Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- TIGER-Lab/MMEB-train
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- microsoft/Phi-3.5-vision-instruct
+library_name: transformers
+tags:
+- Embedding
+---
+
+# VLM2Vec
+
+This repo contains the code and data for [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160). In this paper, we aimed at building a unified multimodal embedding model for any tasks. Our model is based on converting an existing well-trained VLM (Phi-3.5-V) into an embedding model. The basic idea is to add an [EOS] token in the end of the sequence, which will be used as the representation of the multimodal inputs.
+
+<img width="1432" alt="abs" src="https://raw.githubusercontent.com/TIGER-AI-Lab/VLM2Vec/refs/heads/main/figures//train_vlm.png">
+
+## Release
+Our model is being trained on MMEB-train and evaluated on MMEB-eval with contrastive learning. We only use in-batch negatives for training. Our best results were based on Lora training with batch size of 1024. We also have checkpoint with full training with batch size of 2048. Our results on 36 evaluation datasets are:
+### Train/Eval Data
+ - Train data: https://huggingface.co/datasets/TIGER-Lab/MMEB-train
+ - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval
+
+### VLM2Vec Checkpoints
+ - [MMEB.lora8.bs1024](https://huggingface.co/TIGER-Lab/MMEB.lora8.bs1024/)
+ - [MMEB.fullmodel.bs2048](https://huggingface.co/TIGER-Lab/MMEB.fullmodel.bs2048/)
+
+### Experimental Results
+Our model can outperform the existing baselines by a huge margin.
+<img width="900" alt="abs" src="https://raw.githubusercontent.com/TIGER-AI-Lab/VLM2Vec/refs/heads/main/figures//vlm2vec_results.png">
+
+## How to use VLM2Vec
+
+First you can clone our github
+```bash
+git clone https://github.com/TIGER-AI-Lab/VLM2Vec.git
+```
+
+Then you can enter the directory to run the following command.
+```python
+from src.model import MMEBModel
+from src.arguments import ModelArguments
+import torch
+from transformers import HfArgumentParser, AutoProcessor
+from PIL import Image
+import numpy as np
+
+model_args = ModelArguments(
+ model_name='microsoft/Phi-3.5-vision-instruct', 
+ pooling='eos',
+ normalize=True,
+ lora=True,
+ checkpoint_path='TIGER-Lab/VLM2Vec-LoRA')
+
+model = MMEBModel.load(model_args)
+model.eval()
+model = model.to('cuda', dtype=torch.bfloat16)
+
+processor = AutoProcessor.from_pretrained(
+ model_args.model_name,
+ trust_remote_code=True,
+ num_crops=4,
+)
+
+inputs = processor(
+ '<|image_1|> Represent the given image with the following question: What is in the image',
+ [Image.open('figures/example.jpg')])
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+qry_output = model(qry=inputs)["qry_reps"]
+
+# Compute the similarity;
+string = 'A cat and a dog'
+inputs = processor(string, None, return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+
+string = 'A cat and a tiger'
+inputs = processor(string, None, return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+
+string = 'A pig'
+inputs = processor(string, None, return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+
+string = 'a flight'
+inputs = processor(string, None, return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+```
+
+## Citation
+```
+@article{jiang2024vlm2vec,
+ title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
+ author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
+ journal={arXiv preprint arXiv:2410.05160},
+ year={2024}
+}