Docs partial mdformat improvements (#7378)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -117,6 +117,7 @@ YOLOv5 employs various data augmentation techniques to improve the model's abili
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- **Albumentations**: A powerful library for image augmenting that supports a wide variety of augmentation techniques.
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- **HSV Augmentation**: Random changes to the Hue, Saturation, and Value of the images.
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@ -40,15 +40,15 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t
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- Install the `clearml` python package:
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```bash
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pip install clearml
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```
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```bash
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pip install clearml
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```
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- Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
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```bash
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clearml-init
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```
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```bash
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clearml-init
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```
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That's it! You're done 😎
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@ -14,8 +14,7 @@ This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readm
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Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models.
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Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)!
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Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
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Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
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## Getting Started
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@ -180,14 +179,11 @@ python train.py \
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--upload_dataset
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```
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You can find the uploaded dataset in the Artifacts tab in your Comet Workspace
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<img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
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You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
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You can preview the data directly in the Comet UI.
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<img width="1082" alt="artifact-2" src="https://user-images.githubusercontent.com/7529846/186929215-432c36a9-c109-4eb0-944b-84c2786590d6.png">
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You can preview the data directly in the Comet UI. <img width="1082" alt="artifact-2" src="https://user-images.githubusercontent.com/7529846/186929215-432c36a9-c109-4eb0-944b-84c2786590d6.png">
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Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file
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<img width="963" alt="artifact-3" src="https://user-images.githubusercontent.com/7529846/186929256-9d44d6eb-1a19-42de-889a-bcbca3018f2e.png">
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Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file <img width="963" alt="artifact-3" src="https://user-images.githubusercontent.com/7529846/186929256-9d44d6eb-1a19-42de-889a-bcbca3018f2e.png">
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### Using a saved Artifact
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@ -209,8 +205,7 @@ python train.py \
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--weights yolov5s.pt
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```
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Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset.
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<img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
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Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. <img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
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## Resuming a Training Run
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@ -7,6 +7,7 @@ keywords: YOLOv5, object detection, ensemble learning, mAP, Recall
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📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall.
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From [https://en.wikipedia.org/wiki/Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning):
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> Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction. As long as the base models are diverse and independent, the prediction error of the model decreases when the ensemble approach is used. The approach seeks the wisdom of crowds in making a prediction. Even though the ensemble model has multiple base models within the model, it acts and performs as a single model.
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## Before You Start
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@ -134,9 +134,11 @@ Visualize: https://netron.app/
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```
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The 3 exported models will be saved alongside the original PyTorch model:
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<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/122827190-57a8f880-d2e4-11eb-860e-dbb7f9fc57fb.png" alt="YOLO export locations"></p>
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[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models:
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<p align="center"><img width="850" src="https://user-images.githubusercontent.com/26833433/191003260-f94011a7-5b2e-4fe3-93c1-e1a935e0a728.png" alt="YOLO model visualization"></p>
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## Exported Model Usage Examples
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@ -175,7 +177,7 @@ python val.py --weights yolov5s.pt # PyTorch
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Use PyTorch Hub with exported YOLOv5 models:
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``` python
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```python
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import torch
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# Model
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@ -84,15 +84,15 @@ Here are some of the versions supported by JetPack 4.6 and above.
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Supported by JetPack 4.4 (L4T R32.4.3) / JetPack 4.4.1 (L4T R32.4.4) / JetPack 4.5 (L4T R32.5.0) / JetPack 4.5.1 (L4T R32.5.1) / JetPack 4.6 (L4T R32.6.1) with Python 3.6
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**file_name:** torch-1.10.0-cp36-cp36m-linux_aarch64.whl
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**URL:** [https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl](https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl)
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* **file_name:** torch-1.10.0-cp36-cp36m-linux_aarch64.whl
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* **URL:** [https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl](https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl)
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**PyTorch v1.12.0**
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Supported by JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T R34.1.1) / JetPack 5.0.2 (L4T R35.1.0) with Python 3.8
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**file_name:** torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl
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**URL:** [https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl](https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl)
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* **file_name:** torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl
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* **URL:** [https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl](https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl)
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- **Step 1.** Install torch according to your JetPack version in the following format
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@ -59,15 +59,14 @@ Once you have collected images, you will need to annotate the objects of interes
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Whether you [label your images with Roboflow](https://roboflow.com/annotate?ref=ultralytics) or not, you can use it to convert your dataset into YOLO format, create a YOLOv5 YAML configuration file, and host it for importing into your training script.
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[Create a free Roboflow account](https://app.roboflow.com/?model=yolov5&ref=ultralytics)
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and upload your dataset to a `Public` workspace, label any unannotated images, then generate and export a version of your dataset in `YOLOv5 Pytorch` format.
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[Create a free Roboflow account](https://app.roboflow.com/?model=yolov5&ref=ultralytics) and upload your dataset to a `Public` workspace, label any unannotated images, then generate and export a version of your dataset in `YOLOv5 Pytorch` format.
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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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* **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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- **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 point in time 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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@ -78,6 +77,7 @@ Export in `YOLOv5 Pytorch` format, then copy the snippet into your training scri
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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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Now continue with `2. Select a Model`.
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</details>
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<details>
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@ -170,8 +170,7 @@ export COMET_API_KEY=<Your API Key> # 2. paste API key
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python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train
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```
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To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). 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:
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[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
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To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). 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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@ -211,11 +210,11 @@ plot_results('path/to/results.csv') # plot 'results.csv' as 'results.png'
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Once your model is trained you can use your best checkpoint `best.pt` to:
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* Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference on new images and videos
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* [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
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* [Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
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* [Evolve](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) hyperparameters to improve performance
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* [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset
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- Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference on new images and videos
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- [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
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- [Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
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- [Evolve](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) hyperparameters to improve performance
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- [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset
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## Supported Environments
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