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Trial in 30mins

Based on the flowers102 dataset, it takes only 30 mins to experience PaddleClas, include training varieties of backbone and pretrained model, SSLD distillation, and multiple data augmentation, Please refer to Installation to install at first.

Preparation

  • Enter insatallation dir.
cd path_to_PaddleClas
  • Enter dataset/flowers102, download and decompress flowers102 dataset.
cd dataset/flowers102
# If you want to download from the brower, you can copy the link, visit it
# in the browser, download and then decommpress.
wget https://paddle-imagenet-models-name.bj.bcebos.com/data/flowers102.zip
unzip flowers102.zip
  • Return PaddleClas dir
cd ../../

Environment

Download pretrained model

You can use the following commands to downdload the pretrained models.

mkdir pretrained
cd pretrained
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams

cd ../

Note: If you want to download the pretrained models on Windows environment, you can copy the links to the browser and download.

Training

  • All experiments are running on the NVIDIA® Tesla® V100 single card.
  • First of all, use the following command to set visible device.

If you use mac or linux, you can use the following command:

export CUDA_VISIBLE_DEVICES=0
  • If you use windows, you can use the following command.
set CUDA_VISIBLE_DEVICES=0
  • If you want to train on cpu device, you can modify the field use_gpu: True in the config file to use_gpu: False, or you can append -o use_gpu=False in the training command, which means override the value of use_gpu as False.

Train from scratch

  • Train ResNet50_vd
python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml

If you want to train on cpu device, the command is as follows.

python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml -o use_gpu=False

Similarly, for the following commands, if you want to train on cpu device, you can append -o use_gpu=False in the command.

The validation Top1 Acc curve is shown below.

Finetune - ResNet50_vd pretrained model (Acc 79.12%)

  • Finetune ResNet50_vd model pretrained on the 1000-class Imagenet dataset
python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_finetune.yaml

The validation Top1 Acc curve is shown below

Compare with training from scratch, it improve by 65% to 94.02%

You can use the trained model to infer the result of image docs/images/quick_start/flowers102/image_06739.jpg. The command is as follows.

python3 tools/infer/infer.py \
    -i docs/images/quick_start/flowers102/image_06739.jpg \
    --model=ResNet50_vd \
    --pretrained_model="output/ResNet50_vd/best_model/ppcls" \
    --class_num=102

The output is as follows. Top-5 class ids and their scores are printed.

File:image_06739.jpg, Top-5 result: class id(s): [0, 96, 18, 50, 51], score(s): [0.79, 0.02, 0.01, 0.01, 0.01]
  • Note: Results are different for different models, so you might get different results for the command.

SSLD finetune - ResNet50_vd_ssld pretrained model (Acc 82.39%)

Note: when finetuning model, which has been trained by SSLD, please use smaller learning rate in the middle of net.

ARCHITECTURE:
    name: 'ResNet50_vd'
    params:
        lr_mult_list: [0.5, 0.5, 0.6, 0.6, 0.8]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"

Tringing script

python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml

Compare with finetune on the 79.12% pretrained model, it improve by 0.98% to 95%.

More architecture - MobileNetV3

Training script

python3 tools/train.py -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml

Compare with ResNet50_vd pretrained model, it decrease by 5% to 90%. Different architecture generates different performance, actually it is a task-oriented decision to apply the best performance model, should consider the inference time, storage, heterogeneous device, etc.

RandomErasing

Data augmentation works when training data is small.

Training script

python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml

It improves by 1.27% to 96.27%

Distillation

  • The ResNet50_vd model pretrained on previous chapter will be used as the teacher model to train student model. Save the model to specified directory, command as follows:
cp -r output/ResNet50_vd/19/  ./pretrained/flowers102_R50_vd_final/
  • Use extra_list.txt as unlabeled data, Note:
    • Samples in the extra_list.txt and val_list.txt don't have intersection
    • Because of in the source code, label information is unused, This is still unlabeled distillation
    • Teacher model use the pretrained_model trained on the flowers102 dataset, and student model use the MobileNetV3_large_x1_0 pretrained model(Acc 75.32%) trained on the ImageNet1K dataset
total_images: 7169
ARCHITECTURE:
    name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
pretrained_model:
    - "./pretrained/flowers102_R50_vd_final/ppcls"
    - "./pretrained/MobileNetV3_large_x1_0_pretrained/”
TRAIN:
    file_list: "./dataset/flowers102/train_extra_list.txt"

Final training script

python3 tools/train.py -c ./configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml

It significantly imporve by 6.47% to 96.47% with more unlabeled data and teacher model.

All accuracy

Configuration Top1 Acc
ResNet50_vd.yaml 0.2735
MobileNetV3_large_x1_0_finetune.yaml 0.9000
ResNet50_vd_finetune.yaml 0.9402
ResNet50_vd_ssld_finetune.yaml 0.9500
ResNet50_vd_ssld_random_erasing_finetune.yaml 0.9627
R50_vd_distill_MV3_large_x1_0.yaml 0.9647

The whole accuracy curves are shown below

  • NOTE: As flowers102 is a small dataset, validatation accuracy maybe float 1%.

  • Please refer to Getting_started for more details