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+📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀.
+ * [About Weights & Biases](#about-weights-&-biases)
+ * [First-Time Setup](#first-time-setup)
+ * [Viewing runs](#viewing-runs)
+ * [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
+
+ ## First-Time Setup
+
+ Toggle Details
+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 ...
+ ```
+
+ 
+
+
+## Viewing Runs
+
+ Toggle Details
+ 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 realtime . 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**
+
+ 
+
+
+## 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.
+
+ 1. Visualize and Version Datasets
+ Log, visualize, dynamically query, and understand your data with W&B Tables. You can use the following command to log your dataset as a W&B Table. This will generate a {dataset}_wandb.yaml file which can be used to train from dataset artifact.
+
+ Usage
+ Code $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data ..
+
+ 
+
+
+ 2: Train and Log Evaluation simultaneousy
+ This is an extension of the previous section, but it'll also training after uploading the dataset. This also evaluation Table
+ Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
+ so no images will be uploaded from your system more than once.
+
+ Usage
+ Code $ python utils/logger/wandb/log_dataset.py --data .. --upload_data
+
+
+
+
+ 3: Train using dataset artifact
+ 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. This also logs evaluation
+
+ Usage
+ Code $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml
+
+
+
+
+ 4: Save model checkpoints as artifacts
+ 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
+
+
+ Usage
+ Code $ python train.py --save_period 1
+
+
+
+
+
+
+ 5: Resume runs from checkpoint artifacts.
+Any run can be resumed using artifacts if the --resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system.
+
+
+ Usage
+ Code $ python train.py --resume wandb-artifact://{run_path}
+
+
+
+
+ 6: Resume runs from dataset artifact & checkpoint artifacts.
+ Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device
+ 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 --upload_dataset or
+ train from _wandb.yaml file and set --save_period
+
+
+ Usage
+ Code $ python train.py --resume wandb-artifact://{run_path}
+
+
+
+
+
+
+
+
+ Reports
+ 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)).
+
+ 
+
+ ## 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):
+
+ * **Google Colab and Kaggle** notebooks with free GPU: [](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) [](https://www.kaggle.com/ultralytics/yolov5)
+ * **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-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) [](https://hub.docker.com/r/ultralytics/yolov5)
+
+ ## Status
+ 
+
+ If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+