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Ultralytics Asset URL Update (#14345)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-07-10 21:12:33 +02:00 • committed by GitHub
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2
docs/en/yolov5/tutorials/multi_gpu_training.md
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@ -24,7 +24,7 @@ pip install -r requirements.txt # install
Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models. We will train this model with Multi-GPU on the [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset.
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
### Single GPU

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docs/en/yolov5/tutorials/tips_for_best_training_results.md
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@ -28,7 +28,7 @@ We've put together a full guide for users looking to get the best results on the
Larger models like YOLOv5x and [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/tag/v5.0) will produce better results in nearly all cases, but have more parameters, require more CUDA memory to train, and are slower to run. For **mobile** deployments we recommend YOLOv5s/m, for **cloud** deployments we recommend YOLOv5l/x. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
- **Start from Pretrained weights.** Recommended for small to medium-sized datasets (i.e. [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml)). Pass the name of the model to the `--weights` argument. Models download automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).

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docs/en/yolov5/tutorials/train_custom_data.md
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@ -127,7 +127,7 @@ Organize your train and val images and labels according to the example below. YO
Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
## 4. Train
@ -185,7 +185,7 @@ Training results are automatically logged with [Tensorboard](https://www.tensorf
This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280">
<img alt="Local logging results" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/image-local_logging.jpg" width="1280">
Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:
@ -195,7 +195,7 @@ from utils.plots import plot_results
plot_results("path/to/results.csv") # plot 'results.csv' as 'results.png'
```
<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/results.png"></p>
<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/results.png"></p>
## Next Steps