Image Classification

Install Package

• Clone the GitHub repo:

  git clone https://github.com/zhanghang1989/PyTorch-Encoding
  

• Install PyTorch Encoding (if not yet). Please follow the installation guide Installing PyTorch Encoding.

Get Pre-trained Model

Hint

How to get pretrained model, for example ResNeSt50:

model = encoding.models.get_model('ResNeSt50', pretrained=True)

After clicking cmd in the table, the command for training the model can be found below the table.

ResNeSt

Note

The provided models were trained using MXNet Gluon, this PyTorch implementation is slightly worse than the original implementation.

Model	crop-size	Acc	Command
ResNeSt-50	224	81.03	cmd
ResNeSt-101	256	82.83	cmd
ResNeSt-200	320	83.84	cmd
ResNeSt-269	416	84.54	cmd

# change the rank for worker node python train_dist.py --dataset imagenet --model resnest50 --lr-scheduler cos --epochs 270 --checkname resnest50 --lr 0.025 --batch-size 64 --dist-url tcp://MASTER:NODE:IP:ADDRESS:23456 --world-size 4 --label-smoothing 0.1 --mixup 0.2 --no-bn-wd --last-gamma --warmup-epochs 5 --rand-aug --rank 0 # change the rank for worker node python train_dist.py --dataset imagenet --model resnest101 --lr-scheduler cos --epochs 270 --checkname resnest101 --lr 0.025 --batch-size 64 --dist-url tcp://MASTER:NODE:IP:ADDRESS:23456 --world-size 4 --label-smoothing 0.1 --mixup 0.2 --no-bn-wd --last-gamma --warmup-epochs 5 --rand-aug --rank 0 # change the rank for worker node python train_dist.py --dataset imagenet --model resnest200 --lr-scheduler cos --epochs 270 --checkname resnest200 --lr 0.0125 --batch-size 32 --dist-url tcp://MASTER:NODE:IP:ADDRESS:23456 --world-size 8 --label-smoothing 0.1 --mixup 0.2 --no-bn-wd --last-gamma --warmup-epochs 5 --rand-aug --crop-size 256 --rank 0 # change the rank for worker node python train_dist.py --dataset imagenet --model resnest269 --lr-scheduler cos --epochs 270 --checkname resnest269 --lr 0.0125 --batch-size 32 --dist-url tcp://MASTER:NODE:IP:ADDRESS:23456 --world-size 8 --label-smoothing 0.1 --mixup 0.2 --no-bn-wd --last-gamma --warmup-epochs 5 --rand-aug --crop-size 320 --rank 0

Test Pretrained

• Prepare the datasets by downloading the data into current folder and then runing the scripts in the scripts/ folder:

  python scripts/prepare_imagenet.py --data-dir ./
  

• The test script is in the experiments/recognition/ folder. For evaluating the model (using MS), for example ResNeSt50:

  python verify.py --dataset imagenet --model ResNeSt50 --crop-size 224
  

Train Your Own Model

• Prepare the datasets by downloading the data into current folder and then runing the scripts in the scripts/ folder:

  python scripts/prepare_imagenet.py --data-dir ./
  

• The training script is in the experiments/recognition/ folder. Commands for reproducing pre-trained models can be found in the table.