ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Dowon <ks2515@naver.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher
GitHub
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---
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comments: true
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description: Train your custom dataset with YOLOv5. Learn to collect, label and annotate images, and train and deploy models. Get started now.
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---
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📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.
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@ -84,6 +85,7 @@ Now continue with `2. Select a Model`.
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### 1.1 Create dataset.yaml
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[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](http://cocodataset.org/#home) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or *.txt files with image paths) and 2) a class `names` dictionary:
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/coco128 # dataset root dir
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@ -102,7 +104,6 @@ names:
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79: toothbrush
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```
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### 1.2 Create Labels
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After using an annotation tool to label your images, export your labels to **YOLO format**, with one `*.txt` file per image (if no objects in image, no `*.txt` file is required). The `*.txt` file specifications are:
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@ -118,10 +119,10 @@ The label file corresponding to the above image contains 2 persons (class `0`) a
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<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png"></p>
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### 1.3 Organize Directories
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Organize your train and val images and labels according to the example below. YOLOv5 assumes `/coco128` is inside a `/datasets` directory **next to** the `/yolov5` directory. **YOLOv5 locates labels automatically for each image** by replacing the last instance of `/images/` in each image path with `/labels/`. For example:
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```bash
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../datasets/coco128/images/im0.jpg # image
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../datasets/coco128/labels/im0.txt # label
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@ -130,7 +131,6 @@ Organize your train and val images and labels according to the example below. YO
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<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png"></p>
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</details>
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### 2. Select a Model
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Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
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```bash
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python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt
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```
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!!! tip "Tip"
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💡 Add `--cache ram` or `--cache disk` to speed up training (requires significant RAM/disk resources).
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#### Comet Logging and Visualization 🌟 NEW
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[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
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[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
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Getting started is easy:
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```shell
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pip install comet_ml # 1. install
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export COMET_API_KEY=<Your API Key> # 2. paste API key
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@ -186,12 +188,11 @@ You can use ClearML Data to version your dataset and then pass it to YOLOv5 simp
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<a href="https://cutt.ly/yolov5-notebook-clearml">
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<img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"/></a>
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#### Local Logging
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Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.
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This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
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This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
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<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280"/>
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@ -204,18 +205,16 @@ plot_results('path/to/results.csv') # plot 'results.csv' as 'results.png'
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<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/results.png"></p>
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## Next Steps
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Once your model is trained you can use your best checkpoint `best.pt` to:
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* Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference on new images and videos
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* [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
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* [Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
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* [Evolve](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) hyperparameters to improve performance
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* [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset
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## Environments
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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):
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- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
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- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <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>
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## Status
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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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](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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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](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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