If we want to use some features extracted from deep networks such as ResNet, then this code will be of help.
Currently, we support two kinds of datasets: image and digit.
- Image datasets can be versatile.
- Digit datasets: we support MNIST, USPS, and SVHN.
Python 3, PyTorch 1.0+, PIL
- For image dataset, go to folder
for_image_data, then run:
python main.py --dataset_path 'your_data_folder' --model_name resnet50 --src amazon --tar webcam
- For digit dataset, go to folder
for_digit_data, then run:
python digit_deep_feature.py -src mnist -tar usps
Currently, we support ResNet-50 features since this architecture is very popular.
Office-Home ResNet-50 pretrained features
Image-CLEF ResNet-50 pretrained features
VisDA classification dataset features by ResNet-50
You can download finetuned models here:
Finetuned ResNet-50 models For Office-31 dataset: BaiduYun | Mega
Finetuned ResNet-50 models For Office-Home dataset: BaiduYun | Mega
Finetuned ResNet-50 models For ImageCLEF dataset: BaiduYun | Mega
Finetuned ResNet-50 models For VisDA dataset: BaiduYun | Mega
Finetuned LeNet+ models For MNIST dataset: BaiduYun
The names of the model on image datasets: best_resnet_domain.pth, while domain indicates the domain of the dataset.
The finetune procedure following a 8-2 training/validation split.
See the power of deep features here.