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Coin-CLIP: fine-tuned with a vast collection of coin images from CLIP using contrastive learning. It enhances feature extraction for coins, boosting image search accuracy. This model merges Visual Transformer (ViT) with CLIP's multimodal learning, optimized for numismatic applications.

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Coin-CLIP 🪙 : Enhancing Coin Image Retrieval with CLIP

Coin-CLIP breezedeus/coin-clip-vit-base-patch32 is built upon OpenAI's CLIP (ViT-B/32) model and fine-tuned on 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.

Key Features:

  • State-of-the-art coin image retrieval;
  • Enhanced feature extraction for numismatic images;
  • Seamless integration with CLIP's multimodal learning.

To further simplify the use of the Coin-CLIP model, this project provides tools for quickly building a coin image retrieval engine.

Comparison: Coin-CLIP vs. CLIP

Example 1 (Left: Coin-CLIP; Right: CLIP)

1. Coin-CLIP vs. CLIP

Example 2 (Left: Coin-CLIP; Right: CLIP)

2. Coin-CLIP vs. CLIP

More Examples

more

Example 3 (Left: Coin-CLIP; Right: CLIP) 3. Coin-CLIP vs. CLIP

Example 4 (Left: Coin-CLIP; Right: CLIP) 4. Coin-CLIP vs. CLIP

Example 5 (Left: Coin-CLIP; Right: CLIP) 5. Coin-CLIP vs. CLIP

Example 6 (Left: Coin-CLIP; Right: CLIP) 6. Coin-CLIP vs. CLIP

Install

pip install coin_clip

Usage

Code Examples

Extract Feature Vectors from Coin Images

from coin_clip import CoinClip

# Automatically download the model from Huggingface
model = CoinClip(model_name='breezedeus/coin-clip-vit-base-patch32')
images = ['examples/10_back.jpg', 'examples/16_back.jpg']
img_feats, success_ids = model.get_image_features(images)
print(img_feats.shape)  # --> (2, 512)

⚠️ Note:

The above code automatically downloads the breezedeus/coin-clip-vit-base-patch32 model from Huggingface. If you cannot download automatically, please manually download the model locally, and then initialize CoinClip by specifying the local directory of the model through the model_name parameter, like model_name='path/to/coin-clip-vit-base-patch32'.

Command line tools

Building a Vector Retrieval Engine

coin-clip build-db can be used to build a vector search engine. It extracts features from all coin images 🪙 in a specified directory and builds a ChromaDB vector search engine.

$ coin-clip build-db -h
Usage: coin-clip build-db [OPTIONS]

  Extract vectors from a candidate image set and build a search engine based
  on it.

Options:
  -m, --model-name TEXT       Model Name; either local path or huggingface
                              model name  [default: breezedeus/coin-clip-vit-
                              base-patch32]
  -d, --device TEXT           ['cpu', 'cuda']; Either 'cpu' or 'gpu', or
                              specify a specific GPU like 'cuda:0'. Default is
                              'cpu'.  [default: cpu]
  -i, --input-image-dir TEXT  Folder with Coin Images to be indexed. [required]
  -o, --output-db-dir TEXT    Folder where the built search engine is stored.
                              [default: ./coin_clip_chroma.db]
  -h, --help                  Show this message and exit.

For instance,

$ coin-clip build-db -i examples -o coin_clip_chroma.db

Querying

After building the vector search engine with the above command, you can use the coin-clip retrieve command to retrieve the coin images 🪙 most similar to a specified coin image.

$ coin-clip retrieve -h
Usage: coin-clip retrieve [OPTIONS]

  Retrieve images from the search engine, based on the query image.

Options:
  -m, --model-name TEXT  Model Name; either local path or huggingface model
                         name  [default: breezedeus/coin-clip-vit-base-
                         patch32]
  -d, --device TEXT      ['cpu', 'cuda']; Either 'cpu' or 'gpu', or specify a
                         specific GPU like 'cuda:0'. Default is 'cpu'.
                         [default: cpu]
  --db-dir TEXT          Folder where the built search engine is stored.
                         [default: ./coin_clip_chroma.db]
  -i, --image-fp TEXT    Image Path to retrieve  [required]
  -h, --help             Show this message and exit.

For instance,

$ coin-clip retrieve --db-dir coin_clip_chroma.db -i examples/10_back.jpg

A cup of coffee for the author

It is not easy to maintain and evolve the project, so if it is helpful to you, please consider offering the author a cup of coffee 🥤.


Official code base: https://github.com/breezedeus/coin-clip. Please cite it properly.

About

Coin-CLIP: fine-tuned with a vast collection of coin images from CLIP using contrastive learning. It enhances feature extraction for coins, boosting image search accuracy. This model merges Visual Transformer (ViT) with CLIP's multimodal learning, optimized for numismatic applications.

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