TransFG: Reproduction Using Jittor

Framework

Dependencies:

• Python 3.8+
• Jittor 1.3.9.2
• ml_collections
• gradio

Usage

1. Download Google pre-trained ViT models

• Get models in this link: ViT-B_16, ViT-B_32...

wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz

2. Prepare data

In the paper, we use data from 5 publicly available datasets:

• CUB-200-2011
• Stanford Cars
• Stanford Dogs
• NABirds

Please download them from the official websites and put them in the corresponding folders.

3. Install required packages

Install dependencies with the following command:

pip3 install -r requirements.txt

4. Train

To train TransFG on CUB-200-2011 dataset with 4 gpus in FP-16 mode for 10000 steps run:

bash scripts/train_cub_jt.sh

5. demo

To run the demo, run the following command:

bash scripts/demo.sh

The demo will be available at http://localhost:7860.

Acknowledgement

Many thanks to TransFG for the PyTorch reimplementation of TransFG: A Transformer Architecture for Fine-grained Recognition