code, helping to uncover untested areas that might be prone to bugs. Detailed code coverage metrics can be explored via badges displayed on our main repositories or directly on [Codecov](https://codecov.io/gh/ultralytics/ultralytics). +Ultralytics measures code coverage by integrating with [Codecov](https://app.codecov.io/github/ultralytics/ultralytics), providing insights into how much of the codebase is executed during tests. High code coverage can indicate well-tested code, helping to uncover untested areas that might be prone to bugs. Detailed code coverage metrics can be explored via badges displayed on our main repositories or directly on [Codecov](https://app.codecov.io/gh/ultralytics/ultralytics). diff --git a/docs/en/help/FAQ.md b/docs/en/help/FAQ.md index 5165a953d9..24472e77f4 100644 --- a/docs/en/help/FAQ.md +++ b/docs/en/help/FAQ.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, FAQ, object detection, hardware requirements, fine- # Ultralytics YOLO Frequently Asked Questions (FAQ) -This FAQ section addresses common questions and issues users might encounter while working with [Ultralytics](https://ultralytics.com) YOLO repositories. +This FAQ section addresses common questions and issues users might encounter while working with [Ultralytics](https://www.ultralytics.com/) YOLO repositories. ## FAQ @@ -222,7 +222,7 @@ Ultralytics provides a wealth of resources to help you get started and master th - 💻 [GitHub repository](https://github.com/ultralytics/ultralytics): Source code, example scripts, and community contributions. - ✍️ [Ultralytics blog](https://www.ultralytics.com/blog): In-depth articles, use cases, and technical insights. - 💬 [Community forums](https://community.ultralytics.com/): Connect with other users, ask questions, and share your experiences. -- 🎥 [YouTube channel](https://youtube.com/ultralytics?sub_confirmation=1): Video tutorials, demos, and webinars on various Ultralytics topics. +- 🎥 [YouTube channel](https://www.youtube.com/ultralytics?sub_confirmation=1): Video tutorials, demos, and webinars on various Ultralytics topics. These resources provide code examples, real-world use cases, and step-by-step guides for various tasks using Ultralytics models. diff --git a/docs/en/help/code_of_conduct.md b/docs/en/help/code_of_conduct.md index c8638cc61f..625ed601e4 100644 --- a/docs/en/help/code_of_conduct.md +++ b/docs/en/help/code_of_conduct.md @@ -78,7 +78,7 @@ Community leaders will follow these Community Impact Guidelines in determining t This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. -Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity). +Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/inclusion). For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations. @@ -104,6 +104,6 @@ Contributing to Ultralytics means engaging positively and respectfully with othe ### Where can I find additional information about the Ultralytics Code of Conduct? -For more comprehensive details about the Ultralytics Code of Conduct, including reporting guidelines and enforcement policies, you can visit the [Contributor Covenant homepage](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html) or check the [FAQ section of Contributor Covenant](https://www.contributor-covenant.org/faq). Learn more about Ultralytics' goals and initiatives on [our brand page](https://www.ultralytics.com/brand) and [about page](https://www.ultralytics.com/about). +For more comprehensive details about the Ultralytics Code of Conduct, including reporting guidelines and enforcement policies, you can visit the [Contributor Covenant homepage](https://www.contributor-covenant.org/version/2/0/code_of_conduct/) or check the [FAQ section of Contributor Covenant](https://www.contributor-covenant.org/faq/). Learn more about Ultralytics' goals and initiatives on [our brand page](https://www.ultralytics.com/brand) and [about page](https://www.ultralytics.com/about). Should you have more questions or need further assistance, check our [Help Center](../help/FAQ.md) and [Contributing Guide](../help/contributing.md) for more information. diff --git a/docs/en/help/contributing.md b/docs/en/help/contributing.md index a4c23e99dd..637c1ae86e 100644 --- a/docs/en/help/contributing.md +++ b/docs/en/help/contributing.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, open-source, contribution, pull request, code of co # Contributing to Ultralytics Open-Source Projects -Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://ultralytics.com) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire community. This guide provides clear guidelines and best practices to help you get started. +Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire community. This guide provides clear guidelines and best practices to help you get started. Ultralytics open-source contributors @@ -133,7 +133,7 @@ We encourage all contributors to familiarize themselves with the terms of the AG ## Conclusion -Thank you for your interest in contributing to [Ultralytics](https://ultralytics.com) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable. +Thank you for your interest in contributing to [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable. We're excited to see your ideas come to life and appreciate your commitment to advancing object detection technology. Together, let's continue to grow and innovate in this exciting open-source journey. Happy coding! 🚀🌟 diff --git a/docs/en/help/minimum_reproducible_example.md b/docs/en/help/minimum_reproducible_example.md index 92eb629938..eb4e25368c 100644 --- a/docs/en/help/minimum_reproducible_example.md +++ b/docs/en/help/minimum_reproducible_example.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, Minimum Reproducible Example, MRE, bug report, issu # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories -When submitting a bug report for [Ultralytics](https://ultralytics.com) [YOLO](https://github.com/ultralytics) repositories, it's essential to provide a [Minimum Reproducible Example (MRE)](https://stackoverflow.com/help/minimal-reproducible-example). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories. +When submitting a bug report for [Ultralytics](https://www.ultralytics.com/) [YOLO](https://github.com/ultralytics) repositories, it's essential to provide a [Minimum Reproducible Example (MRE)](https://stackoverflow.com/help/minimal-reproducible-example). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories. ## 1. Isolate the Problem diff --git a/docs/en/help/privacy.md b/docs/en/help/privacy.md index 0453569e3d..a053f199fe 100644 --- a/docs/en/help/privacy.md +++ b/docs/en/help/privacy.md @@ -7,7 +7,7 @@ keywords: Ultralytics, data collection, YOLO, Python package, Google Analytics, ## Overview -[Ultralytics](https://ultralytics.com) is dedicated to the continuous enhancement of the user experience and the capabilities of our Python package, including the advanced YOLO models we develop. Our approach involves the gathering of anonymized usage statistics and crash reports, helping us identify opportunities for improvement and ensuring the reliability of our software. This transparency document outlines what data we collect, its purpose, and the choice you have regarding this data collection. +[Ultralytics](https://www.ultralytics.com/) is dedicated to the continuous enhancement of the user experience and the capabilities of our Python package, including the advanced YOLO models we develop. Our approach involves the gathering of anonymized usage statistics and crash reports, helping us identify opportunities for improvement and ensuring the reliability of our software. This transparency document outlines what data we collect, its purpose, and the choice you have regarding this data collection. ## Anonymized Google Analytics @@ -37,7 +37,7 @@ We take several measures to ensure the privacy and security of the data you entr ## Sentry Crash Reporting -[Sentry](https://sentry.io/) is a developer-centric error tracking software that aids in identifying, diagnosing, and resolving issues in real-time, ensuring the robustness and reliability of applications. Within our package, it plays a crucial role by providing insights through crash reporting, significantly contributing to the stability and ongoing refinement of our software. +[Sentry](https://sentry.io/welcome/) is a developer-centric error tracking software that aids in identifying, diagnosing, and resolving issues in real-time, ensuring the robustness and reliability of applications. Within our package, it plays a crucial role by providing insights through crash reporting, significantly contributing to the stability and ongoing refinement of our software. !!! Note @@ -138,7 +138,7 @@ Ultralytics takes user privacy seriously. We design our data collection practice ## Questions or Concerns -If you have any questions or concerns about our data collection practices, please reach out to us via our [contact form](https://ultralytics.com/contact) or via [support@ultralytics.com](mailto:support@ultralytics.com). We are dedicated to ensuring our users feel informed and confident in their privacy when using our package. +If you have any questions or concerns about our data collection practices, please reach out to us via our [contact form](https://www.ultralytics.com/contact) or via [support@ultralytics.com](mailto:support@ultralytics.com). We are dedicated to ensuring our users feel informed and confident in their privacy when using our package. ## FAQ diff --git a/docs/en/help/security.md b/docs/en/help/security.md index 553a0b2408..39fe3829ff 100644 --- a/docs/en/help/security.md +++ b/docs/en/help/security.md @@ -5,7 +5,7 @@ keywords: Ultralytics security policy, Snyk scanning, CodeQL scanning, Dependabo # Ultralytics Security Policy -At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities. +At [Ultralytics](https://www.ultralytics.com/), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities. ## Snyk Scanning @@ -15,7 +15,7 @@ We utilize [Snyk](https://snyk.io/advisor/python/ultralytics) to conduct compreh ## GitHub CodeQL Scanning -Our security strategy includes GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scanning. CodeQL delves deep into our codebase, identifying complex vulnerabilities like SQL injection and XSS by analyzing the code's semantic structure. This advanced level of analysis ensures early detection and resolution of potential security risks. +Our security strategy includes GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/introduction-to-code-scanning/about-code-scanning-with-codeql) scanning. CodeQL delves deep into our codebase, identifying complex vulnerabilities like SQL injection and XSS by analyzing the code's semantic structure. This advanced level of analysis ensures early detection and resolution of potential security risks. [![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml) @@ -31,7 +31,7 @@ We employ GitHub [secret scanning](https://docs.github.com/en/code-security/secr We enable private vulnerability reporting, allowing users to discreetly report potential security issues. This approach facilitates responsible disclosure, ensuring vulnerabilities are handled securely and efficiently. -If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible. +If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://www.ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible. We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone 🙏. @@ -57,7 +57,7 @@ To see the Snyk badge and learn more about its deployment, check the [Snyk Scann ### What is CodeQL and how does it enhance security for Ultralytics? -[CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) is a security analysis tool integrated into Ultralytics' workflow via GitHub. It delves deep into the codebase to identify complex vulnerabilities such as SQL injection and Cross-Site Scripting (XSS). CodeQL analyzes the semantic structure of the code to provide an advanced level of security, ensuring early detection and mitigation of potential risks. +[CodeQL](https://docs.github.com/en/code-security/code-scanning/introduction-to-code-scanning/about-code-scanning-with-codeql) is a security analysis tool integrated into Ultralytics' workflow via GitHub. It delves deep into the codebase to identify complex vulnerabilities such as SQL injection and Cross-Site Scripting (XSS). CodeQL analyzes the semantic structure of the code to provide an advanced level of security, ensuring early detection and mitigation of potential risks. For more information on how CodeQL is used, visit the [GitHub CodeQL Scanning section](#github-codeql-scanning). @@ -69,6 +69,6 @@ For more details, explore the [GitHub Dependabot Alerts section](#github-dependa ### How does Ultralytics handle private vulnerability reporting? -Ultralytics encourages users to report potential security issues through private channels. Users can report vulnerabilities discreetly via the [contact form](https://ultralytics.com/contact) or by emailing [security@ultralytics.com](mailto:security@ultralytics.com). This ensures responsible disclosure and allows the security team to investigate and address vulnerabilities securely and efficiently. +Ultralytics encourages users to report potential security issues through private channels. Users can report vulnerabilities discreetly via the [contact form](https://www.ultralytics.com/contact) or by emailing [security@ultralytics.com](mailto:security@ultralytics.com). This ensures responsible disclosure and allows the security team to investigate and address vulnerabilities securely and efficiently. For more information on private vulnerability reporting, refer to the [Private Vulnerability Reporting section](#private-vulnerability-reporting). diff --git a/docs/en/hub/api/index.md b/docs/en/hub/api/index.md index b417161514..9ae12c3db5 100644 --- a/docs/en/hub/api/index.md +++ b/docs/en/hub/api/index.md @@ -17,13 +17,13 @@ Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work d This placeholder page is your first stop for upcoming developments. Keep an eye out for: -- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news. +- **Newsletter:** Subscribe [here](https://www.ultralytics.com/#newsletter) for the latest news. - **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers. -- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights. +- **Blog:** Visit our [blog](https://www.ultralytics.com/blog) for detailed insights. ## We Value Your Input 🗣️ -Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact). +Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://www.ultralytics.com/contact). ## Thank You, Community! 🌍 diff --git a/docs/en/hub/app/android.md b/docs/en/hub/app/android.md index c3c19b0c17..365180545d 100644 --- a/docs/en/hub/app/android.md +++ b/docs/en/hub/app/android.md @@ -60,7 +60,7 @@ INT8 (or 8-bit integer) quantization further reduces the model's size and comput ## Delegates and Performance Variability -Different delegates are available on Android devices to accelerate model inference. These delegates include CPU, [GPU](https://www.tensorflow.org/lite/android/delegates/gpu), [Hexagon](https://www.tensorflow.org/lite/android/delegates/hexagon) and [NNAPI](https://www.tensorflow.org/lite/android/delegates/nnapi). The performance of these delegates varies depending on the device's hardware vendor, product line, and specific chipsets used in the device. +Different delegates are available on Android devices to accelerate model inference. These delegates include CPU, [GPU](https://ai.google.dev/edge/litert/android/gpu), [Hexagon](https://developer.android.com/ndk/guides/neuralnetworks/migration-guide) and [NNAPI](https://developer.android.com/ndk/guides/neuralnetworks/migration-guide). The performance of these delegates varies depending on the device's hardware vendor, product line, and specific chipsets used in the device. 1. **CPU**: The default option, with reasonable performance on most devices. 2. **GPU**: Utilizes the device's GPU for faster inference. It can provide a significant performance boost on devices with powerful GPUs. @@ -69,13 +69,13 @@ Different delegates are available on Android devices to accelerate model inferen Here's a table showing the primary vendors, their product lines, popular devices, and supported delegates: -| Vendor | Product Lines | Popular Devices | Delegates Supported | -| --------------------------------------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | -| [Qualcomm](https://www.qualcomm.com/) | [Snapdragon (e.g., 800 series)](https://www.qualcomm.com/snapdragon) | [Samsung Galaxy S21](https://www.samsung.com/global/galaxy/galaxy-s21-5g/), [OnePlus 9](https://www.oneplus.com/9), [Google Pixel 6](https://store.google.com/product/pixel_6) | CPU, GPU, Hexagon, NNAPI | -| [Samsung](https://www.samsung.com/) | [Exynos (e.g., Exynos 2100)](https://www.samsung.com/semiconductor/minisite/exynos/) | [Samsung Galaxy S21 (Global version)](https://www.samsung.com/global/galaxy/galaxy-s21-5g/) | CPU, GPU, NNAPI | -| [MediaTek](https://i.mediatek.com/) | [Dimensity (e.g., Dimensity 1200)](https://i.mediatek.com/dimensity-1200) | [Realme GT](https://www.realme.com/global/realme-gt), [Xiaomi Redmi Note](https://www.mi.com/en/phone/redmi/note-list) | CPU, GPU, NNAPI | -| [HiSilicon](https://www.hisilicon.com/) | [Kirin (e.g., Kirin 990)](https://www.hisilicon.com/en/products/Kirin) | [Huawei P40 Pro](https://consumer.huawei.com/en/phones/p40-pro/), [Huawei Mate 30 Pro](https://consumer.huawei.com/en/phones/mate30-pro/) | CPU, GPU, NNAPI | -| [NVIDIA](https://www.nvidia.com/) | [Tegra (e.g., Tegra X1)](https://developer.nvidia.com/content/tegra-x1) | [NVIDIA Shield TV](https://www.nvidia.com/en-us/shield/shield-tv/), [Nintendo Switch](https://www.nintendo.com/switch/) | CPU, GPU, NNAPI | +| Vendor | Product Lines | Popular Devices | Delegates Supported | +| ----------------------------------------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | +| [Qualcomm](https://www.qualcomm.com/) | [Snapdragon (e.g., 800 series)](https://www.qualcomm.com/snapdragon/overview) | [Samsung Galaxy S21](https://www.samsung.com/global/galaxy/galaxy-s21-5g/), [OnePlus 9](https://www.oneplus.com/9), [Google Pixel 6](https://store.google.com/product/pixel_6) | CPU, GPU, Hexagon, NNAPI | +| [Samsung](https://www.samsung.com/) | [Exynos (e.g., Exynos 2100)](https://www.samsung.com/semiconductor/minisite/exynos/) | [Samsung Galaxy S21 (Global version)](https://www.samsung.com/global/galaxy/galaxy-s21-5g/) | CPU, GPU, NNAPI | +| [MediaTek](https://i.mediatek.com/) | [Dimensity (e.g., Dimensity 1200)](https://i.mediatek.com/dimensity-1200) | [Realme GT](https://www.realme.com/global/realme-gt), [Xiaomi Redmi Note](https://www.mi.com/global/phone/redmi/note-list) | CPU, GPU, NNAPI | +| [HiSilicon](https://www.hisilicon.com/cn) | [Kirin (e.g., Kirin 990)](https://www.hisilicon.com/en/products/Kirin) | [Huawei P40 Pro](https://consumer.huawei.com/en/phones/), [Huawei Mate 30 Pro](https://consumer.huawei.com/en/phones/) | CPU, GPU, NNAPI | +| [NVIDIA](https://www.nvidia.com/) | [Tegra (e.g., Tegra X1)](https://developer.nvidia.com/content/tegra-x1) | [NVIDIA Shield TV](https://www.nvidia.com/en-us/shield/shield-tv/), [Nintendo Switch](https://www.nintendo.com/switch/) | CPU, GPU, NNAPI | Please note that the list of devices mentioned is not exhaustive and may vary depending on the specific chipsets and device models. Always test your models on your target devices to ensure compatibility and optimal performance. diff --git a/docs/en/hub/cloud-training.md b/docs/en/hub/cloud-training.md index 9d09a18fc0..42dd681080 100644 --- a/docs/en/hub/cloud-training.md +++ b/docs/en/hub/cloud-training.md @@ -6,9 +6,9 @@ keywords: Ultralytics HUB, cloud training, model training, Pro Plan, easy AI set # Ultralytics HUB Cloud Training -We've listened to the high demand and widespread interest and are thrilled to unveil [Ultralytics HUB](https://ultralytics.com/hub) Cloud Training, offering a single-click training experience for our [Pro](./pro.md) users! +We've listened to the high demand and widespread interest and are thrilled to unveil [Ultralytics HUB](https://www.ultralytics.com/hub) Cloud Training, offering a single-click training experience for our [Pro](./pro.md) users! -[Ultralytics HUB](https://ultralytics.com/hub) [Pro](./pro.md) users can finetune [Ultralytics HUB](https://ultralytics.com/hub) models on a custom dataset using our Cloud Training solution, making the model training process easy. Say goodbye to complex setups and hello to streamlined workflows with [Ultralytics HUB](https://ultralytics.com/hub)'s intuitive interface. +[Ultralytics HUB](https://www.ultralytics.com/hub) [Pro](./pro.md) users can finetune [Ultralytics HUB](https://www.ultralytics.com/hub) models on a custom dataset using our Cloud Training solution, making the model training process easy. Say goodbye to complex setups and hello to streamlined workflows with [Ultralytics HUB](https://www.ultralytics.com/hub)'s intuitive interface.