For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding # [Visualized BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual) In this project, we introduce Visualized-BGE, a universal multi-modal embedding model. By integrating image token embedding into the BGE Text Embedding framework, Visualized-BGE is equipped to handle multi-modal data that extends beyond text in a flexible manner. Visualized-BGE is mainly used for hybrid modal retrieval tasks, including but not limited to: - Multi-Modal Knowledge Retrieval (query: text; candidate: image-text pairs, text, or image) e.g. [WebQA](https://github.com/WebQnA/WebQA) - Composed Image Retrieval (query: image-text pair; candidate: images) e.g. [CIRR](), [FashionIQ]() - Knowledge Retrieval with Multi-Modal Queries (query: image-text pair; candidate: texts) e.g. [ReMuQ]() Moreover, Visualized BGE fully preserves the strong text embedding capabilities of the original BGE model : ) ## Specs ### Model | **Model Name** | **Dimension** | **Text Embedding Model** | **Language** | **Weight** | | --- | --- | --- | --- | --- | | BAAI/bge-visualized-base-en-v1.5 | 768 | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [🤗 HF link](https://huggingface.co/BAAI/bge-visualized/blob/main/Visualized_base_en_v1.5.pth) | | BAAI/bge-visualized-m3 | 1024 | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [🤗 HF link](https://huggingface.co/BAAI/bge-visualized/blob/main/Visualized_m3.pth) | ### Data We have generated a hybrid multi-modal dataset consisting of over 500,000 instances for training. The dataset will be released at a later time. ## Usage ### Installation: #### Install FlagEmbedding: ``` git clone https://github.com/FlagOpen/FlagEmbedding.git cd FlagEmbedding pip install -e . ``` #### Another Core Packages: ``` pip install torchvision timm einops ftfy ``` You don't need to install `xformer` and `apex`. They are not essential for inference and can often cause issues. ### Generate Embedding for Multi-Modal Data: You have the flexibility to use Visualized-BGE encoding for multi-modal data in various formats. This includes data that is exclusively text-based, solely image-based, or a combination of both text and image data. > **Note:** Please download the model weight file ([bge-visualized-base-en-v1.5](https://huggingface.co/BAAI/bge-visualized/resolve/main/Visualized_base_en_v1.5.pth?download=true), [bge-visualized-m3](https://huggingface.co/BAAI/bge-visualized/resolve/main/Visualized_m3.pth?download=true)) in advance and pass the path to the `model_weight` parameter. - Composed Image Retrival ``` python ############ Use Visualized BGE doing composed image retrieval import torch from FlagEmbedding.visual.modeling import Visualized_BGE model = Visualized_BGE(model_name_bge = "BAAI/bge-base-en-v1.5", model_weight="path: Visualized_base_en_v1.5.pth") model.eval() with torch.no_grad(): query_emb = model.encode(image="./imgs/cir_query.png", text="Make the background dark, as if the camera has taken the photo at night") candi_emb_1 = model.encode(image="./imgs/cir_candi_1.png") candi_emb_2 = model.encode(image="./imgs/cir_candi_2.png") sim_1 = query_emb @ candi_emb_1.T sim_2 = query_emb @ candi_emb_2.T print(sim_1, sim_2) # tensor([[0.8750]]) tensor([[0.7816]]) ``` - Multi-Modal Knowledge Retrieval ``` python ####### Use Visualized BGE doing multi-modal knowledge retrieval import torch from FlagEmbedding.visual.modeling import Visualized_BGE model = Visualized_BGE(model_name_bge = "BAAI/bge-base-en-v1.5", model_weight="path: Visualized_base_en_v1.5.pth") with torch.no_grad(): query_emb = model.encode(text="Are there sidewalks on both sides of the Mid-Hudson Bridge?") candi_emb_1 = model.encode(text="The Mid-Hudson Bridge, spanning the Hudson River between Poughkeepsie and Highland.", image="./imgs/wiki_candi_1.jpg") candi_emb_2 = model.encode(text="Golden_Gate_Bridge", image="./imgs/wiki_candi_2.jpg") candi_emb_3 = model.encode(text="The Mid-Hudson Bridge was designated as a New York State Historic Civil Engineering Landmark by the American Society of Civil Engineers in 1983. The bridge was renamed the \"Franklin Delano Roosevelt Mid-Hudson Bridge\" in 1994.") sim_1 = query_emb @ candi_emb_1.T sim_2 = query_emb @ candi_emb_2.T sim_3 = query_emb @ candi_emb_3.T print(sim_1, sim_2, sim_3) # tensor([[0.6932]]) tensor([[0.4441]]) tensor([[0.6415]]) ``` - Multilingual Multi-Modal Retrieval ``` python ##### Use M3 doing Multilingual Multi-Modal Retrieval import torch from FlagEmbedding.visual.modeling import Visualized_BGE model = Visualized_BGE(model_name_bge = "BAAI/bge-m3", model_weight="path: Visualized_m3.pth") model.eval() with torch.no_grad(): query_emb = model.encode(image="./imgs/cir_query.png", text="一匹马牵着这辆车") candi_emb_1 = model.encode(image="./imgs/cir_candi_1.png") candi_emb_2 = model.encode(image="./imgs/cir_candi_2.png") sim_1 = query_emb @ candi_emb_1.T sim_2 = query_emb @ candi_emb_2.T print(sim_1, sim_2) # tensor([[0.7026]]) tensor([[0.8075]]) ``` ## Evaluation Result Visualized BGE delivers outstanding zero-shot performance across multiple hybrid modal retrieval tasks. It can also serve as a base model for downstream fine-tuning for hybrid modal retrieval tasks. #### Zero-shot Performance - Statistical information of the zero-shot multi-modal retrieval benchmark datasets. During the zero-shot evaluation, we utilize the queries from the validation or test set of each dataset to perform retrieval assessments within the entire corpus of the respective dataset. ![Statistical information for the zero-shot multi-modal retrieval benchmark datasets.](./imgs/zs-benchmark.png) - Zero-shot evaluation results with Recall@5 on various hybrid multi-modal retrieval benchmarks. The -MM notation indicates baseline models that have undergone multi-modal training on our generated data. ![Zero-shot evaluation results with Recall@5 on various hybrid multi-modal retrieval benchmarks.](./imgs/zs-performance.png) #### Fine-tuning on Downstream Tasks - Supervised fine-tuning performance on the WebQA dataset. All retrievals are performed on the entire deduplicated corpus. ![image.png](./imgs/SFT-WebQA.png) - Supervised fine-tuning performance on the CIRR test set. ![image.png](./imgs/SFT-CIRR.png) - Supervised fine-tuning performance on the ReMuQ test set. ![image.png](./imgs/SFT-ReMuQ.png) ## FAQ **Q1: Can Visualized BGE be used for cross-modal retrieval (text to image)?** A1: While it is technically possible, it's not the recommended use case. Our model focus on augmenting hybrid modal retrieval tasks with visual capabilities. ## Acknowledgement The image token embedding model in this project is built upon the foundations laid by [EVA-CLIP](https://github.com/baaivision/EVA/tree/master/EVA-CLIP). ## Citation If you find this repository useful, please consider giving a star ⭐ and citation > Paper will be released soon