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ReadMe.md

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-# Visualized BGE
-
-
-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
-