PaddleClas/docs/en/advanced_tutorials/multilabel/multilabel_en.md

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 ../../

Training

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    tools/train.py \
        -c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml

After training for 10 epochs, the best accuracy over the validation set should be around 0.95.

Evaluation

python tools/eval.py \
    -c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
    -o Arch.pretrained="./output/MobileNetV1/best_model"

Prediction

python3 tools/infer.py
    -c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
    -o Arch.pretrained="./output/MobileNetV1/best_model"

You will get multiple output such as the following:

[{'class_ids': [6, 13, 17, 23, 26, 30], 'scores': [0.95683, 0.5567, 0.55211, 0.99088, 0.5943, 0.78767], 'file_name': './deploy/images/0517_2715693311.jpg', 'label_names': []}]  

Prediction based on prediction engine

Export model

python3 tools/export_model.py \
    -c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
    -o Arch.pretrained="./output/MobileNetV1/best_model"

The default path of the inference model is under the current path ./inference

Prediction based on prediction engine

Enter the deploy directory:

cd ./deploy

Prediction based on prediction engine:

python3 python/predict_cls.py \
     -c configs/inference_multilabel_cls.yaml

You will get multiple output such as the following:

0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): []