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>pull/7542/head
Zengyf-CVer
GitHub
parent
cc1d7df03c
commit
c264795f50
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@ -17,23 +17,23 @@ diverse, inclusive, and healthy community.
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Examples of behavior that contributes to a positive environment for our
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community include:
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* Demonstrating empathy and kindness toward other people
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* Being respectful of differing opinions, viewpoints, and experiences
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* Giving and gracefully accepting constructive feedback
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* Accepting responsibility and apologizing to those affected by our mistakes,
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- Demonstrating empathy and kindness toward other people
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- Being respectful of differing opinions, viewpoints, and experiences
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- Giving and gracefully accepting constructive feedback
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- Accepting responsibility and apologizing to those affected by our mistakes,
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and learning from the experience
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* Focusing on what is best not just for us as individuals, but for the
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- Focusing on what is best not just for us as individuals, but for the
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overall community
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Examples of unacceptable behavior include:
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* The use of sexualized language or imagery, and sexual attention or
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- The use of sexualized language or imagery, and sexual attention or
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advances of any kind
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* Trolling, insulting or derogatory comments, and personal or political attacks
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* Public or private harassment
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* Publishing others' private information, such as a physical or email
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- Trolling, insulting or derogatory comments, and personal or political attacks
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- Public or private harassment
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- Publishing others' private information, such as a physical or email
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address, without their explicit permission
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* Other conduct which could reasonably be considered inappropriate in a
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- Other conduct which could reasonably be considered inappropriate in a
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professional setting
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## Enforcement Responsibilities
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@ -121,8 +121,8 @@ https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
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Community Impact Guidelines were inspired by [Mozilla's code of conduct
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enforcement ladder](https://github.com/mozilla/diversity).
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[homepage]: https://www.contributor-covenant.org
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For answers to common questions about this code of conduct, see the FAQ at
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https://www.contributor-covenant.org/faq. Translations are available at
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https://www.contributor-covenant.org/translations.
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[homepage]: https://www.contributor-covenant.org
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@ -13,7 +13,7 @@ ci:
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.1.0
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rev: v4.2.0
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hooks:
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- id: end-of-file-fixer
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- id: trailing-whitespace
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@ -24,7 +24,7 @@ repos:
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- id: check-docstring-first
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- repo: https://github.com/asottile/pyupgrade
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rev: v2.31.1
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rev: v2.32.0
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hooks:
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- id: pyupgrade
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args: [--py36-plus]
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@ -42,15 +42,17 @@ repos:
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- id: yapf
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name: YAPF formatting
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# TODO
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#- repo: https://github.com/executablebooks/mdformat
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# rev: 0.7.7
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# hooks:
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# - id: mdformat
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# additional_dependencies:
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# - mdformat-gfm
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# - mdformat-black
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# - mdformat_frontmatter
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- repo: https://github.com/executablebooks/mdformat
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rev: 0.7.14
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hooks:
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- id: mdformat
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additional_dependencies:
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- mdformat-gfm
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- mdformat-black
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exclude: |
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(?x)^(
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README.md
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)$
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- repo: https://github.com/asottile/yesqa
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rev: v1.3.0
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@ -18,16 +18,19 @@ Submitting a PR is easy! This example shows how to submit a PR for updating `req
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### 1. Select File to Update
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Select `requirements.txt` to update by clicking on it in GitHub.
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<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
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### 2. Click 'Edit this file'
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Button is in top-right corner.
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<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
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### 3. Make Changes
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Change `matplotlib` version from `3.2.2` to `3.3`.
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<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
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### 4. Preview Changes and Submit PR
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@ -35,6 +38,7 @@ Change `matplotlib` version from `3.2.2` to `3.3`.
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Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
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for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
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changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
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<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
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### PR recommendations
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@ -70,21 +74,21 @@ understand and use to **reproduce** the problem. This is referred to by communit
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a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
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the problem should be:
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* ✅ **Minimal** – Use as little code as possible that still produces the same problem
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* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
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* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
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- ✅ **Minimal** – Use as little code as possible that still produces the same problem
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- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
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- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
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In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
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should be:
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* ✅ **Current** – Verify that your code is up-to-date with current
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- ✅ **Current** – Verify that your code is up-to-date with current
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GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
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copy to ensure your problem has not already been resolved by previous commits.
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* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
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- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
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repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
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If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **
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Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
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If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
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**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
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a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
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understand and diagnose your problem.
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53
README.md
53
README.md
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@ -103,8 +103,6 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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</details>
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<details>
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<summary>Inference with detect.py</summary>
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@ -149,20 +147,20 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12
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<details open>
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<summary>Tutorials</summary>
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* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
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* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
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- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
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- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
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RECOMMENDED
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* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
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* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
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* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
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* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
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* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
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* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
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* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
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* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
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* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
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* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
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* [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) ⭐ NEW
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- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
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- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
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- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
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- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
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- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
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- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
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- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
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- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
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- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
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- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
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- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) ⭐ NEW
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</details>
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@ -203,7 +201,6 @@ Get started in seconds with our verified environments. Click each icon below for
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|:-:|:-:|
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|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) |
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<!-- ## <div align="center">Compete and Win</div>
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We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
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@ -224,18 +221,15 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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<details>
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<summary>Figure Notes (click to expand)</summary>
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* **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.
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* **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.
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* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
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* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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- **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.
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- **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.
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- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
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- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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</details>
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### Pretrained Checkpoints
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[assets]: https://github.com/ultralytics/yolov5/releases
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[TTA]: https://github.com/ultralytics/yolov5/issues/303
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|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)
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|--- |--- |--- |--- |--- |--- |--- |--- |---
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|[YOLOv5n][assets] |640 |28.0 |45.7 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
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@ -253,10 +247,10 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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<details>
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<summary>Table Notes (click to expand)</summary>
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* 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).
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* **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`
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* **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`
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* **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`
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- 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).
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- **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`
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- **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`
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- **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`
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</details>
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@ -302,3 +296,6 @@ professional support requests please visit [https://ultralytics.com/contact](htt
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
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</a>
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</div>
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[assets]: https://github.com/ultralytics/yolov5/releases
|
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[tta]: https://github.com/ultralytics/yolov5/issues/303
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@ -1,66 +1,72 @@
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📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
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* [About Weights & Biases](#about-weights-&-biases)
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* [First-Time Setup](#first-time-setup)
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* [Viewing runs](#viewing-runs)
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* [Disabling wandb](#disabling-wandb)
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* [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
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* [Reports: Share your work with the world!](#reports)
|
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|
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- [About Weights & Biases](#about-weights-&-biases)
|
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- [First-Time Setup](#first-time-setup)
|
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- [Viewing runs](#viewing-runs)
|
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- [Disabling wandb](#disabling-wandb)
|
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- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
|
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- [Reports: Share your work with the world!](#reports)
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## About Weights & Biases
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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.
|
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|
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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
|
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* [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
|
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* [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
|
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* [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
|
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|
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## 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
|
||||
|
||||
* To enable wandb again, run `wandb online`
|
||||
|
||||
- training after running `wandb disabled` inside that directory creates no wandb run
|
||||
|
||||
|
||||
- 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>
|
||||
|
||||
|
||||
<h3> 3: Train using dataset artifact </h3>
|
||||
</details>
|
||||
|
||||
<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…
Reference in New Issue