mirror of https://github.com/alibaba/EasyCV.git
149 lines
3.7 KiB
Markdown
149 lines
3.7 KiB
Markdown
# DETR Turtorial
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## Data preparation
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To download the dataset, please refer to [prepare_data.md](../prepare_data.md).
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### COCO format
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To use coco data to train detection, you can refer to [configs/detection/detr/detr_r50_8x2_150e_coco.py](https://github.com/alibaba/EasyCV/tree/master/configs/detection/detr/detr_r50_8x2_150e_coco.py) for more configuration details.
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## Get Started
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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:
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``` python
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>>> from easycv.predictors.detector import DetectionPredictor
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# Specify file path
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>>> model_path = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/detr/epoch_150.pth'
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>>> config_path = 'configs/detection/detr/detr_r50_8x2_150e_coco.py'
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>>> img = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/demo/demo.jpg'
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# Allocate a predictor for object detection
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>>> model = DetectionPredictor(model_path, config_path)
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>>> output = model(img)[0]
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>>> model.visualize(img, output, out_file='./result.jpg')
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output['detection_scores'][:2] = [0.58311516, 0.98532575]
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output['detection_classes'][:2] = [2, 2]
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output['detection_boxes'][:2] = [[1.32131638e+02, 9.08366165e+01, 1.51008240e+02, 1.01831055e+02],
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[1.89690186e+02, 1.08048561e+02, 2.96801422e+02, 1.54441940e+02]]
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```
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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:
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## Quick Start
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To use COCO format data, use config file `configs/detection/detr/detr_r50_8x2_150e_coco.py`
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You can use the [quick_start.md](../quick_start.md) for local installation or use our provided doker images.
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```shell
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registry.cn-shanghai.aliyuncs.com/pai-ai-test/eas-service:blade_cu111_easycv
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```
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### Train
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**Single gpu:**
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```shell
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python tools/train.py \
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${CONFIG_PATH} \
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--work_dir ${WORK_DIR}
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```
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**Multi gpus:**
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```shell
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bash tools/dist_train.sh \
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${NUM_GPUS} \
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${CONFIG_PATH} \
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--work_dir ${WORK_DIR}
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```
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<details>
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<summary>Arguments</summary>
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- `NUM_GPUS`: number of gpus
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- `CONFIG_PATH`: the config file path of a detection method
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- `WORK_DIR`: your path to save models and logs
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</details>
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**Examples:**
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Edit `data_root`path in the `${CONFIG_PATH}` to your own data path.
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```shell
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GPUS=8
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bash tools/dist_train.sh configs/detection/detr/detr_r50_8x2_150e_coco.py $GPUS
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```
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### Evaluation
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**Single gpu:**
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```shell
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python tools/eval.py \
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${CONFIG_PATH} \
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${CHECKPOINT} \
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--eval
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```
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**Multi gpus:**
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```shell
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bash tools/dist_test.sh \
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${CONFIG_PATH} \
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${NUM_GPUS} \
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${CHECKPOINT} \
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--eval
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```
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<details>
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<summary>Arguments</summary>
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- `CONFIG_PATH`: the config file path of a detection method
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- `NUM_GPUS`: number of gpus
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- `CHECKPOINT`: the checkpoint file named as epoch_*.pth.
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</details>
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**Examples:**
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```shell
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GPUS=8
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bash tools/dist_test.sh configs/detection/detr/detr_r50_8x2_150e_coco.py $GPUS work_dirs/detection/detr/detr_150e.pth --eval
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```
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### Export model
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```shell
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python tools/export.py \
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${CONFIG_PATH} \
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${CHECKPOINT} \
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${EXPORT_PATH}
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```
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For more details of the export process, you can refer to [export.md](export.md).
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<details>
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<summary>Arguments</summary>
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- `CONFIG_PATH`: the config file path of a detection method
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- `CHECKPOINT`:your checkpoint file of a detection method named as epoch_*.pth.
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- `EXPORT_PATH`: your path to save export model
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</details>
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**Examples:**
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```shell
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python tools/export.py configs/detection/detr/detr_r50_8x2_150e_coco.py \
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work_dirs/detection/detr/detr_150e.pth \
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work_dirs/detection/detr/detr_150e_export.pth
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```
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