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DETR Turtorial

Data preparation

To download the dataset, please refer to prepare_data.md.

COCO format

To use coco data to train detection, you can refer to configs/detection/detr/detr_r50_8x2_150e_coco.py for more configuration details.

Get Started

To immediately use a model on a given input image, we provide the Predictor API. Predictor group together a pretrained model with the preprocessing that was used during that model's training. For example, we can easily extract detected objects in an image:

>>> from easycv.predictors.detector import DetectionPredictor

# Specify file path
>>> model_path = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/detr/epoch_150.pth'
>>> config_path = 'configs/detection/detr/detr_r50_8x2_150e_coco.py'
>>> img = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/demo/demo.jpg'

# Allocate a predictor for object detection
>>> model = DetectionPredictor(model_path, config_path)
>>> output = model(img)[0]
>>> model.visualize(img, output, out_file='./result.jpg')
output['detection_scores'][:2] = [0.58311516, 0.98532575]
output['detection_classes'][:2] = [2, 2]
output['detection_boxes'][:2] = [[1.32131638e+02, 9.08366165e+01, 1.51008240e+02, 1.01831055e+02],
								[1.89690186e+02, 1.08048561e+02, 2.96801422e+02, 1.54441940e+02]]

Here we get a list of objects detected in the image, with a box surrounding the object and a confidence score. The prediction results are as follows:

result

Quick Start

To use COCO format data, use config file configs/detection/detr/detr_r50_8x2_150e_coco.py

You can use the quick_start.md for local installation or use our provided doker images.

registry.cn-shanghai.aliyuncs.com/pai-ai-test/eas-service:blade_cu111_easycv

Train

Single gpu:

python tools/train.py \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \
		${NUM_GPUS} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}
Arguments

  • NUM_GPUS: number of gpus

  • CONFIG_PATH: the config file path of a detection method

  • WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

GPUS=8
bash tools/dist_train.sh configs/detection/detr/detr_r50_8x2_150e_coco.py $GPUS

Evaluation

Single gpu:

python tools/eval.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		--eval

Multi gpus:

bash tools/dist_test.sh \
		${CONFIG_PATH} \
		${NUM_GPUS} \
		${CHECKPOINT} \
		--eval
Arguments

  • CONFIG_PATH: the config file path of a detection method

  • NUM_GPUS: number of gpus

  • CHECKPOINT: the checkpoint file named as epoch_*.pth.

Examples:

GPUS=8
bash tools/dist_test.sh configs/detection/detr/detr_r50_8x2_150e_coco.py $GPUS work_dirs/detection/detr/detr_150e.pth --eval

Export model

python tools/export.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		${EXPORT_PATH}

For more details of the export process, you can refer to export.md.

Arguments
  • CONFIG_PATH: the config file path of a detection method
  • CHECKPOINT:your checkpoint file of a detection method named as epoch_*.pth.
  • EXPORT_PATH: your path to save export model

Examples:

python tools/export.py configs/detection/detr/detr_r50_8x2_150e_coco.py \
        work_dirs/detection/detr/detr_150e.pth \
        work_dirs/detection/detr/detr_150e_export.pth