$ pip install git+https://github.com/openai/CLIP.git
$ pin install -r requirements.txt
Fine-tune CLIP in image-retrieval task
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Input: Image or text query related to FASHION.
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Output: Top images with the highest similarity according to the cosine metrics.
Using Pinecone vector database for fast retrieval result
- Vector database contains 85577 vector ids, those vectors are images embedding and their metadata.
Using Google Cloud Storage for storing image data
$ docker pull duong05102002/retrieval-local-service:v1.23
$ docker run -p 30000:30000 duong05102002/retrieval-local-service:v1.23
Run client.py
for test the local api.
- Image query
$ python client.py --save_dir temp.html --image_query your_image_file
- Text query
$ python client.py --save_dir temp.html --text_query your_text_query
Note: Refresh the html page to display the images
- Top 8 products images similar with image query: