Browse Source
Add mdformat to precommit checks and update other version (#7529)
* Update .pre-commit-config.yaml * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update .pre-commit-config.yaml * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update CONTRIBUTING.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md * Update README.md * Update README.md Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>modifyDataloader
Zengyf-CVer
GitHub
2 years ago
5 changed files with 119 additions and 106 deletions
+ 12
- 12
.github/CODE_OF_CONDUCT.md
View File
| @@ -17,23 +17,23 @@ diverse, inclusive, and healthy community. | |||
| Examples of behavior that contributes to a positive environment for our | |||
| community include: | |||
| * Demonstrating empathy and kindness toward other people | |||
| * Being respectful of differing opinions, viewpoints, and experiences | |||
| * Giving and gracefully accepting constructive feedback | |||
| * Accepting responsibility and apologizing to those affected by our mistakes, | |||
| - Demonstrating empathy and kindness toward other people | |||
| - Being respectful of differing opinions, viewpoints, and experiences | |||
| - Giving and gracefully accepting constructive feedback | |||
| - Accepting responsibility and apologizing to those affected by our mistakes, | |||
| and learning from the experience | |||
| * Focusing on what is best not just for us as individuals, but for the | |||
| - Focusing on what is best not just for us as individuals, but for the | |||
| overall community | |||
| Examples of unacceptable behavior include: | |||
| * The use of sexualized language or imagery, and sexual attention or | |||
| - The use of sexualized language or imagery, and sexual attention or | |||
| advances of any kind | |||
| * Trolling, insulting or derogatory comments, and personal or political attacks | |||
| * Public or private harassment | |||
| * Publishing others' private information, such as a physical or email | |||
| - Trolling, insulting or derogatory comments, and personal or political attacks | |||
| - Public or private harassment | |||
| - Publishing others' private information, such as a physical or email | |||
| address, without their explicit permission | |||
| * Other conduct which could reasonably be considered inappropriate in a | |||
| - Other conduct which could reasonably be considered inappropriate in a | |||
| professional setting | |||
| ## Enforcement Responsibilities | |||
| @@ -121,8 +121,8 @@ https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. | |||
| Community Impact Guidelines were inspired by [Mozilla's code of conduct | |||
| enforcement ladder](https://github.com/mozilla/diversity). | |||
| [homepage]: https://www.contributor-covenant.org | |||
| For answers to common questions about this code of conduct, see the FAQ at | |||
| https://www.contributor-covenant.org/faq. Translations are available at | |||
| https://www.contributor-covenant.org/translations. | |||
| [homepage]: https://www.contributor-covenant.org | |||
+ 13
- 11
.pre-commit-config.yaml
View File
| @@ -13,7 +13,7 @@ ci: | |||
| repos: | |||
| - repo: https://github.com/pre-commit/pre-commit-hooks | |||
| rev: v4.1.0 | |||
| rev: v4.2.0 | |||
| hooks: | |||
| - id: end-of-file-fixer | |||
| - id: trailing-whitespace | |||
| @@ -24,7 +24,7 @@ repos: | |||
| - id: check-docstring-first | |||
| - repo: https://github.com/asottile/pyupgrade | |||
| rev: v2.31.1 | |||
| rev: v2.32.0 | |||
| hooks: | |||
| - id: pyupgrade | |||
| args: [--py36-plus] | |||
| @@ -42,15 +42,17 @@ repos: | |||
| - id: yapf | |||
| name: YAPF formatting | |||
| # TODO | |||
| #- repo: https://github.com/executablebooks/mdformat | |||
| # rev: 0.7.7 | |||
| # hooks: | |||
| # - id: mdformat | |||
| # additional_dependencies: | |||
| # - mdformat-gfm | |||
| # - mdformat-black | |||
| # - mdformat_frontmatter | |||
| - repo: https://github.com/executablebooks/mdformat | |||
| rev: 0.7.14 | |||
| hooks: | |||
| - id: mdformat | |||
| additional_dependencies: | |||
| - mdformat-gfm | |||
| - mdformat-black | |||
| exclude: | | |||
| (?x)^( | |||
| README.md | |||
| )$ | |||
| - repo: https://github.com/asottile/yesqa | |||
| rev: v1.3.0 | |||
+ 11
- 7
CONTRIBUTING.md
View File
| @@ -18,16 +18,19 @@ Submitting a PR is easy! This example shows how to submit a PR for updating `req | |||
| ### 1. Select File to Update | |||
| Select `requirements.txt` to update by clicking on it in GitHub. | |||
| <p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p> | |||
| ### 2. Click 'Edit this file' | |||
| Button is in top-right corner. | |||
| <p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p> | |||
| ### 3. Make Changes | |||
| Change `matplotlib` version from `3.2.2` to `3.3`. | |||
| <p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p> | |||
| ### 4. Preview Changes and Submit PR | |||
| @@ -35,6 +38,7 @@ Change `matplotlib` version from `3.2.2` to `3.3`. | |||
| Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** | |||
| for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose | |||
| changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! | |||
| <p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p> | |||
| ### PR recommendations | |||
| @@ -70,21 +74,21 @@ understand and use to **reproduce** the problem. This is referred to by communit | |||
| a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces | |||
| the problem should be: | |||
| * ✅ **Minimal** – Use as little code as possible that still produces the same problem | |||
| * ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself | |||
| * ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem | |||
| - ✅ **Minimal** – Use as little code as possible that still produces the same problem | |||
| - ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself | |||
| - ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem | |||
| In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code | |||
| should be: | |||
| * ✅ **Current** – Verify that your code is up-to-date with current | |||
| - ✅ **Current** – Verify that your code is up-to-date with current | |||
| GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new | |||
| copy to ensure your problem has not already been resolved by previous commits. | |||
| * ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this | |||
| - ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this | |||
| repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. | |||
| If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 ** | |||
| Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing | |||
| If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 | |||
| **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing | |||
| a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better | |||
| understand and diagnose your problem. | |||
+ 25
- 28
README.md
View File
| @@ -103,8 +103,6 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc. | |||
| </details> | |||
| <details> | |||
| <summary>Inference with detect.py</summary> | |||
| @@ -149,20 +147,20 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12 | |||
| <details open> | |||
| <summary>Tutorials</summary> | |||
| * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED | |||
| * [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ | |||
| - [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED | |||
| - [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ | |||
| RECOMMENDED | |||
| * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW | |||
| * [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW | |||
| * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) | |||
| * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW | |||
| * [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 | |||
| * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) | |||
| * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) | |||
| * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) | |||
| * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) | |||
| * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW | |||
| * [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) ⭐ NEW | |||
| - [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW | |||
| - [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW | |||
| - [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) | |||
| - [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW | |||
| - [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 | |||
| - [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) | |||
| - [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) | |||
| - [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) | |||
| - [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) | |||
| - [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW | |||
| - [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) ⭐ NEW | |||
| </details> | |||
| @@ -203,7 +201,6 @@ Get started in seconds with our verified environments. Click each icon below for | |||
| |:-:|:-:| | |||
| |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | | |||
| <!-- ## <div align="center">Compete and Win</div> | |||
| We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes! | |||
| @@ -224,18 +221,15 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi | |||
| <details> | |||
| <summary>Figure Notes (click to expand)</summary> | |||
| * **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. | |||
| * **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. | |||
| * **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. | |||
| * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` | |||
| - **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. | |||
| - **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. | |||
| - **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. | |||
| - **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` | |||
| </details> | |||
| ### Pretrained Checkpoints | |||
| [assets]: https://github.com/ultralytics/yolov5/releases | |||
| [TTA]: https://github.com/ultralytics/yolov5/issues/303 | |||
| |Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B) | |||
| |--- |--- |--- |--- |--- |--- |--- |--- |--- | |||
| |[YOLOv5n][assets] |640 |28.0 |45.7 |**45** |**6.3**|**0.6**|**1.9**|**4.5** | |||
| @@ -253,10 +247,10 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi | |||
| <details> | |||
| <summary>Table Notes (click to expand)</summary> | |||
| * All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). | |||
| * **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` | |||
| * **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` | |||
| * **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` | |||
| - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). | |||
| - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` | |||
| - **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` | |||
| - **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` | |||
| </details> | |||
| @@ -302,3 +296,6 @@ professional support requests please visit [https://ultralytics.com/contact](htt | |||
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/> | |||
| </a> | |||
| </div> | |||
| [assets]: https://github.com/ultralytics/yolov5/releases | |||
| [tta]: https://github.com/ultralytics/yolov5/issues/303 | |||
+ 58
- 48
utils/loggers/wandb/README.md
View File
| @@ -1,66 +1,72 @@ | |||
| 📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021. | |||
| * [About Weights & Biases](#about-weights-&-biases) | |||
| * [First-Time Setup](#first-time-setup) | |||
| * [Viewing runs](#viewing-runs) | |||
| * [Disabling wandb](#disabling-wandb) | |||
| * [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage) | |||
| * [Reports: Share your work with the world!](#reports) | |||
| - [About Weights & Biases](#about-weights-&-biases) | |||
| - [First-Time Setup](#first-time-setup) | |||
| - [Viewing runs](#viewing-runs) | |||
| - [Disabling wandb](#disabling-wandb) | |||
| - [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage) | |||
| - [Reports: Share your work with the world!](#reports) | |||
| ## About Weights & Biases | |||
| Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions. | |||
| Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows: | |||
| * [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time | |||
| * [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically | |||
| * [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization | |||
| * [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators | |||
| * [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently | |||
| * [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models | |||
| - [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time | |||
| - [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically | |||
| - [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization | |||
| - [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators | |||
| - [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently | |||
| - [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models | |||
| ## First-Time Setup | |||
| <details open> | |||
| <summary> Toggle Details </summary> | |||
| When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device. | |||
| W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as: | |||
| ```shell | |||
| $ python train.py --project ... --name ... | |||
| ``` | |||
| ```shell | |||
| $ python train.py --project ... --name ... | |||
| ``` | |||
| YOLOv5 notebook example: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | |||
| <img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png"> | |||
| </details> | |||
| </details> | |||
| ## Viewing Runs | |||
| <details open> | |||
| <summary> Toggle Details </summary> | |||
| Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged: | |||
| * Training & Validation losses | |||
| * Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95 | |||
| * Learning Rate over time | |||
| * A bounding box debugging panel, showing the training progress over time | |||
| * GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage** | |||
| * System: Disk I/0, CPU utilization, RAM memory usage | |||
| * Your trained model as W&B Artifact | |||
| * Environment: OS and Python types, Git repository and state, **training command** | |||
| - Training & Validation losses | |||
| - Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95 | |||
| - Learning Rate over time | |||
| - A bounding box debugging panel, showing the training progress over time | |||
| - GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage** | |||
| - System: Disk I/0, CPU utilization, RAM memory usage | |||
| - Your trained model as W&B Artifact | |||
| - Environment: OS and Python types, Git repository and state, **training command** | |||
| <p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p> | |||
| </details> | |||
| ## Disabling wandb | |||
| * training after running `wandb disabled` inside that directory creates no wandb run | |||
|  | |||
| ## Disabling wandb | |||
| - training after running `wandb disabled` inside that directory creates no wandb run | |||
|  | |||
| * To enable wandb again, run `wandb online` | |||
|  | |||
| - To enable wandb again, run `wandb online` | |||
|  | |||
| ## Advanced Usage | |||
| You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started. | |||
| <details open> | |||
| <h3> 1: Train and Log Evaluation simultaneousy </h3> | |||
| This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b> | |||
| @@ -71,18 +77,20 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma | |||
| <b>Code</b> <code> $ python train.py --upload_data val</code> | |||
|  | |||
| </details> | |||
| <h3>2. Visualize and Version Datasets</h3> | |||
| </details> | |||
| <h3>2. Visualize and Version Datasets</h3> | |||
| Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact. | |||
| <details> | |||
| <summary> <b>Usage</b> </summary> | |||
| <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code> | |||
|  | |||
| </details> | |||
|  | |||
| </details> | |||
| <h3> 3: Train using dataset artifact </h3> | |||
| <h3> 3: Train using dataset artifact </h3> | |||
| When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that | |||
| can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b> | |||
| <details> | |||
| @@ -90,51 +98,54 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma | |||
| <b>Code</b> <code> $ python train.py --data {data}_wandb.yaml </code> | |||
|  | |||
| </details> | |||
| <h3> 4: Save model checkpoints as artifacts </h3> | |||
| </details> | |||
| <h3> 4: Save model checkpoints as artifacts </h3> | |||
| To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. | |||
| You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged | |||
| <details> | |||
| <details> | |||
| <summary> <b>Usage</b> </summary> | |||
| <b>Code</b> <code> $ python train.py --save_period 1 </code> | |||
|  | |||
| </details> | |||
| </details> | |||
| <h3> 5: Resume runs from checkpoint artifacts. </h3> | |||
| </details> | |||
| <h3> 5: Resume runs from checkpoint artifacts. </h3> | |||
| Any run can be resumed using artifacts if the <code>--resume</code> argument starts with <code>wandb-artifact://</code> prefix followed by the run path, i.e, <code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system. | |||
| <details> | |||
| <details> | |||
| <summary> <b>Usage</b> </summary> | |||
| <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code> | |||
|  | |||
| </details> | |||
| <h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3> | |||
| </details> | |||
| <h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3> | |||
| <b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b> | |||
| The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or | |||
| train from <code>_wandb.yaml</code> file and set <code>--save_period</code> | |||
| <details> | |||
| <details> | |||
| <summary> <b>Usage</b> </summary> | |||
| <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code> | |||
|  | |||
| </details> | |||
| </details> | |||
| <h3> Reports </h3> | |||
| </details> | |||
| <h3> Reports </h3> | |||
| W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)). | |||
| <img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png"> | |||
| ## Environments | |||
| YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): | |||
| @@ -144,7 +155,6 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with | |||
| - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) | |||
| - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> | |||
| ## Status | |||
|  | |||
Loading…