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  tags:
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  - vision
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  - coin
 
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  - coin-retrieval
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  - coin-recognition
 
 
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  widget:
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  - src: >-
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  https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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  library_name: transformers
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  ---
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- # Model Card: CLIP
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  ## Model Details / 模型细节
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- This model is fine-tuned on a coin dataset using **contrastive learning** techniques, based on OpenAI's CLIP (ViT-B/32). It aims to enhance the feature extraction capabilities for **Coin** images, thus achieving more accurate image-based search functionalities. The model combines the powerful features of the Vision Transformer (ViT) with the multimodal learning capabilities of CLIP, specifically optimized for coin imagery.
 
 
 
 
 
 
 
 
 
 
 
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- 这个模型是在 OpenAI 的 CLIP (ViT-B/32) 基础上,利用对比学习技术并使用硬币数据集进行微调得到的。它旨在提高硬币图像的特征提取能力,从而实现更准确的以图搜图功能。该模型结合了视觉变换器(ViT)的强大功能和 CLIP 的多模态学习能力,专门针对硬币图像进行了优化。
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  ## Comparison: Coin-CLIP vs. CLIP / 效果对比
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  ## Model Use / 模型使用
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  ```python3
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  from PIL import Image
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  import requests
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  img_features = F.normalize(img_features, dim=1)
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  ```
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  ## Training Data / 训练数据
 
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  tags:
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  - vision
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  - coin
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+ - clip
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  - coin-retrieval
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  - coin-recognition
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+ - coin-search-engine
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+ - multi-modal learning
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  widget:
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  - src: >-
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  https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
 
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  library_name: transformers
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  ---
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+ # Coin-CLIP 🪙 : Enhancing Coin Image Retrieval with CLIP
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  ## Model Details / 模型细节
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+ This model (**Coin-CLIP**) is built upon
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+ OpenAI's **[CLIP](https://huggingface.co/openai/clip-vit-base-patch32) (ViT-B/32)** model and fine-tuned on
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+ a dataset of more than `340,000` coin images using contrastive learning techniques. This specialized model is designed to significantly improve feature extraction for coin images, leading to more accurate image-based search capabilities. Coin-CLIP combines the power of Visual Transformer (ViT) with CLIP's multimodal learning capabilities, specifically tailored for the numismatic domain.
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+
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+ **Key Features:**
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+ - State-of-the-art coin image retrieval;
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+ - Enhanced feature extraction for numismatic images;
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+ - Seamless integration with CLIP's multimodal learning.
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+
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+ 本模型(**Coin-CLIP**)
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+ 在 OpenAI 的 **[CLIP](https://huggingface.co/openai/clip-vit-base-patch32) (ViT-B/32)** 模型基础上,利用对比学习技术在超过 `340,000` 张硬币图片数据上微调得到的。
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+ **Coin-CLIP** 旨在提高模型针对硬币图片的特征提取能力,从而实现更准确的以图搜图功能。该模型结合了视觉变换器(ViT)的强大功能和 CLIP 的多模态学习能力,并专门针对硬币图片进行了优化。
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  ## Comparison: Coin-CLIP vs. CLIP / 效果对比
 
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  ## Model Use / 模型使用
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+ ### Transformers
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  ```python3
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  from PIL import Image
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  import requests
 
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  img_features = F.normalize(img_features, dim=1)
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  ```
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+ ### Tool / 工具
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+
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+ To further simplify the use of the **Coin-CLIP** model, we provide a simple Python library [breezedeus/Coin-CLIP: Coin CLIP](https://github.com/breezedeus/Coin-CLIP) for quickly building a coin image retrieval engine.
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+ 为了进一步简化 **Coin-CLIP** 模型的使用,我们提供了一个简单的 Python 库 [breezedeus/Coin-CLIP: Coin CLIP](https://github.com/breezedeus/Coin-CLIP),以便快速构建硬币图像检索引擎。
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+
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+ #### Install
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+ ```bash
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+ pip install coin_clip
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+ ```
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+
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+ #### Extract Feature Vectors
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+ ```python
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+ from coin_clip import CoinClip
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+
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+ # Automatically download the model from Huggingface
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+ model = CoinClip(model_name='breezedeus/coin-clip-vit-base-patch32')
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+ images = ['examples/10_back.jpg', 'examples/16_back.jpg']
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+ img_feats, success_ids = model.get_image_features(images)
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+ print(img_feats.shape) # --> (2, 512)
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+ ```
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+ More Tools can be found: [breezedeus/Coin-CLIP: Coin CLIP](https://github.com/breezedeus/Coin-CLIP) .
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  ## Training Data / 训练数据