# CLIP Model ## Custom Image-Text Retrieval Dataset ### 1. Prepare the Dataset Prepare the `image_queries.jsonl` dataset for image-text retrieval in the following format (file name must be fixed): ```{code-block} json :caption: custom_eval/multimodal/text-image-retrieval/image_queries.jsonl {"image_path": "custom_eval/multimodal/images/dog.jpg", "query": ["dog"]} {"image_path": "custom_eval/multimodal/images/AMNH.jpg", "query": ["building"]} {"image_path": "custom_eval/multimodal/images/tokyo.jpg", "query": ["city", "tokyo"]} {"image_path": "custom_eval/multimodal/images/tesla.jpg", "query": ["car", "tesla"]} {"image_path": "custom_eval/multimodal/images/running.jpg", "query": ["man", "running"]} ``` Where: - `image_path`: Path to the image, supporting local paths. - `query`: Text descriptions for image-text retrieval, supporting multiple descriptions, such as `["dog", "cat"]`. ### 2. Configure Evaluation Parameters ```python task_cfg = { "eval_backend": "RAGEval", "eval_config": { "tool": "clip_benchmark", "eval": { "models": [ { "model_name": "AI-ModelScope/chinese-clip-vit-large-patch14-336px", } ], "dataset_name": ["custom"], "data_dir": "custom_eval/multimodal/text-image-retrieval", "split": "test", "batch_size": 128, "num_workers": 1, "verbose": True, "skip_existing": False, "limit": 1000, }, }, } ``` ```{seealso} [Full Parameter Explanation](../../user_guides/backend/rageval_backend/clip_benchmark.md#configure-evaluation-parameters) ``` Where: - `dataset_name`: Dataset name, must be specified as `custom`. - `data_dir`: Dataset directory, containing the `image_queries.jsonl` file. ### 3. Run Evaluation Task ```python from evalscope.run import run_task run_task(task_cfg=task_cfg) ``` The evaluation output is as follows: ```json {"dataset": "custom", "model": "AI-ModelScope/chinese-clip-vit-large-patch14-336px", "task": "zeroshot_retrieval", "metrics": {"image_retrieval_recall@5": 1.0, "text_retrieval_recall@5": 1.0}} ``` ## Convert Image-Text Retrieval Data to Text Retrieval Data To facilitate the evaluation of different multimodal retrieval methods, this framework supports converting image-text retrieval problems into text retrieval problems using a multimodal large model, followed by text retrieval evaluation. ### 1. Prepare the Dataset Supported input datasets include [image-text retrieval datasets](../../user_guides/backend/rageval_backend/clip_benchmark.md#supported-datasets) and the custom image-text retrieval dataset mentioned above. ### 2. Configure Evaluation Parameters ```python task_cfg = { "eval_backend": "RAGEval", "eval_config": { "tool": "clip_benchmark", "eval": { "models": [ { "model_name": "internvl2-8b", "api_base": "http://localhost:8008/v1", "api_key": "xxx", "prompt": "用中文描述这张图片", } ], "dataset_name": ["muge"], "split": "test", "task": "image_caption", "batch_size": 2, "num_workers": 1, "verbose": True, "skip_existing": False, "limit": 10, }, }, } ``` Parameter Explanation: - The `models` list must include a multimodal large model configuration: - `model_name`: Name of the multimodal large model, e.g., `internvl2-8b`. - `api_base`: API address of the multimodal large model, e.g., `http://localhost:8008/v1`. - `api_key`: API key for the multimodal large model, e.g., `xxx`. - `prompt`: Prompt for the multimodal large model input, e.g., `"用中文描述这张图片"`. - `task`: Evaluation task, must be specified as `image_caption`. ### 3. Run the Conversion Task Run the following code to start the conversion: ```python from evalscope.run import run_task run_task(task_cfg=task_cfg) ``` The output is as follows: ``` 2024-10-22 19:56:09,832 - evalscope - INFO - Write files to outputs/internvl2-8b/muge/retrieval_data 2024-10-22 19:56:10,543 - evalscope - INFO - Evaluation results: {'dataset': 'muge', 'model': 'internvl2-8b', 'task': 'image_caption', 'metrics': {'convertion_successful': True, 'save_path': 'outputs/internvl2-8b/muge/retrieval_data'}} 2024-10-22 19:56:10,544 - evalscope - INFO - Dump results to: outputs/internvl2-8b/muge_image_caption.json ``` The output file directory structure is as follows: ``` muge ├── retrieval_data │ ├── corpus.jsonl │ ├── queries.jsonl │ └── qrels │ └── test.tsv └── muge_image_caption.json ``` The specific contents of the files are as follows: ```{code-block} json :caption: outputs/internvl2-8b/muge/retrieval_data/corpus.jsonl {"_id":0,"text":"这是一张展示澳亚奇品牌的产品广告图片，图片中包含了六罐澳亚奇品牌的饮料，饮料罐上印有品牌的名称和图案。饮料罐排列在纸箱上，纸箱上也有品牌名称和图案。整个包装以红色和黄色为主基调，给人以醒目和吸引人的感觉。"} {"_id":1,"text":"这是一副时尚的眼镜，镜框是金属材质的，颜色为玫瑰金色，镜腿部分是黑色的。镜腿的内侧有品牌标志，看起来像是“The Row”。这款眼镜的设计比较现代，适合日常佩戴。"} {"_id":2,"text":"这张图片展示了一位女性，她正在用手机拍摄自己的侧脸自拍。她有长长的棕色头发，并佩戴着一对精美的耳环。耳环的设计有点像是字母“A”。背景是室内环境，可以看到淡蓝色墙壁和浅色的柜子。"} {"_id":3,"text":"这是一张黑色塑料瓶的图片，瓶身上贴有红色标签，标签上有白色和黄色的文字。标签上内容包括产品名称、品牌和一些图案。瓶口是红色和灰色的盖子。"} {"_id":4,"text":"这是一张客厅的照片，里面有一把单人沙发椅。沙发的靠背和坐垫上有黑白相间的斑马纹图案，椅子的框架是黑色的木制结构，带有卷曲的扶手。沙发的腿部是黑色的，造型优雅。沙发布置在一个铺有地毯的地板上，背景中可以看到部分沙发和装饰画，整个房间的装饰风格显得温馨且现代。"} {"_id":5,"text":"这是一张一次性纸杯的图片。纸杯呈圆柱形，杯壁较为光滑，没有明显的装饰或花纹。杯口部分略微向外扩展，便于抓握。杯子整体呈浅灰色或乳白色，质地看起来较为轻薄。这种纸杯常用于盛装饮料或冷食，适合一次性使用。"} {"_id":6,"text":"这张图片展示的是四个卡通人物，背景有五彩斑斓的光芒。从左到右，这四个角色分别是：\n\n1. 一个穿着蓝色服装、戴着紫色头巾和发饰的角色。\n2. 一个穿着蓝绿色服装、戴着蓝色发饰和翅膀的角色。\n3. 一个穿着粉红色服装、带着红色头饰和翅膀的角色。\n4. 一个穿着红色和白色服装、戴着红色头饰的角色。\n\n背景中，有“新格林童话”和“NEW GREEN”的字样。"} {"_id":7,"text":"这是一张展示手中握着蓝色葡萄的照片。手的主人穿着绿色的毛衣，手指修长。葡萄颜色深蓝，表面光滑，每颗葡萄看起来都十分饱满多汁。旁边有一些绿色叶子和干燥的枝条做装饰。背景是一张木质的桌子，整体画面给人一种自然清新的感觉。"} {"_id":8,"text":"这张图片展示了一个可爱的小马克杯，杯身是浅绿色，配有圆弧形的手柄。杯子上绘有可爱的卡通图案，包括一只戴着耳机的小兔子，并配有“热爱学习”字样，旁边还有两只小耳朵和几颗星星。整个马克杯的设计简洁可爱，适合用作日常饮品盛器。"} {"_id":9,"text":"这是一张展示塑料包装中大量线状物体的图片。这些线状物体堆叠在一起，看起来像是一些纤维或麻线，可能是用于编织或加工的。"} ``` ```{code-block} json :caption: outputs/internvl2-8b/muge/retrieval_data/queries.jsonl {"_id":0,"text":"酸角汁饮料 整箱 云南"} {"_id":1,"text":"达芬奇眼镜"} {"_id":2,"text":"水钻蝴蝶结耳钉"} {"_id":3,"text":"邓州黄酒"} {"_id":4,"text":"斑马纹老虎椅"} {"_id":5,"text":"布丁杯模具"} {"_id":6,"text":"光之美少女盒蛋"} {"_id":7,"text":"蓝莓模型"} {"_id":8,"text":"少女心喝水杯"} {"_id":9,"text":"炸面"} ``` ```{code-block} :caption: outputs/internvl2-8b/muge/retrieval_data/qrels/test.tsv query-id corpus-id score 0 0 1 1 1 1 2 2 1 3 3 1 4 4 1 5 5 1 6 6 1 7 7 1 8 8 1 9 9 1 ``` ### 4. Execute Text Retrieval Task Once the dataset is ready, you can perform text retrieval tasks as per the CMTEB tutorial. ```{seealso} Refer to [Custom Text Retrieval Evaluation](./embedding.md) ```