From f79bed1b36d54b93c324e2278fe63ba908363dc6 Mon Sep 17 00:00:00 2001 From: Laughing-q <1185102784@qq.com> Date: Sun, 3 Mar 2024 20:16:23 +0800 Subject: [PATCH] rename --- docs/en/models/yolov9.md | 26 +++++++++---------- .../models/v9/{yolov9-c.yaml => yolov9c.yaml} | 0 .../models/v9/{yolov9-e.yaml => yolov9e.yaml} | 0 ultralytics/utils/downloads.py | 1 + 4 files changed, 14 insertions(+), 13 deletions(-) rename ultralytics/cfg/models/v9/{yolov9-c.yaml => yolov9c.yaml} (100%) rename ultralytics/cfg/models/v9/{yolov9-e.yaml => yolov9e.yaml} (100%) diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md index adc578f0c..3252c1d0b 100644 --- a/docs/en/models/yolov9.md +++ b/docs/en/models/yolov9.md @@ -94,11 +94,11 @@ This example provides simple YOLOv9 training and inference examples. For full do ```python from ultralytics import YOLO - # Build a YOLOv9-c model from scratch - model = YOLO('yolov9-c.yaml') + # Build a YOLOv9c model from scratch + model = YOLO('yolov9c.yaml') - # Build a YOLOv9-c model from pretrained weight - model = YOLO('yolov9-c.pt') + # Build a YOLOv9c model from pretrained weight + model = YOLO('yolov9c.pt') # Display model information (optional) model.info() @@ -106,7 +106,7 @@ This example provides simple YOLOv9 training and inference examples. For full do # Train the model on the COCO8 example dataset for 100 epochs results = model.train(data='coco8.yaml', epochs=100, imgsz=640) - # Run inference with the YOLOv9-c model on the 'bus.jpg' image + # Run inference with the YOLOv9c model on the 'bus.jpg' image results = model('path/to/bus.jpg') ``` @@ -115,21 +115,21 @@ This example provides simple YOLOv9 training and inference examples. For full do CLI commands are available to directly run the models: ```bash - # Build a YOLOv9-c model from scratch and train it on the COCO8 example dataset for 100 epochs - yolo train model=yolov9-c.yaml data=coco8.yaml epochs=100 imgsz=640 + # Build a YOLOv9c model from scratch and train it on the COCO8 example dataset for 100 epochs + yolo train model=yolov9c.yaml data=coco8.yaml epochs=100 imgsz=640 - # Build a YOLOv9-c model from scratch and run inference on the 'bus.jpg' image - yolo predict model=yolov9-c.yaml source=path/to/bus.jpg + # Build a YOLOv9c model from scratch and run inference on the 'bus.jpg' image + yolo predict model=yolov9c.yaml source=path/to/bus.jpg ``` ## Supported Tasks and Modes The YOLOv9 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications. -| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | -|------------|------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------| -| YOLOv9-C | [yolov9-c.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9-c.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv9-E | [yolov9-e.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9-e.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | +|------------|-----------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------| +| YOLOv9-C | [yolov9c.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9c.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv9-E | [yolov9e.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9e.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | This table provides a detailed overview of the YOLOv9 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv9 models in a broad range of object detection scenarios. diff --git a/ultralytics/cfg/models/v9/yolov9-c.yaml b/ultralytics/cfg/models/v9/yolov9c.yaml similarity index 100% rename from ultralytics/cfg/models/v9/yolov9-c.yaml rename to ultralytics/cfg/models/v9/yolov9c.yaml diff --git a/ultralytics/cfg/models/v9/yolov9-e.yaml b/ultralytics/cfg/models/v9/yolov9e.yaml similarity index 100% rename from ultralytics/cfg/models/v9/yolov9-e.yaml rename to ultralytics/cfg/models/v9/yolov9e.yaml diff --git a/ultralytics/utils/downloads.py b/ultralytics/utils/downloads.py index 213145624..9b2f33a25 100644 --- a/ultralytics/utils/downloads.py +++ b/ultralytics/utils/downloads.py @@ -21,6 +21,7 @@ GITHUB_ASSETS_NAMES = ( + [f"yolov5{k}{resolution}u.pt" for k in "nsmlx" for resolution in ("", "6")] + [f"yolov3{k}u.pt" for k in ("", "-spp", "-tiny")] + [f"yolov8{k}-world.pt" for k in "sml"] + + [f"yolov9{k}.pt" for k in "ce"] + [f"yolo_nas_{k}.pt" for k in "sml"] + [f"sam_{k}.pt" for k in "bl"] + [f"FastSAM-{k}.pt" for k in "sx"]