TencentARC / ViSFT

This is the official repo for paper Supervised Fine-tuning in turn Improves Visual Foundation Models .

News

• [2024/01/19] We open source the ViSFT including training scripts and weights. Evaluation codes will be released soon.

Introduction

Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning into CLIP’s pretraining but face scalability challenges due to the lack of large-scale region-level datasets. Drawing inspiration from supervised fine-tuning (SFT) in natural language processing such as instruction tuning, we explore the potential of fine-grained SFT in enhancing the generation of vision foundation models after their pretraining. Thus a two-stage method ViSFT ( Vi sion SFT ) is proposed to unleash the fine-grained knowledge of vision foundation models. In ViSFT, the vision foundation model is enhanced by performing visual joint learning on some in-domain tasks and then tested on out-of-domain benchmarks. With updating using ViSFT on 8 V100 GPUs in less than 2 days, a vision transformer with over 4.4B parameters shows improvements across various out-of-domain benchmarks including vision and vision-linguistic scenarios.

Installation

creating a conda environment

conda create -n ViSFT python=3.8

conda activate ViSFT

Install pytorch

we use torch1.12 with CUDA11.3 on 8 NVIDIA Volta V100- SXM2-32GB GPUs

pip install --extra-index-url https://download.pytorch.org/whl/cu113 torch==1.12.0

pip install --extra-index-url https://download.pytorch.org/whl/cu113 torchvision==0.13.0

pip install --extra-index-url https://download.pytorch.org/whl/cu113 torchaudio==0.12.0 

xformers installation

Flash attention is required for running EVA-ViT-E. please refer to xformers

loralib installation

pip install --user git+https://github.com/microsoft/LoRA

compile MSDeform for Mask2former head

cd ./mmf/models/visft/ops
sudo sh make.sh
# back to root dir
cd ../../../../

Other packages installation

pip install -r requirements.txt

Dataset Preparation

export DATA_PATH=your_data_path

image caption

Generating hdf5 files for image caption following hdf5

file strcture:

DATA_PATH/
└── processed_datasets/
    └─── coco_caption_hdf5_files
        ├──TEST_CAPLENS_coco_5_cap_per_img_5_min_word_freq.json
        ├──TEST_CAPTIONS_coco_5_cap_per_img_5_min_word_freq.json
        ├──TEST_IMAGES_coco_5_cap_per_img_5_min_word_freq.hdf5
        ├──TRAIN_CAPLENS_coco_5_cap_per_img_5_min_word_freq.json
        ├──TRAIN_CAPTIONS_coco_5_cap_per_img_5_min_word_freq.json
        ├──TRAIN_IMAGES_coco_5_cap_per_img_5_min_word_freq.hdf5
        ├──VAL_CAPLENS_coco_5_cap_per_img_5_min_word_freq.json
        ├──VAL_CAPTIONS_coco_5_cap_per_img_5_min_word_freq.json
        ├──VAL_IMAGES_coco_5_cap_per_img_5_min_word_freq.hdf5
        └───WORDMAP_coco_5_cap_per_img_5_min_word_freq.json

Detection & Segmentation

file strcture:

DATA_PATH/
└── public_datasets/
    └─── coco
        ├──train2017
        ├──val2017
        ├──test2017
        └───annotations
            ├──instances_train2017.json
            ├──instances_val2017.json
            └───image_info_test-dev2017.json

Training

Stage1

To get compatible in-domain task heads. Using 8 NVIDIA Volta V100-SXM2-32GB GPUs for every in-domain task head.

For eva-vit-g

Preparing weights from LAVIS

wget https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth

Adding your weights path to configs under dir:./projects/visft/configs/stage1/eva_g/

backbone_dir: path/eva_vit_g.pth

Implementing training

bash ./scripts/stage1_train/eva_g/caption.sh
bash ./scripts/stage1_train/eva_g/detection.sh
bash ./scripts/stage1_train/eva_g/segment.sh

For eva-vit-e

Preparing EVA-CLIP weights from EVA

Extact ViT weights

python ./scripts/preprocess/extract_eva_e_vit.py

Adding your weights path to configs under dir:./projects/visft/configs/stage1/eva_e/

backbone_dir: path/EVA02_CLIP_E_psz14_plus_s9B_Visual.pt

Implementing training

# can be executed in parallel
bash ./scripts/stage1_train/eva_e/caption.sh
bash ./scripts/stage1_train/eva_e/detection.sh
bash ./scripts/stage1_train/eva_e/segment.sh

Or you can use the weights we provided.

Stage2

For eva-vit-g

Adding your weights path to configs under dir:./projects/visft/configs/stage2/eva_g/stage2.yaml

backbone_dir: path/eva_vit_g.pth
caption_ckpt_path: 'path/eva_g_caption_heads.ckpt'
segment_ckpt_path:'path/eva_g_segment_heads.ckpt'
detection_ckpt_path: 'path/eva_g_detection_heads.ckpt'

Implementing training

bash ./scripts/stage2_train/eva_g/stage2.sh

For eva-vit-e

Adding your weights path to configs under dir:./projects/visft/configs/stage2/eva_e/stage2.yaml

backbone_dir: path/EVA02_CLIP_E_psz14_plus_s9B_Visual.pt
caption_ckpt_path: 'path/eva_e_caption_heads.ckpt'
segment_ckpt_path:'path/eva_e_segment_heads.ckpt'
detection_ckpt_path: 'path/eva_e_detection_heads.ckpt'

Implementing training

bash ./scripts/stage2_train/eva_e/stage2.sh

Get LoRA Weights

You can extract expected LoRA weights by

python ./scripts/postprocess/extract_lora_weights.py

Or use the LoRA weights we provide:

Evaluation Benchmarks

• [] Zero-shot Image Classification
• [] Zero-shot Image-text Retrieval
• [] OCR
• [] Grounded Object Indentification
• [] VQA
• [] Image Captioning on NoCaps

Acknowledgement

The code of ViSFT is based on the official implementation of mmf , EVA and LAVIS

Citation

If you found our work valuable, please cite:

@misc{jiang2024supervised,
      title={Supervised Fine-tuning in turn Improves Visual Foundation Models}, 
      author={Xiaohu Jiang and Yixiao Ge and Yuying Ge and Chun Yuan and Ying Shan},
      year={2024},
      eprint={2401.10222},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}