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

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

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

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

• pip install easydict

For training:

1. Modify the .yml file in ./cfg/imagenet/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 cls_train.py --cfg ./cfg/imagenet/air50_1x64d.yml 
   

   2.1 Training with mixup (optional):

   python tools/cls_mixup_train.py --cfg ./cfg/imagenet/air50_1x64d_mixup.yml 
   

   for better performace:

   • double the epochs for training with mixup
   • a few extra epochs with no mixup after the process above

   2.2 Ttraining cifar dataset (optional):

   python tools/cls_cifar.py --cfg ./cfg/cifar10/resnext29_8x64d.yml
   

   or with mixup (usually weight_decay: 0.0001):

   python tools/cls_mixup_cifar.py --cfg ./cfg/cifar10/resnext29_8x64d_mixup.yml
   

For evaluating:

1. Modify the .yml file in ./cfg/imagenet/air50-1x64d:

   • add the model.pth.tar to pretrained:
   • set the evaluate: True

2. Evaluate a network:

   python train_cls.py --cfg ./cfg/imagenet/air50_1x64d.yml 
   

For evaluating image by image:

1. Modify the tools/cls_eval.py file

2. Evaluate a network:

   python tools/cls_eval.py
   

• Aligned Inception ResNet (air)
• Cosine Learning Rate
• Mixup
• Random-Erasing

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

• Speed test on single Titan xp GPU with batch_size: 1.

Validation error rate is reported.

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

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).