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>

.github/CODE_OF_CONDUCT.md

@@ -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

.pre-commit-config.yaml

@@ -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

CONTRIBUTING.md

@@ -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.
 

README.md

@@ -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)&nbsp; 🚀 RECOMMENDED
-* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️
+- [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)&nbsp; 🌟 NEW
-* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)&nbsp; 🌟 NEW
-* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
-* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ 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)&nbsp; ⭐ NEW
-* [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)&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)  🌟 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

utils/loggers/wandb/README.md

@@ -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
-![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png)
+## 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)
 
-* To enable wandb again, run `wandb online`
-![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png)
+- To enable wandb again, run `wandb online`
+  ![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png)
 
 ## 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>
 
 ![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.
  <details>
   <summary> <b>Usage</b> </summary>
    <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)
- </details>
+![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
+
+</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>
 
 ![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.
   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>
 
 ![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
- </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>
 
 ![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>
  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>
 
 ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
- </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
 
 ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)