Skip to content

Latest commit

 

History

History
85 lines (67 loc) · 3.4 KB

GETTING_STARTED.md

File metadata and controls

85 lines (67 loc) · 3.4 KB
Raw

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

  1. Pick a model and its config file from the Model Zoo, such as, ./configs/yolov7/yolov7.yaml.
  2. Download the corresponding pre-trained checkpoint from the Model Zoo of each model.
  3. 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 1 NPU/GPU/CPU:

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

  • To train a model on 8 NPUs/GPUs:

    msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python train.py --config ./configs/yolov7/yolov7.yaml  --is_parallel True

  • 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:

    msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log 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.

  • Notice that if you are using msrun startup with 2 devices, please add --bind_core=True to improve performance. For example:

  msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118 \
        --log_dir=msrun_log --join=True --cluster_time_out=300 \
        python train.py --config ./configs/yolov7/yolov7.yaml  --is_parallel True

For more information, please refer to here.

Deployment

See here.

To use MindYOLO APIs in Your Code

To be supplemented.