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GETTING_STARTED.md

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简体中文 | English

How To Start

This page provides a basic tutorial on the use of YOLODetection.

For installation instructions, see INSTALL.md.

Model training

YOLODetection provides training to perform single or multiple cards. All output (log files and checkpoints) will be saved to the working directory.

This is specified by work_dir in the configuration file.

Important: The default learning rate of the configuration file is 1 gpu and the small batch size is 2, accumulating to 64 batch sizes for gradient update.

According to Cosine Fighting Rules, if you use different GPUs or images of each GPU, you need to set a learning rate proportional to the batch size. batch_size and subdivisions are determined.

Use GPU training

python tools/train.py ${CONFIG_FILE}

If you want to specify the working directory in the command, you can add a parameter --work_dir ${YOUR_WORK_DIR}.

Use assign GPU training

python tools/train.py ${CONFIG_FILE} --device ${device} [optional arguments]

Optional parameters:

  • --validate (strongly recommended): every time k during the training epoch (the default value is 1, you can modify this) to execute Evaluation.

  • --work_dir ${WORK_DIR}: Overwrite the working directory specified in the configuration file.

  • --device ${device}: assign cuda device , 0 or 0,1,2,3 or cpu，use all by default。

  • --resume_from ${CHECKPOINT_FILE}: Resume training from the checkpoints file of previous training.

  • --multi-scale: Multi-scale scaling, the size range is +/- 50% of the training image size

The difference between resume_from and load_from:

resume_from loads the model weight and optimizer state, and the training continues from the specified checkpoint. It is usually used to resume training that was interrupted unexpectedly. load_from only loads model weights, and training starts from epoch 0. It is usually used for fine-tuning.

Advanced api interface for testing images

Below is an example of building a model and testing a given image.

from yolodet.apis.inference import inference_detector,init_detector,show_result
import os

config_file ='cfg/yolov4_gpu.py'
checkpoint_file ='work_dirs/yolov4/latest.pth'

# build the model from a config file and a checkpoint file
model = init_detector(config_file, checkpoint_file, device='cuda:0')

# test a single image and show the results
img ='test.jpg' # or img = mmcv.imread(img), which will only load it once
result,t = inference_detector(model, img)
print('%s Done , object num : %s .time:(%.3fs)' % (img,len(result), t))
# visualize the results in a new window
show_result(img, result, model.CLASSES)