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Multilabel classification quick start

Based on the NUS-WIDE-SCENE dataset which is a subset of NUS-WIDE dataset, you can experience multilabel of PaddleClas, include training, evaluation and prediction. Please refer to Installation to install at first.

Preparation

Enter PaddleClas directory

cd path_to_PaddleClas

Create and enter dataset/NUS-WIDE-SCENE directory, download and decompress NUS-WIDE-SCENE dataset

mkdir dataset/NUS-WIDE-SCENE
cd dataset/NUS-WIDE-SCENE
wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
tar -xf NUS-SCENE-dataset.tar

Return PaddleClas root home

cd ../../

Environment

Download pretrained model

You can use the following commands to download the pretrained model of ResNet50_vd.

mkdir pretrained
cd pretrained
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
cd ../

Training

export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/ResNet50_vd_multilabel.yaml

After training for 10 epochs, the best accuracy over the validation set should be around 0.72.

Evaluation

python tools/eval.py \
    -c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
    -o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
    -o load_static_weights=False

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.

Prediction

python tools/infer/infer.py \
    -i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
    --model ResNet50_vd \
    --pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
    --use_gpu True \
    --load_static_weights False \
    --multilabel True \
    --class_num 33

You will get multiple output such as the following:

    class id: 3, probability: 0.6025
    class id: 23, probability: 0.5491
    class id: 32, probability: 0.7006

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