Optimize Docs images (#15900)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar
GitHub
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@ -19,7 +19,7 @@ pip install -r requirements.txt # install
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## Train On Custom Data
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<a href="https://ultralytics.com/hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png" alt="Ultralytics active learning"></a>
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<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-active-learning-loop.avif" alt="Ultralytics active learning"></a>
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<br>
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<br>
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@ -48,7 +48,7 @@ If this is not possible, you can start from [a public dataset](https://universe.
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Once you have collected images, you will need to annotate the objects of interest to create a ground truth for your model to learn from.
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<p align="center"><a href="https://app.roboflow.com/?model=yolov5&ref=ultralytics" title="Create a Free Roboflow Account"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif" alt="YOLOv5 accuracies"></a></p>
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<p align="center"><a href="https://app.roboflow.com/?model=yolov5&ref=ultralytics" title="Create a Free Roboflow Account"><img width="450" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-annotate.avif" alt="YOLOv5 accuracies"></a></p>
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[Roboflow Annotate](https://roboflow.com/annotate?ref=ultralytics) is a simple web-based tool for managing and labeling your images with your team and exporting them in [YOLOv5's annotation format](https://roboflow.com/formats/yolov5-pytorch-txt?ref=ultralytics).
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@ -60,18 +60,18 @@ Whether you [label your images with Roboflow](https://roboflow.com/annotate?ref=
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Note: YOLOv5 does online augmentation during training, so we do not recommend applying any augmentation steps in Roboflow for training with YOLOv5. But we recommend applying the following preprocessing steps:
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<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273477fccf42a0fd3d6_roboflow-preprocessing.png" alt="Recommended Preprocessing Steps"></p>
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<p align="center"><img width="450" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-preprocessing-steps.avif" alt="Recommended Preprocessing Steps"></p>
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- **Auto-Orient** - to strip EXIF orientation from your images.
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- **Resize (Stretch)** - to the square input size of your model (640x640 is the YOLOv5 default).
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Generating a version will give you a snapshot of your dataset, so you can always go back and compare your future model training runs against it, even if you add more images or change its configuration later.
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<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a2733fd1da943619934e_roboflow-export.png" alt="Export in YOLOv5 Format"></p>
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<p align="center"><img width="450" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-export.avif" alt="Export in YOLOv5 Format"></p>
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Export in `YOLOv5 Pytorch` format, then copy the snippet into your training script or notebook to download your dataset.
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<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273a92e4f5cb72594df_roboflow-snippet.png" alt="Roboflow dataset download snippet"></p>
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<p align="center"><img width="450" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-dataset-download-snippet.avif" alt="Roboflow dataset download snippet"></p>
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## Option 2: Create a Manual Dataset
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@ -106,11 +106,11 @@ After using an annotation tool to label your images, export your labels to **YOL
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- Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height.
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- Class numbers are zero-indexed (start from 0).
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<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg" alt="Roboflow annotations"></p>
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<p align="center"><img width="750" src="https://github.com/ultralytics/docs/releases/download/0/two-persons-tie.avif" alt="Roboflow annotations"></p>
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The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
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<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png" alt="Roboflow dataset preprocessing"></p>
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<p align="center"><img width="428" src="https://github.com/ultralytics/docs/releases/download/0/two-persons-tie-1.avif" alt="Roboflow dataset preprocessing"></p>
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### 2.3 Organize Directories
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@ -121,13 +121,13 @@ Organize your train and val images and labels according to the example below. YO
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../datasets/coco128/labels/im0.txt # label
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```
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<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png" alt="YOLOv5 dataset structure"></p>
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<p align="center"><img width="700" src="https://github.com/ultralytics/docs/releases/download/0/yolov5-dataset-structure.avif" alt="YOLOv5 dataset structure"></p>
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## 3. Select a Model
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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.
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<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
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<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/docs/releases/download/0/yolov5-model-comparison.avif"></p>
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## 4. Train
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@ -163,7 +163,7 @@ python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt #
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To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](./comet_logging_integration.md). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook: [](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
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<img width="1920" alt="YOLO UI" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
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<img width="1920" alt="YOLO UI" src="https://github.com/ultralytics/docs/releases/download/0/yolo-ui.avif">
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### ClearML Logging and Automation 🌟 NEW
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@ -177,7 +177,7 @@ You'll get all the great expected features from an experiment manager: live upda
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You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](./clearml_logging_integration.md) for details!
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<a href="https://clear.ml/">
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<img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"></a>
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<img alt="ClearML Experiment Management UI" src="https://github.com/ultralytics/docs/releases/download/0/clearml-experiment-management-ui.avif" width="1280"></a>
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### Local Logging
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@ -185,7 +185,7 @@ Training results are automatically logged with [Tensorboard](https://www.tensorf
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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.
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<img alt="Local logging results" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/image-local_logging.jpg" width="1280">
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<img alt="Local logging results" src="https://github.com/ultralytics/docs/releases/download/0/local-logging-results.avif" width="1280">
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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:
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@ -195,7 +195,7 @@ from utils.plots import plot_results
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plot_results("path/to/results.csv") # plot 'results.csv' as 'results.png'
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```
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<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/results.png"></p>
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<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/docs/releases/download/0/results.avif"></p>
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## Next Steps
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