syjarvis
/

mini-embed-vision-v1.0

@@ -1,199 +1,222 @@
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 ---
+language:
+- zh
+license: apache-2.0
 ---
+<div align="center">
+![logo](./images/logb.png)
+# mini-embed-vision
+轻量级中文图文统一嵌入模型（Multimodal Embedding Model for Chinese）
+中文 | [English](./README_en.md)
+</div>
+## 📌 简介
+* **mini-embed-vision** 是一个轻量级的中文多模态嵌入模型，旨在为个人开发者提供可复现、低成本、高性能的图文联合嵌入方案。
+* 本项目在 **冻结文本编码器** 的前提下，基于对比学习（Contrastive Learning）框架，通过可训练的投影层对齐图像与文本的嵌入空间，在显著降低训练成本的同时保持良好的跨模态对齐能力。
+* **基座模型**：
+  - 文本编码器：`BAAI/bge-base-zh-v1.5`
+  - 视觉编码器：`openai/clip-vit-base-patch32`
+* **适用场景**：中文图文检索、多模态搜索、内容理解、边缘设备部署等资源受限环境。
+---
+## 📦 项目结构
+* mini-embed-vision/
+  ├── Model.py # 多模态模型结构定义
+  ├── train.py # 训练脚本
+  ├── data.py # 数据加载与预处理（基于 COCO128，支持扩展）
+  ├── example/ # 使用示例
+  │ ├── test_embed_image.py # 图像嵌入示例
+  │ └── test_image_text_ser.py # 图文检索示例
+  ├── requirements.txt # 依赖库
+  └── README.md # 本文档
+  > ✅ 兼容 `transformers`、`peft`、`datasets` 等主流 Hugging Face 生态库。
+---
+## 🚀 快速开始
+### 0. 环境搭建
+#### 0.1 克隆代码
+```bash
+git clone https://github.com/SyJarvis/mini-embed-vision.git
+cd mini-embed-vision
+```
+#### 0.2 安装依赖
+```
+pip install -r requirements.txt
+```
+### 1. 使用预训练模型
+#### 1.1 下载模型（推荐使用国内镜像加速）
+> 💡 由于 Hugging Face 官方服务器访问受限，建议通过 HF-Mirror 镜像下载。
+方法一：使用 huggingface-cli + 镜像
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+huggingface-cli download --resume-download syjarvis/mini-embed-vision-v1.0 --local-dir ./mini-embed-vision-v1.0
+```
+方法二：使用modelscope
+```bash
+modelscope download --model shangye/mini-embed-vision-v1.0
+```
+#### 1.2 运行示例
+* 图像嵌入提取
+  ```bash
+  python example/test_embed_image.py
+  ```
+* 图文检索示例
+  ```
+  python example/test_image_text_ser.py
+  ```
+### 2. 推理示例（代码）
+#### 图像嵌入提取
+```python
+from PIL import Image
+from transformers import AutoTokenizer, AutoImageProcessor, AutoModel
+import requests
+import torch
+model_dir = "./mini-embed-vision-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+image_processor = AutoImageProcessor.from_pretrained(model_dir)
+model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).to("cuda")
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+inputs = image_processor(image, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    emb = model.encode_image(inputs["pixel_values"])  # shape: [1, 768]
+print("✅ 图像嵌入形状:", emb.shape)
+print("✅ 嵌入范数（应为 1.0）:", torch.norm(emb, dim=-1).item())
+```
+#### 多模态图文检索
+```python
+from PIL import Image
+from transformers import AutoTokenizer, AutoImageProcessor, AutoModel
+import requests
+import torch
+model_dir = "./mini-embed-vision-v1.0"
+model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+image_processor = AutoImageProcessor.from_pretrained(model_dir)
+# 图像
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+image_inputs = image_processor(image, return_tensors="pt").to("cuda")
+# 文本查询
+queries = [
+    "两只猫坐在沙发上",
+    "一只狗在草地上奔跑",
+    "可爱的猫咪",
+    "海洋中的鲸鱼",
+    "室内宠物"
+]
+# 编码图像
+with torch.no_grad():
+    image_embedding = model.get_image_features(**image_inputs)  # [1, 768]
+# 编码文本
+text_embeddings = []
+for text in queries:
+    encoded = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512).to("cuda")
+    with torch.no_grad():
+        emb = model.get_text_features(**encoded)  # [1, 768]
+    text_embeddings.append(emb)
+text_embeddings = torch.cat(text_embeddings, dim=0)  # [N, 768]
+# 计算相似度（已 L2 归一化，点积 = 余弦相似度）
+similarities = torch.matmul(image_embedding, text_embeddings.T).squeeze(0)
+# 排序输出
+print("\n🔍 图文检索结果（按相关性排序）:")
+results = sorted(zip(queries, similarities.tolist()), key=lambda x: x[1], reverse=True)
+for i, (query, score) in enumerate(results, 1):
+    print(f"{i}. {query} → 相似度: {score:.4f}")
+```
+### 3. 从头训练模型
+🛠️ 训练脚本位于 train.py， 现提供了COCO128 数据集。欢迎提交 PR 扩展更多数据集或训练策略。
+训练命令
+```bash
+python train.py
+```
+## **📝 更新日志**
+<details close>
+    <summary><b>2025-10-20</b></summary>
+    * 实现了基础的多模态嵌入模型
+    * 支持图像/文本嵌入提取与多模态检索
+    * 发布V1.0预训练模型到Hugging Face Hub
+</details>
+## 📌 Acknowledge
+> [!NOTE]
+> 如果觉得`miniembed-vision`对您有所帮助，可以在 GitHub 上加一个⭐<br/>
+> 因水平有限难免疏漏，欢迎在Issues交流指正或提交PR改进项目<br/>
+## **📜 许可证**
+本项目采用 [Apache License 2.0](LICENSE) 开源协议。
+## 🙏 致谢
+* [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)
+* [OpenAI CLIP](https://github.com/openai/CLIP)
+* [contrastors](https://github.com/nomic-ai/contrastors)