pytorch-priv

Pytorch implementation for Classification, Semantic Segmentation, Pose Estimation and Object Detection

• Image Classification
• Semantic Segmentation (progressing...)
• Object Detection (progressing...)
• Pose Estimation (progressing...)

Install

• Install PyTorch>=0.3.0
• Install torchvision>=0.2.0
• Clone recursively

  git clone --recursive https://github.com/soeaver/pytorch-priv
  

• pip install easydict

Training and Evaluating

For training:

1. Modify the .yml file in ./cfg/cls/air50-1x64d:
   • the ckpt is used to save the checkpoints   * if you want use cosine learning rate, please set cosine_lr: True, then lr_schedule and gamma will not be used
   • for resuming training, add the model.pth.tar to resume: and modify start_epoch   * rotation, pixel_jitter and grayscale are extra data augmentation, recommended for training complex networks only
2. Train a network:

   python train_cls.py --cfg ./cfg/cls/air101-1x64d/air50_1x64d_imagenet.yml 
   

For evaluating:

1. Modify the .yml file in ./cfg/cls/air50-1x64d:
   • add the model.pth.tar to pretrained:
   • set the evaluate: True
2. Evaluate a network:

   python train_cls.py --cfg ./cfg/cls/air101-1x64d/air50_1x64d_imagenet.yml 
   

Features

• Aligned Inception ResNet (AIR)
• Cosine Learning Rate
• Mixup (progressing...)

Results

ImageNet

Single-crop (224x224) validation error rate is reported.

Network	Flops (M)	Params (M)	Top-1 Error (%)	Top-5 Error (%)
resnet50-1x64d	4109.4	25.5	22.96	6.54
air50-1x32d	1543.9	9.5	24.99	7.62
air50-1x64d	6148.2	35.9	21.02	5.55
air50-1x80d	9597.9	55.4	-----	----
air101-1x64d	11722.9	64.4	20.13	5.02

License

pytorch-priv is released under the MIT License (refer to the LICENSE file for details).

Contribute

Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).