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Unverified 提交 c264795f authored 作者: Zengyf-CVer's avatar Zengyf-CVer 提交者： GitHub
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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: 's avatarpre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com>
上级 cc1d7df0
隐藏空白字符变更
内嵌 并排
正在显示 包含 119 行增加 和 106 行删除
.github/CODE_OF_CONDUCT.md
浏览文件 @ c264795f
...@@ -17,23 +17,23 @@ diverse, inclusive, and healthy community. ...@@ -17,23 +17,23 @@ diverse, inclusive, and healthy community.
Examples of behavior that contributes to a positive environment for our Examples of behavior that contributes to a positive environment for our
community include: community include:
* Demonstrating empathy and kindness toward other people - Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences - Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback - Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes, - Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience 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 overall community
Examples of unacceptable behavior include: 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 advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks - Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment - Public or private harassment
* Publishing others' private information, such as a physical or email - Publishing others' private information, such as a physical or email
address, without their explicit permission 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 professional setting
## Enforcement Responsibilities ## Enforcement Responsibilities
...@@ -121,8 +121,8 @@ https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. ...@@ -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 Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity). 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 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/faq. Translations are available at
https://www.contributor-covenant.org/translations. https://www.contributor-covenant.org/translations.
[homepage]: https://www.contributor-covenant.org
.pre-commit-config.yaml
浏览文件 @ c264795f
...@@ -13,7 +13,7 @@ ci: ...@@ -13,7 +13,7 @@ ci:
repos: repos:
- repo: https://github.com/pre-commit/pre-commit-hooks - repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.1.0 rev: v4.2.0
hooks: hooks:
- id: end-of-file-fixer - id: end-of-file-fixer
- id: trailing-whitespace - id: trailing-whitespace
...@@ -24,7 +24,7 @@ repos: ...@@ -24,7 +24,7 @@ repos:
- id: check-docstring-first - id: check-docstring-first
- repo: https://github.com/asottile/pyupgrade - repo: https://github.com/asottile/pyupgrade
rev: v2.31.1 rev: v2.32.0
hooks: hooks:
- id: pyupgrade - id: pyupgrade
args: [--py36-plus] args: [--py36-plus]
...@@ -42,15 +42,17 @@ repos: ...@@ -42,15 +42,17 @@ repos:
- id: yapf - id: yapf
name: YAPF formatting name: YAPF formatting
# TODO - repo: https://github.com/executablebooks/mdformat
#- repo: https://github.com/executablebooks/mdformat rev: 0.7.14
# rev: 0.7.7 hooks:
# hooks: - id: mdformat
# - id: mdformat additional_dependencies:
# additional_dependencies: - mdformat-gfm
# - mdformat-gfm - mdformat-black
# - mdformat-black exclude: |
# - mdformat_frontmatter (?x)^(
README.md
)$
- repo: https://github.com/asottile/yesqa - repo: https://github.com/asottile/yesqa
rev: v1.3.0 rev: v1.3.0
......
CONTRIBUTING.md
浏览文件 @ c264795f
...@@ -18,16 +18,19 @@ Submitting a PR is easy! This example shows how to submit a PR for updating `req ...@@ -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 ### 1. Select File to Update
Select `requirements.txt` to update by clicking on it in GitHub. 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> <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' ### 2. Click 'Edit this file'
Button is in top-right corner. 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> <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 ### 3. Make Changes
Change `matplotlib` version from `3.2.2` to `3.3`. 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> <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 ### 4. Preview Changes and Submit PR
...@@ -35,6 +38,7 @@ Change `matplotlib` version from `3.2.2` to `3.3`. ...@@ -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** 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 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 😃! 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> <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 ### PR recommendations
...@@ -70,21 +74,21 @@ understand and use to **reproduce** the problem. This is referred to by communit ...@@ -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 a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
the problem should be: the problem should be:
* ✅ **Minimal** – Use as little code as possible that still produces the same 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 - ✅ **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 - ✅ **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 In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
should be: 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 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. 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 ⚠️. 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 🐛 ** 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 **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 a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
understand and diagnose your problem. understand and diagnose your problem.
......
README.md
浏览文件 @ c264795f
...@@ -103,8 +103,6 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc. ...@@ -103,8 +103,6 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
</details> </details>
<details> <details>
<summary>Inference with detect.py</summary> <summary>Inference with detect.py</summary>
...@@ -149,20 +147,20 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12 ...@@ -149,20 +147,20 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12
<details open> <details open>
<summary>Tutorials</summary> <summary>Tutorials</summary>
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED - [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)&nbsp; ☘️ - [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️
RECOMMENDED RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 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)&nbsp; 🌟 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) - [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW - [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW
* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 - [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) - [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) - [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) - [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) - [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW - [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  ⭐ NEW
* [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)&nbsp; ⭐ NEW - [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)  ⭐ NEW
</details> </details>
...@@ -203,7 +201,6 @@ Get started in seconds with our verified environments. Click each icon below for ...@@ -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) | |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> <!-- ## <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! 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 ...@@ -224,18 +221,15 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
<details> <details>
<summary>Figure Notes (click to expand)</summary> <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. - **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. - **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. - **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` - **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> </details>
### Pretrained Checkpoints ### 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) |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** |[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 ...@@ -253,10 +247,10 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
<details> <details>
<summary>Table Notes (click to expand)</summary> <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). - 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` - **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` - **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` - **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> </details>
...@@ -302,3 +296,6 @@ professional support requests please visit [https://ultralytics.com/contact](htt ...@@ -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%"/> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
</a> </a>
</div> </div>
[assets]: https://github.com/ultralytics/yolov5/releases
[tta]: https://github.com/ultralytics/yolov5/issues/303
utils/loggers/wandb/README.md
浏览文件 @ c264795f
📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021. 📚 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) - [About Weights & Biases](#about-weights-&-biases)
* [Viewing runs](#viewing-runs) - [First-Time Setup](#first-time-setup)
* [Disabling wandb](#disabling-wandb) - [Viewing runs](#viewing-runs)
* [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage) - [Disabling wandb](#disabling-wandb)
* [Reports: Share your work with the world!](#reports) - [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
- [Reports: Share your work with the world!](#reports)
## About Weights & Biases ## 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. 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: 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 - [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 - [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 - [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 - [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 - [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
* [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models - [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
## First-Time Setup ## First-Time Setup
<details open> <details open>
<summary> Toggle Details </summary> <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. 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: 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 ```shell
$ python train.py --project ... --name ... $ 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> 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"> <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 ## Viewing Runs
<details open> <details open>
<summary> Toggle Details </summary> <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: 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 - Training & Validation losses
* Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95 - Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
* Learning Rate over time - Learning Rate over time
* A bounding box debugging panel, showing the training progress over time - A bounding box debugging panel, showing the training progress over time
* GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage** - GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
* System: Disk I/0, CPU utilization, RAM memory usage - System: Disk I/0, CPU utilization, RAM memory usage
* Your trained model as W&B Artifact - Your trained model as W&B Artifact
* Environment: OS and Python types, Git repository and state, **training command** - 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> <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> </details>
## Disabling wandb ## Disabling wandb
* training after running `wandb disabled` inside that directory creates no wandb run
![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png) - training after running `wandb disabled` inside that directory creates no wandb run
![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png)
* To enable wandb again, run `wandb online` - To enable wandb again, run `wandb online`
![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png) ![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png)
## Advanced Usage ## 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. 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> <details open>
<h3> 1: Train and Log Evaluation simultaneousy </h3> <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> 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 ...@@ -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> <b>Code</b> <code> $ python train.py --upload_data val</code>
![Screenshot from 2021-11-21 17-40-06](https://user-images.githubusercontent.com/15766192/142761183-c1696d8c-3f38-45ab-991a-bb0dfd98ae7d.png) ![Screenshot from 2021-11-21 17-40-06](https://user-images.githubusercontent.com/15766192/142761183-c1696d8c-3f38-45ab-991a-bb0dfd98ae7d.png)
</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. 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> <details>
<summary> <b>Usage</b> </summary> <summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code> <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png) ![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
</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 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> can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
<details> <details>
...@@ -90,51 +98,54 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma ...@@ -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> <b>Code</b> <code> $ python train.py --data {data}_wandb.yaml </code>
![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png) ![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
</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. 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 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> <summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --save_period 1 </code> <b>Code</b> <code> $ python train.py --save_period 1 </code>
![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png) ![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
</details>
</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. 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> <summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code> <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png) ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
</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> <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 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> train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
<details> <details>
<summary> <b>Usage</b> </summary> <summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code> <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png) ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
</details>
</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)). 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"> <img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
## Environments ## 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): 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 ...@@ -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) - **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> - **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 ## Status
![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg) ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
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