--- title: ViSFT canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/ViSFT" md_url: "https://www.modelscope.cn/models/AI-ModelScope/ViSFT.md" repository: AI-ModelScope/ViSFT last_updated: 2024-03-08 license: apache-2.0 pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis library_name: - pytorch frameworks: - pytorch downloads: 4921 stars: 0 --- # ViSFT > ViSFT - AI-ModelScope 在 ModelScope 开源的模型。This is the official repo for paper Supervised Fine-tuning in turn Improves Visual Foundation Models. AI-ModelScope/ViSFT 是 ModelScope 魔搭社区上的text-to-image-synthesis模型，采用 apache-2.0 许可。 - **Repository**: AI-ModelScope/ViSFT - **License**: apache-2.0 - **Tasks**: text-to-image-synthesis - **Downloads**: 4921 - **Stars**: 0 - **Last updated**: 2024-03-08 Source: https://www.modelscope.cn/models/AI-ModelScope/ViSFT --- This is the official repo for paper [Supervised Fine-tuning *in turn* Improves Visual Foundation Models](https://arxiv.org/abs/2401.10222).
📃[**Paper (ArXiv)**](https://arxiv.org/abs/2401.10222) **|** [**Code**](https://github.com/TencentARC/ViSFT) **|** 🤗[**Huggingface**](https://huggingface.co/TencentARC/ViSFT)
## 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](https://github.com/facebookresearch/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](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/master/create_input_files.py) 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](https://github.com/salesforce/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](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_E_psz14_plus_s9B.pt) 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. | In-domain Heads | | | |----------|:-------------:|:-------------:| | | EVA-G | EVA-E| | Caption Head | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_caption_heads.ckpt) | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_caption_heads.ckpt)| | Segment Head | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_segment_heads.ckpt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_segment_heads.ckpt)| | Detection Head | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_detection_heads.ckpt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_detection_heads.ckpt)| ### 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: | LoRA weights | | | |----------|:-------------:|:-------------:| | Iters| EVA-G | EVA-E| | 5k | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_lora_5000.pt) | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_lora_5000.pt)| | 10k | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_lora_10000.pt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_lora_10000.pt)| | 15k | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_lora_15000.pt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_lora_15000.pt)| | 20k | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_lora_20000.pt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_lora_20000.pt)| | 50k | [weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_g_lora_50000.pt) |[weights](https://huggingface.co/TencentARC/ViSFT/blob/main/eva_e_lora_50000.pt)| ## 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](https://github.com/facebookresearch/mmf), [EVA](https://github.com/baaivision/EVA/tree/master) and [LAVIS](https://github.com/salesforce/LAVIS/tree/main) ## 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} } ```