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| ## Install Requirements: | ||
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| - PaddlePaddle 2.1 | ||
| - OS 64 bit | ||
| - Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit | ||
| - pip/pip3(9.0.1+), 64 bit | ||
| - CUDA >= 10.1 | ||
| - cuDNN >= 7.6 | ||
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| ## Install PaddleDetection | ||
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| ```bash | ||
| # Clone PaddleDetection repository | ||
| cd <path/to/clone/PaddleDetection> | ||
| git clone https://github.com/PaddlePaddle/PaddleDetection.git | ||
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| cd PaddleDetection | ||
| # Install other dependencies | ||
| pip install -r requirements.txt | ||
| ``` | ||
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| ## Prepare Dataset | ||
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| Download [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)： | ||
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| ```bash | ||
| cd PaddleDetection/dataset/ | ||
| mkdir publaynet | ||
| # download dataset | ||
| wget https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733 | ||
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| tar -xvf publaynet.tar.gz | ||
| ``` | ||
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| Folder structure： | ||
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| | File or Folder | Description | num | | ||
| | :------------- | :----------------------------------------------- | ------- | | ||
| | `train/` | Images in the training subset | 335,703 | | ||
| | `val/` | Images in the validation subset | 11,245 | | ||
| | `test/` | Images in the testing subset | 11,405 | | ||
| | `train.json` | Annotations for training images | | | ||
| | `val.json` | Annotations for validation images | | | ||
| | `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | | | ||
| | `README.txt` | Text file with the file names and description | | | ||
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| ## Modify Config Files | ||
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| Use `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` config for training： | ||
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| <div align='center'> | ||
| <img src='../../examples/data/PaddleDetection_config.png' width='600px'/> | ||
| </div> | ||
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| From the figure above, `ppyolov2_r50vd_dcn_365e_coco.yml` the config depends on other config files: | ||
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| ``` | ||
| coco_detection.yml：mainly explains the path of training data and verification data | ||
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| runtime.yml：describes common runtime parameters, such as whether to use a GPU, and how many Epoch checkpoints to store per Epoch,etc. | ||
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| optimizer_365e.yml：mainly explains learning rate and optimizer. | ||
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| ppyolov2_r50vd_dcn.yml：mainly explains the model, and the trunk network. | ||
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| ppyolov2_reader.yml：mainly explains the configuration of data reader, such as batch size, number of concurrent loading child processes, etc, and post-read preprocessing operations, such as resize, data enhancement, etc | ||
| ``` | ||
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| You will need to modify the above configuration file according to the actual situation. | ||
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| ## Train | ||
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| * Perform evaluation in training | ||
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| ```bash | ||
| export CUDA_VISIBLE_DEVICES=0,1,2,3 | ||
| python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval | ||
| ``` | ||
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| Notice: If you encounter "`Out of memory error`" problem, try reducing batch size in `ppyolov2_reader.yml` file. | ||
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| * Fine-tune other task | ||
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| When using pre-trained model to fine-tune other task, pretrain_weights can be used directly. The parameters with different shape will be ignored automatically. For example: | ||
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| ```bash | ||
| export CUDA_VISIBLE_DEVICES=0,1,2,3 | ||
| # If the shape of parameters in program is different from pretrain_weights, | ||
| # then PaddleDetection will not use such parameters. | ||
| python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml \ | ||
| -o pretrain_weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final \ | ||
| ``` | ||
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| ## Inference | ||
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| - Output specified directory && Set up threshold | ||
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| ``` | ||
| export CUDA_VISIBLE_DEVICES=0 | ||
| python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml \ | ||
| --infer_img=demo/000000570688.jpg \ | ||
| --output_dir=infer_output/ \ | ||
| --draw_threshold=0.5 \ | ||
| -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final \ | ||
| --use_vdl=Ture | ||
| ``` | ||
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| `--draw_threshold` is an optional argument. Default is 0.5. Different thresholds will produce different results depending on the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659). | ||
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| ## Inference and deployment | ||
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| ### Export model for inference | ||
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| ```bash | ||
| python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference \ | ||
| -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams | ||
| ``` | ||
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| * -c：config file | ||
| * --output_dir：model save dir | ||
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| The prediction model is exported to the directory 'inference/ppyolov2_r50vd_dcn_365e_coco', respectively:`infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel` | ||
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| More Info：https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md | ||
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| ### Python inference | ||
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| ```bash | ||
| python deploy/python/infer.py --model_dir=./inference/ppyolov2_r50vd_dcn_365e_coco --image_file=./demo/road554.png --use_gpu=True | ||
| ``` | ||
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| * --model_dir：the previous step exported model dir | ||
| * --image_file：inference image name | ||
| * --use_gpu：whether use gpu | ||
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| More Info：https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/deploy/python | ||
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| C++ infernece：https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/cpp | ||
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Do you have external documentations for training the Paddle models - we can add a link to that. But this should not be included in the layout parser documentation.