Quick Start¶

Getting Started with MindYOLO¶

This document provides a brief introduction to the usage of built-in command-line tools in MindYOLO.

Inference Demo with Pre-trained Models¶

Pick a model and its config file from the model zoo, such as, ./configs/yolov7/yolov7.yaml.
Download the corresponding pre-trained checkpoint from the model zoo of each model.
To run YOLO object detection with the built-in configs, please run:

# Run with Ascend (By default)
python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg

# Run with GPU
python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg --device_target=GPU

For details of the command line arguments, see demo/predict.py -h or look at its source code to understand their behavior. Some common arguments are: * To run on cpu, modify device_target to CPU. * The results will be saved in ./detect_results

Training & Evaluation in Command Line¶

Prepare your dataset in YOLO format. If trained with COCO (YOLO format), prepare it from yolov5 or the darknet.

  coco/
    {train,val}2017.txt
    annotations/
      instances_{train,val}2017.json
    images/
      {train,val}2017/
          00000001.jpg
          ...
          # image files that are mentioned in the corresponding train/val2017.txt
    labels/
      {train,val}2017/
          00000001.txt
          ...
          # label files that are mentioned in the corresponding train/val2017.txt

To train a model on 8 NPUs/GPUs:

mpirun --allow-run-as-root -n 8 python train.py --config ./configs/yolov7/yolov7.yaml  --is_parallel True

To train a model on 1 NPU/GPU/CPU:

python train.py --config ./configs/yolov7/yolov7.yaml

To evaluate a model's performance on 1 NPU/GPU/CPU:

python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt

To evaluate a model's performance 8 NPUs/GPUs:
```
mpirun --allow-run-as-root -n 8 python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt --is_parallel True
```
Notes: (1) The default hyper-parameter is used for 8-card training, and some parameters need to be adjusted in the case of a single card. (2) The default device is Ascend, and you can modify it by specifying 'device_target' as Ascend/GPU/CPU, as these are currently supported.
For more options, see train/test.py -h.

Deployment¶

See here.

To use MindYOLO APIs in Your Code¶

To be supplemented.