`ultralytics 8.0.58` new SimpleClass, fixes and updates (#1636)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

.github/workflows/greetings.yml

@@ -18,8 +18,9 @@ jobs:
           pr-message: |
             👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv8 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
 
-            - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
+            - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
             - ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
+            - ✅ Update YOLOv8 [Docs](https://docs.ultralytics.com) for any new or updated features.
             - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
 
             See our [Contributing Guide](https://github.com/ultralytics/ultralytics/blob/main/CONTRIBUTING.md) for details and let us know if you have any questions!
@@ -33,7 +34,7 @@ jobs:
 
             ## Install
 
-            Pip install the `ultralytics` package including all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+            Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
 
             ```bash
             pip install ultralytics

.github/workflows/links.yml

@@ -23,7 +23,7 @@ jobs:
         with:
           fail: true
           # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter)
-          args: --accept 429,999 --exclude twitter.com --verbose --no-progress './**/*.md' './**/*.html'
+          args: --accept 429,999 --exclude-loopback --exclude twitter.com --verbose --no-progress './**/*.md' './**/*.html'
         env:
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
 
@@ -33,6 +33,6 @@ jobs:
         with:
           fail: true
           # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter)
-          args: --accept 429,999 --exclude twitter.com,url.com --verbose --no-progress './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          args: --accept 429,999 --exclude-loopback --exclude twitter.com,url.com --verbose --no-progress './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
         env:
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

README.md

@@ -58,7 +58,7 @@ full documentation on training, validation, prediction and deployment.
 <summary>Install</summary>
 
 Pip install the ultralytics package including
-all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
+all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
 [**Python>=3.7**](https://www.python.org/) environment with
 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
 
@@ -105,28 +105,11 @@ success = model.export(format="onnx")  # export the model to ONNX format
 Ultralytics [release](https://github.com/ultralytics/assets/releases). See
 YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
 
-#### Model Architectures
-
-⭐ **NEW** YOLOv5u anchor free models are now available.
-
-All supported model architectures can be found in the [Models](./ultralytics/models/) section.
-
-#### Known Issues / TODOs
-
-We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
-to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
-will submit to [arxiv.org](https://arxiv.org) once complete.
-
-- [x] TensorFlow exports
-- [x] DDP resume
-- [ ] [arxiv.org](https://arxiv.org) paper
-
 </details>
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
-while Classification models are pretrained on the ImageNet dataset.
+All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
 Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@@ -147,7 +130,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
   <br>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.
-  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -167,7 +150,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
   <br>Reproduce by `yolo val segment 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.
-  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -187,7 +170,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
   <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
 - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
   instance.
-  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 </details>
 

README.zh-CN.md

@@ -52,7 +52,7 @@ SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能
 <details open>
 <summary>安装</summary>
 
-Pip 安装包含所有 [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的
+Pip 安装包含所有 [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的
 ultralytics 包，环境要求 [**Python>=3.7**](https://www.python.org/)，且 [\*\*PyTorch>=1.7
 \*\*](https://pytorch.org/get-started/locally/)。
 
@@ -100,15 +100,6 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 [模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
 
-### 已知问题 / 待办事项
-
-我们仍在努力完善 YOLOv8 的几个部分！我们的目标是尽快完成这些工作，使 YOLOv8 的功能设置达到YOLOv5
-的水平，包括对所有相同格式的导出和推理。我们还在写一篇 YOLOv8 的论文，一旦完成，我们将提交给 [arxiv.org](https://arxiv.org)。
-
-- [x] TensorFlow 导出
-- [x] DDP 恢复训练
-- [ ] [arxiv.org](https://arxiv.org) 论文
-
 </details>
 
 ## <div align="center">模型</div>
@@ -132,7 +123,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val detect data=coco.yaml device=0`
 - **推理速度**使用 COCO
   验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
-  <br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
+  <br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -150,7 +141,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val segment data=coco.yaml device=0`
 - **推理速度**使用 COCO
   验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
-  <br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
+  <br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -168,7 +159,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val classify data=path/to/ImageNet device=0`
 - **推理速度**使用 ImageNet
   验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
-  <br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
+  <br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 </details>
 

docs/tasks/classify.md

@@ -9,9 +9,31 @@ of that class are located or what their exact shape is.
 
 !!! tip "Tip"
 
-    YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Classify models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
+Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+| Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------|
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224                   | 66.6             | 87.0             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224                   | 72.3             | 91.1             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224                   | 76.4             | 93.2             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224                   | 78.0             | 94.1             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224                   | 78.4             | 94.3             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
+
+- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 ## Train
 

docs/tasks/detect.md

@@ -9,9 +9,31 @@ scene, but don't need to know exactly where the object is or its exact shape.
 
 !!! tip "Tip"
 
-    YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
+Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|--------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>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.
+  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 ## Train
 

docs/tasks/segment.md

@@ -9,9 +9,31 @@ segmentation is useful when you need to know not only where objects are in an im
 
 !!! tip "Tip"
 
-    YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Segment models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
+Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/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/v0.0.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>Reproduce by `yolo val segment 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.
+  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 ## Train
 

examples/tutorial.ipynb

@@ -59,21 +59,21 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "9bda69d4-e57f-404b-b6fe-117234e24677"
+        "outputId": "ea235da2-8fb5-4094-9dc2-8523d0800a22"
       },
       "source": [
         "%pip install ultralytics\n",
         "import ultralytics\n",
         "ultralytics.checks()"
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
-            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.8/166.8 GB disk)\n"
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
+            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 25.9/166.8 GB disk)\n"
           ]
         }
       ]
@@ -96,28 +96,24 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "abe002b5-3df9-4324-9e50-1587394398a2"
+        "outputId": "fe0a5a26-3bcc-4c1f-e688-cae00ee5b958"
       },
       "source": [
         "# Run inference on an image with YOLOv8n\n",
         "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
       ],
-      "execution_count": null,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
-            "\r  0% 0.00/6.23M [00:00<?, ?B/s]\r100% 6.23M/6.23M [00:00<00:00, 266MB/s]\n",
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
             "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
             "\n",
-            "Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
-            "100% 165k/165k [00:00<00:00, 87.4MB/s]\n",
-            "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 13.3ms\n",
-            "Speed: 0.5ms preprocess, 13.3ms inference, 43.5ms postprocess per image at shape (1, 3, 640, 640)\n",
-            "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
+            "Found https://ultralytics.com/images/zidane.jpg locally at zidane.jpg\n",
+            "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 14.3ms\n",
+            "Speed: 0.5ms preprocess, 14.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n"
           ]
         }
       ]
@@ -160,7 +156,7 @@
       "cell_type": "code",
       "metadata": {
         "id": "X58w8JLpMnjH",
-        "outputId": "df71d7a8-f42f-473a-c143-75f033c58433",
+        "outputId": "ae2040df-0f95-4701-c680-8bbb7be92bcd",
         "colab": {
           "base_uri": "https://localhost:8080/"
         }
@@ -169,26 +165,26 @@
         "# Validate YOLOv8n on COCO128 val\n",
         "!yolo val model=yolov8n.pt data=coco128.yaml"
       ],
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
             "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
             "\n",
-            "Dataset 'coco128.yaml' not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
+            "Dataset 'coco128.yaml' images not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
             "Downloading https://ultralytics.com/assets/coco128.zip to /content/datasets/coco128.zip...\n",
-            "100% 6.66M/6.66M [00:00<00:00, 50.5MB/s]\n",
-            "Unzipping /content/datasets/coco128.zip...\n",
-            "Dataset download success ✅ (0.5s), saved to \u001b[1m/content/datasets\u001b[0m\n",
+            "100% 6.66M/6.66M [00:00<00:00, 87.2MB/s]\n",
+            "Unzipping /content/datasets/coco128.zip to /content/datasets...\n",
+            "Dataset download success ✅ (0.4s), saved to \u001b[1m/content/datasets\u001b[0m\n",
             "\n",
             "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
-            "100% 755k/755k [00:00<00:00, 99.8MB/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1290.29it/s]\n",
+            "100% 755k/755k [00:00<00:00, 16.9MB/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 2007.12it/s]\n",
             "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 8/8 [00:06<00:00,  1.22it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 8/8 [00:08<00:00,  1.04s/it]\n",
             "                   all        128        929       0.64      0.537      0.605      0.446\n",
             "                person        128        254      0.797      0.677      0.764      0.538\n",
             "               bicycle        128          6      0.514      0.333      0.315      0.264\n",
@@ -261,7 +257,8 @@
             "              scissors        128          1          1          0      0.249     0.0746\n",
             "            teddy bear        128         21      0.877      0.333      0.591      0.394\n",
             "            toothbrush        128          5      0.743        0.6      0.638      0.374\n",
-            "Speed: 2.4ms preprocess, 7.8ms inference, 0.0ms loss, 3.3ms postprocess per image\n"
+            "Speed: 2.9ms preprocess, 6.2ms inference, 0.0ms loss, 5.1ms postprocess per image\n",
+            "Results saved to \u001b[1mruns/detect/val\u001b[0m\n"
           ]
         }
       ]
@@ -283,7 +280,7 @@
       "cell_type": "code",
       "metadata": {
         "id": "1NcFxRcFdJ_O",
-        "outputId": "e0978a9a-ef1d-4a20-8082-19c8049d8c7e",
+        "outputId": "fcb5e3da-3766-4c72-97e1-73c1bd2ccbef",
         "colab": {
           "base_uri": "https://localhost:8080/"
         }
@@ -292,14 +289,14 @@
         "# Train YOLOv8n on COCO128 for 3 epochs\n",
         "!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
       ],
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
-            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train\n",
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
+            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train\n",
             "\n",
             "                   from  n    params  module                                       arguments                     \n",
             "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
@@ -328,111 +325,120 @@
             "Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
             "\n",
             "Transferred 355/355 items from pretrained weights\n",
+            "2023-03-26 14:57:47.224672: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+            "2023-03-26 14:57:48.209047: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
+            "2023-03-26 14:57:48.209179: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
+            "2023-03-26 14:57:48.209199: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
+            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
             "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
             "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
             "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
             "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
+            "Plotting labels to runs/detect/train/labels.jpg... \n",
             "Image sizes 640 train, 640 val\n",
             "Using 2 dataloader workers\n",
             "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
             "Starting training for 3 epochs...\n",
             "\n",
             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        1/3      4.31G      1.221      1.429      1.241        196        640: 100% 8/8 [00:08<00:00,  1.01s/it]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.18it/s]\n",
-            "                   all        128        929      0.671      0.516      0.617      0.457\n",
+            "        1/3      2.77G      1.221      1.429      1.241        196        640: 100% 8/8 [00:10<00:00,  1.34s/it]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.70it/s]\n",
+            "                   all        128        929      0.674      0.517      0.616      0.456\n",
             "\n",
             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        2/3      5.31G      1.186      1.306      1.255        287        640: 100% 8/8 [00:04<00:00,  1.63it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.23it/s]\n",
-            "                   all        128        929      0.668      0.582      0.637      0.473\n",
+            "        2/3      3.61G      1.186      1.306      1.255        287        640: 100% 8/8 [00:05<00:00,  1.58it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.83it/s]\n",
+            "                   all        128        929      0.644      0.599      0.638      0.473\n",
             "\n",
             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        3/3      5.31G       1.17      1.408      1.267        189        640: 100% 8/8 [00:04<00:00,  1.64it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.03it/s]\n",
-            "                   all        128        929      0.638      0.601      0.645      0.483\n",
+            "        3/3      3.61G      1.169      1.405      1.266        189        640: 100% 8/8 [00:04<00:00,  1.65it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:06<00:00,  1.59s/it]\n",
+            "                   all        128        929       0.63      0.607      0.641       0.48\n",
             "\n",
-            "3 epochs completed in 0.009 hours.\n",
+            "3 epochs completed in 0.011 hours.\n",
             "Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
             "Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
             "\n",
             "Validating runs/detect/train/weights/best.pt...\n",
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
             "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:04<00:00,  1.05s/it]\n",
-            "                   all        128        929      0.638      0.602      0.644      0.483\n",
-            "                person        128        254      0.703      0.709      0.769      0.548\n",
-            "               bicycle        128          6      0.455      0.333      0.322      0.254\n",
-            "                   car        128         46      0.773      0.217      0.291      0.184\n",
-            "            motorcycle        128          5      0.551        0.8      0.895      0.724\n",
-            "              airplane        128          6      0.743      0.833      0.927       0.73\n",
-            "                   bus        128          7      0.692      0.714        0.7      0.636\n",
-            "                 train        128          3      0.733      0.931      0.913      0.797\n",
-            "                 truck        128         12      0.752        0.5      0.497      0.324\n",
-            "                  boat        128          6       0.41      0.333      0.492      0.344\n",
-            "         traffic light        128         14      0.682      0.214      0.202      0.139\n",
-            "             stop sign        128          2      0.933          1      0.995      0.671\n",
-            "                 bench        128          9      0.752      0.556      0.603      0.416\n",
-            "                  bird        128         16      0.875      0.876      0.957      0.641\n",
-            "                   cat        128          4      0.863          1      0.995       0.76\n",
-            "                   dog        128          9      0.554      0.778      0.855      0.664\n",
-            "                 horse        128          2      0.706          1      0.995      0.561\n",
-            "              elephant        128         17      0.761      0.882      0.929      0.722\n",
-            "                  bear        128          1      0.595          1      0.995      0.995\n",
-            "                 zebra        128          4       0.85          1      0.995      0.966\n",
-            "               giraffe        128          9      0.891          1      0.995      0.683\n",
-            "              backpack        128          6      0.487      0.333      0.354      0.224\n",
-            "              umbrella        128         18       0.54      0.667      0.687      0.461\n",
-            "               handbag        128         19      0.496      0.105      0.212      0.125\n",
-            "                   tie        128          7      0.611      0.714      0.615      0.432\n",
-            "              suitcase        128          4      0.469          1      0.745      0.529\n",
-            "               frisbee        128          5      0.622        0.8      0.733       0.64\n",
-            "                  skis        128          1      0.721          1      0.995      0.531\n",
-            "             snowboard        128          7      0.687      0.714      0.751       0.51\n",
-            "           sports ball        128          6       0.71       0.42      0.503      0.282\n",
-            "                  kite        128         10       0.81        0.5       0.59      0.197\n",
-            "          baseball bat        128          4      0.474      0.461      0.261      0.115\n",
-            "        baseball glove        128          7       0.67      0.429       0.43      0.317\n",
-            "            skateboard        128          5      0.751        0.6      0.599      0.387\n",
-            "         tennis racket        128          7      0.742      0.415      0.507      0.378\n",
-            "                bottle        128         18      0.409      0.333      0.354      0.235\n",
-            "            wine glass        128         16      0.562        0.5      0.597      0.356\n",
-            "                   cup        128         36       0.67      0.306      0.411      0.296\n",
-            "                  fork        128          6       0.57      0.167      0.229      0.203\n",
-            "                 knife        128         16      0.608      0.562      0.634      0.405\n",
-            "                 spoon        128         22      0.529      0.358      0.369      0.201\n",
-            "                  bowl        128         28      0.594      0.679      0.671       0.56\n",
-            "                banana        128          1     0.0625      0.312      0.199     0.0513\n",
-            "              sandwich        128          2      0.638      0.913      0.828      0.828\n",
-            "                orange        128          4      0.743      0.728      0.895      0.595\n",
-            "              broccoli        128         11       0.49      0.264      0.278      0.232\n",
-            "                carrot        128         24      0.547      0.667      0.704       0.47\n",
-            "               hot dog        128          2      0.578          1      0.828      0.796\n",
-            "                 pizza        128          5      0.835          1      0.995       0.84\n",
-            "                 donut        128         14      0.537          1      0.891      0.788\n",
-            "                  cake        128          4      0.807          1      0.995      0.904\n",
-            "                 chair        128         35      0.401      0.514      0.485      0.277\n",
-            "                 couch        128          6      0.795      0.649      0.746      0.504\n",
-            "          potted plant        128         14      0.563      0.643      0.676      0.471\n",
-            "                   bed        128          3      0.777          1      0.995      0.735\n",
-            "          dining table        128         13      0.425      0.692      0.578       0.48\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:06<00:00,  1.66s/it]\n",
+            "                   all        128        929      0.632      0.597      0.639      0.479\n",
+            "                person        128        254      0.708      0.693      0.766       0.55\n",
+            "               bicycle        128          6      0.458      0.333      0.323      0.254\n",
+            "                   car        128         46      0.763      0.217      0.287      0.181\n",
+            "            motorcycle        128          5      0.542        0.8      0.895      0.727\n",
+            "              airplane        128          6      0.744      0.833      0.903      0.695\n",
+            "                   bus        128          7      0.699      0.714      0.724       0.64\n",
+            "                 train        128          3       0.73      0.919      0.913      0.797\n",
+            "                 truck        128         12      0.747        0.5      0.497      0.324\n",
+            "                  boat        128          6      0.365      0.333      0.393      0.291\n",
+            "         traffic light        128         14      0.708      0.214      0.202      0.139\n",
+            "             stop sign        128          2          1      0.953      0.995      0.666\n",
+            "                 bench        128          9      0.754      0.556      0.606      0.412\n",
+            "                  bird        128         16      0.929      0.819      0.948      0.643\n",
+            "                   cat        128          4      0.864          1      0.995      0.778\n",
+            "                   dog        128          9      0.598      0.828      0.834      0.627\n",
+            "                 horse        128          2      0.679          1      0.995      0.547\n",
+            "              elephant        128         17      0.757      0.882      0.929      0.722\n",
+            "                  bear        128          1      0.593          1      0.995      0.995\n",
+            "                 zebra        128          4      0.851          1      0.995      0.966\n",
+            "               giraffe        128          9      0.838          1      0.984      0.681\n",
+            "              backpack        128          6      0.473      0.333      0.342      0.217\n",
+            "              umbrella        128         18      0.569      0.667      0.708      0.454\n",
+            "               handbag        128         19      0.649      0.105      0.233      0.137\n",
+            "                   tie        128          7      0.608      0.714      0.614      0.428\n",
+            "              suitcase        128          4      0.471          1      0.745       0.54\n",
+            "               frisbee        128          5      0.629        0.8      0.732       0.64\n",
+            "                  skis        128          1      0.699          1      0.995      0.522\n",
+            "             snowboard        128          7      0.654      0.714      0.758      0.497\n",
+            "           sports ball        128          6      0.707      0.415      0.515      0.288\n",
+            "                  kite        128         10      0.687        0.4      0.561      0.207\n",
+            "          baseball bat        128          4      0.439      0.409      0.263      0.114\n",
+            "        baseball glove        128          7      0.679      0.429       0.43      0.317\n",
+            "            skateboard        128          5      0.738        0.6      0.599      0.386\n",
+            "         tennis racket        128          7      0.738      0.408      0.493      0.371\n",
+            "                bottle        128         18       0.44      0.333      0.377      0.247\n",
+            "            wine glass        128         16      0.545        0.5      0.596      0.358\n",
+            "                   cup        128         36      0.651      0.278      0.412      0.297\n",
+            "                  fork        128          6      0.568      0.167      0.229      0.202\n",
+            "                 knife        128         16      0.557      0.562      0.628      0.399\n",
+            "                 spoon        128         22      0.471      0.318      0.369      0.214\n",
+            "                  bowl        128         28      0.611      0.679      0.671      0.547\n",
+            "                banana        128          1     0.0573      0.286      0.199     0.0522\n",
+            "              sandwich        128          2      0.377        0.5      0.745      0.745\n",
+            "                orange        128          4      0.743      0.729      0.895      0.595\n",
+            "              broccoli        128         11      0.491      0.265      0.281      0.233\n",
+            "                carrot        128         24      0.548      0.667      0.694      0.465\n",
+            "               hot dog        128          2      0.586          1      0.828      0.796\n",
+            "                 pizza        128          5      0.873          1      0.995      0.798\n",
+            "                 donut        128         14      0.554          1      0.891      0.786\n",
+            "                  cake        128          4      0.806          1      0.995      0.904\n",
+            "                 chair        128         35      0.408      0.514      0.469      0.267\n",
+            "                 couch        128          6      0.517        0.5      0.655      0.463\n",
+            "          potted plant        128         14      0.564      0.643      0.673      0.467\n",
+            "                   bed        128          3       0.72          1      0.995      0.734\n",
+            "          dining table        128         13      0.433      0.692       0.58       0.48\n",
             "                toilet        128          2      0.508        0.5      0.745      0.721\n",
-            "                    tv        128          2       0.55      0.649      0.828      0.762\n",
-            "                laptop        128          3          1          0      0.741      0.653\n",
-            "                 mouse        128          2          1          0     0.0454    0.00907\n",
-            "                remote        128          8       0.83        0.5      0.569      0.449\n",
-            "            cell phone        128          8          0          0     0.0819     0.0266\n",
-            "             microwave        128          3      0.475      0.667       0.83      0.699\n",
-            "                  oven        128          5        0.5        0.4      0.348      0.275\n",
-            "                  sink        128          6      0.354      0.187      0.368      0.217\n",
-            "          refrigerator        128          5      0.518        0.4      0.729      0.571\n",
-            "                  book        128         29      0.583      0.241      0.396      0.204\n",
-            "                 clock        128          9      0.891      0.889       0.91      0.773\n",
-            "                  vase        128          2      0.506          1      0.828      0.745\n",
+            "                    tv        128          2      0.563      0.689      0.828      0.762\n",
+            "                laptop        128          3          1      0.455      0.747      0.657\n",
+            "                 mouse        128          2          1          0     0.0405     0.0081\n",
+            "                remote        128          8      0.832        0.5      0.574      0.445\n",
+            "            cell phone        128          8          0          0     0.0724     0.0315\n",
+            "             microwave        128          3      0.496      0.667      0.806      0.685\n",
+            "                  oven        128          5      0.501        0.4      0.345      0.273\n",
+            "                  sink        128          6      0.379      0.212      0.366       0.21\n",
+            "          refrigerator        128          5      0.612        0.4       0.77      0.608\n",
+            "                  book        128         29      0.553      0.207      0.387        0.2\n",
+            "                 clock        128          9       0.88      0.889      0.907      0.772\n",
+            "                  vase        128          2      0.508          1      0.828      0.745\n",
             "              scissors        128          1          1          0      0.142     0.0426\n",
-            "            teddy bear        128         21      0.587      0.476       0.63      0.458\n",
-            "            toothbrush        128          5      0.784      0.736      0.898      0.544\n",
-            "Speed: 2.0ms preprocess, 4.0ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
+            "            teddy bear        128         21      0.662      0.476      0.603      0.442\n",
+            "            toothbrush        128          5      0.792      0.768      0.898      0.574\n",
+            "Speed: 1.1ms preprocess, 5.4ms inference, 0.0ms loss, 2.9ms postprocess per image\n",
             "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
           ]
         }
@@ -479,26 +485,26 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "CYIjW4igCjqD",
-        "outputId": "69cab2fb-cbfa-4acf-8e29-9c4fb6f4a38f"
+        "outputId": "49b5bb9d-2c16-415b-c3e7-ec95c15a9e62"
       },
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CPU\n",
+            "Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
             "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
             "\n",
-            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with output shape (1, 84, 8400) (6.2 MB)\n",
+            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
             "\n",
             "\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 1.13.1+cu116...\n",
-            "\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.7s, saved as yolov8n.torchscript (12.4 MB)\n",
+            "\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.9s, saved as yolov8n.torchscript (12.4 MB)\n",
             "\n",
-            "Export complete (2.2s)\n",
+            "Export complete (2.6s)\n",
             "Results saved to \u001b[1m/content\u001b[0m\n",
-            "Predict:         yolo task=detect mode=predict model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
-            "Validate:        yolo task=detect mode=val model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
+            "Predict:         yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
+            "Validate:        yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",
             "Visualize:       https://netron.app\n"
           ]
         }

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.57'
+__version__ = '8.0.58'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks

ultralytics/nn/autobackend.py

@@ -25,8 +25,8 @@ def check_class_names(names):
     if isinstance(names, list):  # names is a list
         names = dict(enumerate(names))  # convert to dict
     if isinstance(names, dict):
-        if not all(isinstance(k, int) for k in names.keys()):  # convert string keys to int, i.e. '0' to 0
-            names = {int(k): v for k, v in names.items()}
+        # convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
+        names = {int(k): str(v) for k, v in names.items()}
         n = len(names)
         if max(names.keys()) >= n:
             raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '

ultralytics/nn/tasks.py

@@ -245,7 +245,7 @@ class SegmentationModel(DetectionModel):
         super().__init__(cfg, ch, nc, verbose)
 
     def _forward_augment(self, x):
-        raise NotImplementedError('WARNING ⚠️ SegmentationModel has not supported augment inference yet!')
+        raise NotImplementedError(emojis('WARNING ⚠️ SegmentationModel has not supported augment inference yet!'))
 
 
 class ClassificationModel(BaseModel):

ultralytics/tracker/track.py

@@ -3,9 +3,7 @@
 import torch
 
 from ultralytics.yolo.utils import IterableSimpleNamespace, yaml_load
-from ultralytics.yolo.utils.checks import check_requirements, check_yaml
-
-check_requirements('lap')  # for linear_assignment
+from ultralytics.yolo.utils.checks import check_yaml
 
 from .trackers import BOTSORT, BYTETracker
 

ultralytics/tracker/utils/matching.py

@@ -1,12 +1,20 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import lap
 import numpy as np
 import scipy
 from scipy.spatial.distance import cdist
 
 from .kalman_filter import chi2inv95
 
+try:
+    import lap  # for linear_assignment
+    assert lap.__version__  # verify package is not directory
+except (ImportError, AssertionError, AttributeError):
+    from ultralytics.yolo.utils.checks import check_requirements
+
+    check_requirements('lap>=0.4')  # install
+    import lap
+
 
 def merge_matches(m1, m2, shape):
     O, P, Q = shape

ultralytics/yolo/data/base.py

@@ -12,8 +12,8 @@ import numpy as np
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from ..utils import NUM_THREADS, TQDM_BAR_FORMAT
-from .utils import HELP_URL, IMG_FORMATS, LOCAL_RANK
+from ..utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT
+from .utils import HELP_URL, IMG_FORMATS
 
 
 class BaseDataset(Dataset):

ultralytics/yolo/data/build.py

@@ -14,10 +14,10 @@ from ultralytics.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImage
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.yolo.utils.checks import check_file
 
-from ..utils import LOGGER, colorstr
+from ..utils import LOGGER, RANK, colorstr
 from ..utils.torch_utils import torch_distributed_zero_first
 from .dataset import ClassificationDataset, YOLODataset
-from .utils import PIN_MEMORY, RANK
+from .utils import PIN_MEMORY
 
 
 class InfiniteDataLoader(dataloader.DataLoader):

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -335,6 +335,7 @@ class LoadTensor:
     def __init__(self, imgs) -> None:
         self.im0 = imgs
         self.bs = imgs.shape[0]
+        self.mode = 'image'
 
     def __iter__(self):
         self.count = 0
@@ -346,6 +347,9 @@ class LoadTensor:
         self.count += 1
         return None, self.im0, self.im0, None, ''  # self.paths, im, self.im0, None, ''
 
+    def __len__(self):
+        return self.bs
+
 
 def autocast_list(source):
     """

ultralytics/yolo/data/dataset.py

@@ -10,10 +10,10 @@ import torch
 import torchvision
 from tqdm import tqdm
 
-from ..utils import NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
+from ..utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
 from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
 from .base import BaseDataset
-from .utils import HELP_URL, LOCAL_RANK, LOGGER, get_hash, img2label_paths, verify_image_label
+from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image_label
 
 
 class YOLODataset(BaseDataset):

ultralytics/yolo/data/utils.py

@@ -25,8 +25,6 @@ from ultralytics.yolo.utils.ops import segments2boxes
 HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm'  # video suffixes
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
 PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
 IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
 IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation

ultralytics/yolo/engine/model.py

@@ -2,6 +2,7 @@
 
 import sys
 from pathlib import Path
+from typing import Union
 
 from ultralytics import yolo  # noqa
 from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight,
@@ -67,7 +68,7 @@ class YOLO:
         list(ultralytics.yolo.engine.results.Results): The prediction results.
     """
 
-    def __init__(self, model='yolov8n.pt', task=None, session=None) -> None:
+    def __init__(self, model: Union[str, Path] = 'yolov8n.pt', task=None, session=None) -> None:
         """
         Initializes the YOLO model.
 
@@ -87,6 +88,7 @@ class YOLO:
         self.session = session  # HUB session
 
         # Load or create new YOLO model
+        model = str(model).strip()  # strip spaces
         suffix = Path(model).suffix
         if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
             model, suffix = Path(model).with_suffix('.pt'), '.pt'  # add suffix, i.e. yolov8n -> yolov8n.pt

ultralytics/yolo/engine/predictor.py

@@ -42,6 +42,18 @@ from ultralytics.yolo.utils.checks import check_imgsz, check_imshow
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
 
+STREAM_WARNING = """
+    WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
+    causing potential out-of-memory errors for large sources or long-running streams/videos.
+
+    Usage:
+        results = model(source=..., stream=True)  # generator of Results objects
+        for r in results:
+            boxes = r.boxes  # Boxes object for bbox outputs
+            masks = r.masks  # Masks object for segment masks outputs
+            probs = r.probs  # Class probabilities for classification outputs
+"""
+
 
 class BasePredictor:
     """
@@ -108,6 +120,7 @@ class BasePredictor:
         return preds
 
     def __call__(self, source=None, model=None, stream=False):
+        self.stream = stream
         if stream:
             return self.stream_inference(source, model)
         else:
@@ -132,6 +145,10 @@ class BasePredictor:
                                              stride=self.model.stride,
                                              auto=self.model.pt)
         self.source_type = self.dataset.source_type
+        if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or  # streams
+                                                  len(self.dataset) > 1000 or  # images
+                                                  any(getattr(self.dataset, 'video_flag', [False]))):  # videos
+            LOGGER.warning(STREAM_WARNING)
         self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs
 
     @smart_inference_mode()

ultralytics/yolo/engine/results.py

@@ -5,7 +5,6 @@ Ultralytics Results, Boxes and Masks classes for handling inference results
 Usage: See https://docs.ultralytics.com/modes/predict/
 """
 
-import pprint
 from copy import deepcopy
 from functools import lru_cache
 
@@ -13,11 +12,11 @@ import numpy as np
 import torch
 import torchvision.transforms.functional as F
 
-from ultralytics.yolo.utils import LOGGER, ops
+from ultralytics.yolo.utils import LOGGER, SimpleClass, ops
 from ultralytics.yolo.utils.plotting import Annotator, colors
 
 
-class Results:
+class Results(SimpleClass):
     """
     A class for storing and manipulating inference results.
 
@@ -96,17 +95,6 @@ class Results:
         for k in self.keys:
             return len(getattr(self, k))
 
-    def __str__(self):
-        attr = {k: v for k, v in vars(self).items() if not isinstance(v, type(self))}
-        return pprint.pformat(attr, indent=2, width=120, depth=10, compact=True)
-
-    def __repr__(self):
-        return self.__str__()
-
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     @property
     def keys(self):
         return [k for k in self._keys if getattr(self, k) is not None]
@@ -153,7 +141,7 @@ class Results:
         return np.asarray(annotator.im) if annotator.pil else annotator.im
 
 
-class Boxes:
+class Boxes(SimpleClass):
     """
     A class for storing and manipulating detection boxes.
 
@@ -242,15 +230,6 @@ class Boxes:
 
     def pandas(self):
         LOGGER.info('results.pandas() method not yet implemented')
-        '''
-        new = copy(self)  # return copy
-        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
-        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
-        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
-            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
-        return new
-        '''
 
     @property
     def shape(self):
@@ -263,25 +242,11 @@ class Boxes:
     def __len__(self):  # override len(results)
         return len(self.boxes)
 
-    def __str__(self):
-        return self.boxes.__str__()
-
-    def __repr__(self):
-        return (f'{self.__class__.__module__}.{self.__class__.__name__}\n'
-                f'type:  {self.boxes.__class__.__module__}.{self.boxes.__class__.__name__}\n'
-                f'shape: {self.boxes.shape}\n'
-                f'dtype: {self.boxes.dtype}\n'
-                f'{self.boxes.__repr__()}')
-
     def __getitem__(self, idx):
         return Boxes(self.boxes[idx], self.orig_shape)
 
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
 
-
-class Masks:
+class Masks(SimpleClass):
     """
     A class for storing and manipulating detection masks.
 
@@ -301,11 +266,6 @@ class Masks:
         numpy(): Returns a copy of the masks tensor as a numpy array.
         cuda(): Returns a copy of the masks tensor on GPU memory.
         to(): Returns a copy of the masks tensor with the specified device and dtype.
-        __len__(): Returns the number of masks in the tensor.
-        __str__(): Returns a string representation of the masks tensor.
-        __repr__(): Returns a detailed string representation of the masks tensor.
-        __getitem__(): Returns a new Masks object with the masks at the specified index.
-        __getattr__(): Raises an AttributeError with a list of valid attributes and properties.
     """
 
     def __init__(self, masks, orig_shape) -> None:
@@ -342,19 +302,5 @@ class Masks:
     def __len__(self):  # override len(results)
         return len(self.masks)
 
-    def __str__(self):
-        return self.masks.__str__()
-
-    def __repr__(self):
-        return (f'{self.__class__.__module__}.{self.__class__.__name__}\n'
-                f'type:  {self.masks.__class__.__module__}.{self.masks.__class__.__name__}\n'
-                f'shape: {self.masks.shape}\n'
-                f'dtype: {self.masks.dtype}\n'
-                f'{self.masks.__repr__()}')
-
     def __getitem__(self, idx):
         return Masks(self.masks[idx], self.orig_shape)
-
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

ultralytics/yolo/engine/trainer.py

@@ -174,7 +174,12 @@ class BaseTrainer:
 
         # Run subprocess if DDP training, else train normally
         if world_size > 1 and 'LOCAL_RANK' not in os.environ:
-            cmd, file = generate_ddp_command(world_size, self)  # security vulnerability in Snyk scans
+            # Argument checks
+            if self.args.rect:
+                LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with Multi-GPU training, setting rect=False")
+                self.args.rect = False
+            # Command
+            cmd, file = generate_ddp_command(world_size, self)
             try:
                 LOGGER.info(f'Running DDP command {cmd}')
                 subprocess.run(cmd, check=True)
@@ -183,17 +188,15 @@ class BaseTrainer:
             finally:
                 ddp_cleanup(self, str(file))
         else:
-            self._do_train(RANK, world_size)
+            self._do_train(world_size)
 
-    def _setup_ddp(self, rank, world_size):
-        # os.environ['MASTER_ADDR'] = 'localhost'
-        # os.environ['MASTER_PORT'] = '9020'
-        torch.cuda.set_device(rank)
-        self.device = torch.device('cuda', rank)
-        LOGGER.info(f'DDP settings: RANK {rank}, WORLD_SIZE {world_size}, DEVICE {self.device}')
-        dist.init_process_group('nccl' if dist.is_nccl_available() else 'gloo', rank=rank, world_size=world_size)
+    def _setup_ddp(self, world_size):
+        torch.cuda.set_device(RANK)
+        self.device = torch.device('cuda', RANK)
+        LOGGER.info(f'DDP settings: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
+        dist.init_process_group('nccl' if dist.is_nccl_available() else 'gloo', rank=RANK, world_size=world_size)
 
-    def _setup_train(self, rank, world_size):
+    def _setup_train(self, world_size):
         """
         Builds dataloaders and optimizer on correct rank process.
         """
@@ -213,7 +216,7 @@ class BaseTrainer:
         self.amp = bool(self.amp)  # as boolean
         self.scaler = amp.GradScaler(enabled=self.amp)
         if world_size > 1:
-            self.model = DDP(self.model, device_ids=[rank])
+            self.model = DDP(self.model, device_ids=[RANK])
         # Check imgsz
         gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32)  # grid size (max stride)
         self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
@@ -243,8 +246,8 @@ class BaseTrainer:
 
         # dataloaders
         batch_size = self.batch_size // world_size if world_size > 1 else self.batch_size
-        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=rank, mode='train')
-        if rank in (-1, 0):
+        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode='train')
+        if RANK in (-1, 0):
             self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode='val')
             self.validator = self.get_validator()
             metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
@@ -256,11 +259,11 @@ class BaseTrainer:
         self.scheduler.last_epoch = self.start_epoch - 1  # do not move
         self.run_callbacks('on_pretrain_routine_end')
 
-    def _do_train(self, rank=-1, world_size=1):
+    def _do_train(self, world_size=1):
         if world_size > 1:
-            self._setup_ddp(rank, world_size)
+            self._setup_ddp(world_size)
 
-        self._setup_train(rank, world_size)
+        self._setup_train(world_size)
 
         self.epoch_time = None
         self.epoch_time_start = time.time()
@@ -280,7 +283,7 @@ class BaseTrainer:
             self.epoch = epoch
             self.run_callbacks('on_train_epoch_start')
             self.model.train()
-            if rank != -1:
+            if RANK != -1:
                 self.train_loader.sampler.set_epoch(epoch)
             pbar = enumerate(self.train_loader)
             # Update dataloader attributes (optional)
@@ -291,7 +294,7 @@ class BaseTrainer:
                 if hasattr(self.train_loader.dataset, 'close_mosaic'):
                     self.train_loader.dataset.close_mosaic(hyp=self.args)
 
-            if rank in (-1, 0):
+            if RANK in (-1, 0):
                 LOGGER.info(self.progress_string())
                 pbar = tqdm(enumerate(self.train_loader), total=nb, bar_format=TQDM_BAR_FORMAT)
             self.tloss = None
@@ -315,7 +318,7 @@ class BaseTrainer:
                     batch = self.preprocess_batch(batch)
                     preds = self.model(batch['img'])
                     self.loss, self.loss_items = self.criterion(preds, batch)
-                    if rank != -1:
+                    if RANK != -1:
                         self.loss *= world_size
                     self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
                         else self.loss_items
@@ -332,7 +335,7 @@ class BaseTrainer:
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                 loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
                 losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
-                if rank in (-1, 0):
+                if RANK in (-1, 0):
                     pbar.set_description(
                         ('%11s' * 2 + '%11.4g' * (2 + loss_len)) %
                         (f'{epoch + 1}/{self.epochs}', mem, *losses, batch['cls'].shape[0], batch['img'].shape[-1]))
@@ -347,7 +350,7 @@ class BaseTrainer:
             self.scheduler.step()
             self.run_callbacks('on_train_epoch_end')
 
-            if rank in (-1, 0):
+            if RANK in (-1, 0):
 
                 # Validation
                 self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
@@ -377,7 +380,7 @@ class BaseTrainer:
             if self.stop:
                 break  # must break all DDP ranks
 
-        if rank in (-1, 0):
+        if RANK in (-1, 0):
             # Do final val with best.pt
             LOGGER.info(f'\n{epoch - self.start_epoch + 1} epochs completed in '
                         f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
@@ -408,7 +411,8 @@ class BaseTrainer:
             torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt')
         del ckpt
 
-    def get_dataset(self, data):
+    @staticmethod
+    def get_dataset(data):
         """
         Get train, val path from data dict if it exists. Returns None if data format is not recognized.
         """

ultralytics/yolo/utils/__init__.py

@@ -22,11 +22,15 @@ import yaml
 
 from ultralytics import __version__
 
-# Constants
+# PyTorch Multi-GPU DDP Constants
+RANK = int(os.getenv('RANK', -1))
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
+
+# Other Constants
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[2]  # YOLO
 DEFAULT_CFG_PATH = ROOT / 'yolo/cfg/default.yaml'
-RANK = int(os.getenv('RANK', -1))
 NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
 AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
 VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true'  # global verbose mode
@@ -92,25 +96,59 @@ HELP_MSG = \
     """
 
 # Settings
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
+torch.set_printoptions(linewidth=320, precision=4, profile='default')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
 cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
 os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
 os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'  # for deterministic training
 
 
+class SimpleClass:
+    """
+    Ultralytics SimpleClass is a base class providing helpful string representation, error reporting, and attribute
+    access methods for easier debugging and usage.
+    """
+
+    def __str__(self):
+        """Return a human-readable string representation of the object."""
+        attr = []
+        for a in dir(self):
+            v = getattr(self, a)
+            if not callable(v) and not a.startswith('__'):
+                if isinstance(v, SimpleClass):
+                    # Display only the module and class name for subclasses
+                    s = f'{a}: {v.__module__}.{v.__class__.__name__} object'
+                else:
+                    s = f'{a}: {repr(v)}'
+                attr.append(s)
+        return f'{self.__module__}.{self.__class__.__name__} object with attributes:\n\n' + '\n'.join(attr)
+
+    def __repr__(self):
+        """Return a machine-readable string representation of the object."""
+        return self.__str__()
+
+    def __getattr__(self, attr):
+        """Custom attribute access error message with helpful information."""
+        name = self.__class__.__name__
+        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
+
+
 class IterableSimpleNamespace(SimpleNamespace):
     """
-    Iterable SimpleNamespace class to allow SimpleNamespace to be used with dict() and in for loops
+    Ultralytics IterableSimpleNamespace is an extension class of SimpleNamespace that adds iterable functionality and
+    enables usage with dict() and for loops.
     """
 
     def __iter__(self):
+        """Return an iterator of key-value pairs from the namespace's attributes."""
         return iter(vars(self).items())
 
     def __str__(self):
+        """Return a human-readable string representation of the object."""
         return '\n'.join(f'{k}={v}' for k, v in vars(self).items())
 
     def __getattr__(self, attr):
+        """Custom attribute access error message with helpful information."""
         name = self.__class__.__name__
         raise AttributeError(f"""
             '{name}' object has no attribute '{attr}'. This may be caused by a modified or out of date ultralytics
@@ -120,6 +158,7 @@ class IterableSimpleNamespace(SimpleNamespace):
             """)
 
     def get(self, key, default=None):
+        """Return the value of the specified key if it exists; otherwise, return the default value."""
         return getattr(self, key, default)
 
 

ultralytics/yolo/utils/callbacks/clearml.py

@@ -8,7 +8,7 @@ try:
 
     assert clearml.__version__  # verify package is not directory
     assert not TESTS_RUNNING  # do not log pytest
-except (ImportError, AssertionError):
+except (ImportError, AssertionError, AttributeError):
     clearml = None
 
 

ultralytics/yolo/utils/callbacks/comet.py

@@ -7,7 +7,7 @@ try:
 
     assert not TESTS_RUNNING  # do not log pytest
     assert comet_ml.__version__  # verify package is not directory
-except (ImportError, AssertionError):
+except (ImportError, AssertionError, AttributeError):
     comet_ml = None
 
 

ultralytics/yolo/utils/checks.py

@@ -239,7 +239,7 @@ def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
         if isinstance(suffix, str):
             suffix = (suffix, )
         for f in file if isinstance(file, (list, tuple)) else [file]:
-            s = Path(f).suffix.lower()  # file suffix
+            s = Path(f).suffix.lower().strip()  # file suffix
             if len(s):
                 assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}, not {s}'
 
@@ -261,7 +261,7 @@ def check_yolov5u_filename(file: str, verbose: bool = True):
 def check_file(file, suffix='', download=True, hard=True):
     # Search/download file (if necessary) and return path
     check_suffix(file, suffix)  # optional
-    file = str(file)  # convert to string
+    file = str(file).strip()  # convert to string and strip spaces
     file = check_yolov5u_filename(file)  # yolov5n -> yolov5nu
     if not file or ('://' not in file and Path(file).exists()):  # exists ('://' check required in Windows Python<3.10)
         return file

ultralytics/yolo/utils/metrics.py

@@ -11,7 +11,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from ultralytics.yolo.utils import LOGGER, TryExcept
+from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept
 
 
 # boxes
@@ -425,7 +425,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=Path(), na
     return tp, fp, p, r, f1, ap, unique_classes.astype(int)
 
 
-class Metric:
+class Metric(SimpleClass):
     """
         Class for computing evaluation metrics for YOLOv8 model.
 
@@ -461,10 +461,6 @@ class Metric:
         self.ap_class_index = []  # (nc, )
         self.nc = 0
 
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     @property
     def ap50(self):
         """AP@0.5 of all classes.
@@ -550,7 +546,7 @@ class Metric:
         self.p, self.r, self.f1, self.all_ap, self.ap_class_index = results
 
 
-class DetMetrics:
+class DetMetrics(SimpleClass):
     """
     This class is a utility class for computing detection metrics such as precision, recall, and mean average precision
     (mAP) of an object detection model.
@@ -585,10 +581,6 @@ class DetMetrics:
         self.box = Metric()
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     def process(self, tp, conf, pred_cls, target_cls):
         results = ap_per_class(tp, conf, pred_cls, target_cls, plot=self.plot, save_dir=self.save_dir,
                                names=self.names)[2:]
@@ -622,7 +614,7 @@ class DetMetrics:
         return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
 
 
-class SegmentMetrics:
+class SegmentMetrics(SimpleClass):
     """
     Calculates and aggregates detection and segmentation metrics over a given set of classes.
 
@@ -657,10 +649,6 @@ class SegmentMetrics:
         self.seg = Metric()
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     def process(self, tp_m, tp_b, conf, pred_cls, target_cls):
         """
         Processes the detection and segmentation metrics over the given set of predictions.
@@ -724,7 +712,7 @@ class SegmentMetrics:
         return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
 
 
-class ClassifyMetrics:
+class ClassifyMetrics(SimpleClass):
     """
     Class for computing classification metrics including top-1 and top-5 accuracy.
 
@@ -747,10 +735,6 @@ class ClassifyMetrics:
         self.top5 = 0
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
-    def __getattr__(self, attr):
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     def process(self, targets, pred):
         # target classes and predicted classes
         pred, targets = torch.cat(pred), torch.cat(targets)

ultralytics/yolo/utils/plotting.py

@@ -295,7 +295,7 @@ def plot_images(images,
             for j, box in enumerate(boxes.T.tolist()):
                 c = classes[j]
                 color = colors(c)
-                c = names[c] if names else c
+                c = names.get(c, c) if names else c
                 if labels or conf[j] > 0.25:  # 0.25 conf thresh
                     label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
                     annotator.box_label(box, label, color=color)

ultralytics/yolo/utils/torch_utils.py

@@ -8,6 +8,7 @@ import time
 from contextlib import contextmanager
 from copy import deepcopy
 from pathlib import Path
+from typing import Union
 
 import numpy as np
 import thop
@@ -15,15 +16,10 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.nn.parallel import DistributedDataParallel as DDP
 
-from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, __version__
+from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, __version__
 from ultralytics.yolo.utils.checks import check_version
 
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-
 TORCH_1_9 = check_version(torch.__version__, '1.9.0')
 TORCH_1_11 = check_version(torch.__version__, '1.11.0')
 TORCH_1_12 = check_version(torch.__version__, '1.12.0')
@@ -49,17 +45,6 @@ def smart_inference_mode():
     return decorate
 
 
-def DDP_model(model):
-    # Model DDP creation with checks
-    assert not check_version(torch.__version__, '1.12.0', pinned=True), \
-        'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
-        'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
-    if TORCH_1_11:
-        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
-    else:
-        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
-
-
 def select_device(device='', batch=0, newline=False, verbose=True):
     # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
     s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
@@ -141,6 +126,7 @@ def fuse_conv_and_bn(conv, bn):
 
 
 def fuse_deconv_and_bn(deconv, bn):
+    # Fuse ConvTranspose2d() and BatchNorm2d() layers
     fuseddconv = nn.ConvTranspose2d(deconv.in_channels,
                                     deconv.out_channels,
                                     kernel_size=deconv.kernel_size,
@@ -186,14 +172,17 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
 
 
 def get_num_params(model):
+    # Return the total number of parameters in a YOLO model
     return sum(x.numel() for x in model.parameters())
 
 
 def get_num_gradients(model):
+    # Return the total number of parameters with gradients in a YOLO model
     return sum(x.numel() for x in model.parameters() if x.requires_grad)
 
 
 def get_flops(model, imgsz=640):
+    # Return a YOLO model's FLOPs
     try:
         model = de_parallel(model)
         p = next(model.parameters())
@@ -208,6 +197,7 @@ def get_flops(model, imgsz=640):
 
 
 def initialize_weights(model):
+    # Initialize model weights to random values
     for m in model.modules():
         t = type(m)
         if t is nn.Conv2d:
@@ -239,7 +229,7 @@ def make_divisible(x, divisor):
 
 
 def copy_attr(a, b, include=(), exclude=()):
-    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    # Copy attributes from 'b' to 'a', options to only include [...] and to exclude [...]
     for k, v in b.__dict__.items():
         if (len(include) and k not in include) or k.startswith('_') or k in exclude:
             continue
@@ -322,7 +312,7 @@ class ModelEMA:
             copy_attr(self.ema, model, include, exclude)
 
 
-def strip_optimizer(f='best.pt', s=''):
+def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
     """
     Strip optimizer from 'f' to finalize training, optionally save as 's'.
 

ultralytics/yolo/v8/segment/train.py

@@ -126,11 +126,11 @@ class SegLoss(Loss):
 
                 # WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
                 else:
-                    loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
+                    loss[1] += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss
 
         # WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
         else:
-            loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
+            loss[1] += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss
 
         loss[0] *= self.hyp.box  # box gain
         loss[1] *= self.hyp.box / batch_size  # seg gain