Train Custom Data 📌
📚 This guide explains how to train your own custom dataset with YOLOv5 🚀.
Before You Start
Clone this repo, download tutorial dataset, and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.7.
$ git clone https://github.com/ultralytics/yolov5 # clone repo $ cd yolov5 $ pip install -r requirements.txt # install
Train On Custom Data
1. Create dataset.yaml
COCO128 is a small tutorial dataset composed of the first 128 images in COCO train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. data/coco128.yaml, shown below, is the dataset configuration file that defines 1) an optional download command/URL for auto-downloading, 2) a path to a directory of training images (or path to a *.txt file with a list of training images), 3) the same for our validation images, 4) the number of classes, 5) a list of class names:
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../coco128/images/train2017/ val: ../coco128/images/train2017/ # number of classes nc: 80 # class names names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
2. Create Labels
After using a tool like CVAT, makesense.ai or Labelbox 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:
- One row per object
- Each row is
class x_center y_center width heightformat.
- Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide
widthby image width, and
heightby image height.
- Class numbers are zero-indexed (start from 0).
The label file corresponding to the above image contains 2 persons (class
0) and a tie (class
3. Organize Directories
Organize your train and val images and labels according to the example below. In this example we assume
/coco128 is 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:
dataset/images/im0.jpg # image dataset/labels/im0.txt # label
4. Select a Model
Train a YOLOv5s model on COCO128 by specifying dataset, batch-size, image size and either pretrained
--weights yolov5s.pt (recommended), or randomly initialized
--weights '' --cfg yolov5s.yaml (not recommended). Pretrained weights are auto-downloaded from the latest YOLOv5 release.
# Train YOLOv5s on COCO128 for 5 epochs $ python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt
Weights & Biases Logging (🚀 NEW)
Weights & Biases (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration among team members. To enable W&B logging install
wandb, and then train normally (you will be guided setup on first use).
$ pip install wandb
All results are logged by default to
runs/train, with a new experiment directory created for each new training as
runs/train/exp3, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a Mosaic Dataloader is used for training (shown below), a new concept developed by Ultralytics and first featured in YOLOv4.
train_batch0.jpg shows train batch 0 mosaics and labels:
test_batch0_labels.jpg shows test batch 0 labels:
test_batch0_pred.jpg shows test batch 0 predictions:
Training losses and performance metrics are also logged to Tensorboard and a custom
results.txt logfile which is plotted as
results.png (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained
--weights yolov5s.pt (orange).
from utils.plots import plot_results plot_results(save_dir='runs/train/exp') # plot results.txt as results.png
- Google Colab and Kaggle notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
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