Quick Start of Multi-label Classification

Experience the training, evaluation, and prediction of multi-label classification based on the NUS-WIDE-SCENE dataset, which is a subset of the NUS-WIDE dataset. Please first install PaddlePaddle and PaddleClas, see Paddle Installation and PaddleClas installation for more details.

Catalogue

• 1. Data and Model Preparation

• 2. Model Training

• 3. Model Evaluation

• 4. Model Prediction

• 5. Predictive engine-based Prediction

  • 5.1 Export inference model

  • 5.2 Predictive engine-based Prediction

1. Data and Model Preparation

• Go to PaddleClas.

cd path_to_PaddleClas

• Create and go to dataset/NUS-WIDE-SCENE, download and unzip the 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 to PaddleClas root directory

cd ../../

2. Model 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 10 epochs, the best correctness of the validation set should be around 0.95.

Note:

1. Currently, the loss function for multi-label classification only supports MultiLabelLoss (BCE Loss).

2. Currently, the evaluation metrics of multi-label classification support Accuracy_Score and HammingDistance. Please look forward to the support of other evaluation metrics.

3. Model Evaluation

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

4. Model Prediction

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

Obtain an output silimar to the following:

[{'class_ids': [6, 13, 17, 23, 30], 'scores': [0.98217, 0.78129, 0.64377, 0.9942, 0.96109], 'label_names': ['clouds', 'lake', 'ocean', 'sky', 'water'], 'file_name': 'deploy/images/0517_2715693311.jpg'}]

5. Predictive engine-based Prediction

5.1 Export inference model

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

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

5.2 Predictive engine-based Prediction

Go to the deploy first：

cd ./deploy

Inference and prediction through predictive engines:

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

The predicted pictures are as follows:

After executing the inference command, obtain an output silimar to the following:

0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 30], score(s): [0.98, 0.78, 0.64, 0.99, 0.96], label_name(s): ['clouds', 'lake', 'ocean', 'sky', 'water']