Global: model: PP-HGNetV2-B6_ML mode: check_dataset # check_dataset/train/evaluate/predict dataset_dir: dataset/mlcls_nus_examples device: gpu:0,1,2,3 output: "output" CheckDataset: convert: enable: False src_dataset_type: null split: enable: False train_percent: null val_percent: null Train: num_classes: 33 epochs_iters: 20 batch_size: 16 learning_rate: 0.05 pretrain_weight_path: null warmup_steps: 5 resume_path: null log_interval: 20 eval_interval: 1 save_interval: 1 Evaluate: weight_path: "output/best_model.pdparams" log_interval: 1 Export: weight_path: https://paddleclas.bj.bcebos.com/models/multi_label/PP-HGNetV2-B6_ml_decoder_448_pretrained.pdparams Predict: model_dir: "output/best_model" input_path: "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg" kernel_option: run_mode: paddle batch_size: 1