82 lines
2.2 KiB
Markdown
82 lines
2.2 KiB
Markdown
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# Multilabel classification quick start
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Based on the [NUS-WIDE-SCENE](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html) dataset which is a subset of NUS-WIDE dataset, you can experience multilabel of PaddleClas, include training, evaluation and prediction. Please refer to [Installation](install.md) to install at first.
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## Preparation
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* Enter PaddleClas directory
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```
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cd path_to_PaddleClas
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```
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* Create and enter `dataset/NUS-WIDE-SCENE` directory, download and decompress NUS-WIDE-SCENE dataset
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```shell
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mkdir dataset/NUS-WIDE-SCENE
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cd dataset/NUS-WIDE-SCENE
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wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
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tar -xf NUS-SCENE-dataset.tar
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```
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* Return `PaddleClas` root home
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```
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cd ../../
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```
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## Environment
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### Download pretrained model
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You can use the following commands to download the pretrained model of ResNet50_vd.
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```bash
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mkdir pretrained
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cd pretrained
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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cd ../
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```
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## Training
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```shell
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export CUDA_VISIBLE_DEVICES=0
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python -m paddle.distributed.launch \
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--gpus="0" \
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tools/train.py \
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-c ./configs/quick_start/ResNet50_vd_multilabel.yaml
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```
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After training for 10 epochs, the best accuracy over the validation set should be around 0.72.
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## Evaluation
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```bash
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python tools/eval.py \
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-c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
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-o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
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-o load_static_weights=False
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```
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The metric of evaluation is based on mAP, which is commonly used in multilabel task to show model perfermance. The mAP over validation set should be around 0.57.
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## Prediction
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```bash
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python tools/infer/infer.py \
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-i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
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--model ResNet50_vd \
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--pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
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--use_gpu True \
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--load_static_weights False \
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--multilabel True \
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--class_num 33
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```
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You will get multiple output such as the following:
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```
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class id: 3, probability: 0.6025
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class id: 23, probability: 0.5491
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class id: 32, probability: 0.7006
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```
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