lix-byte / Image-retrieval-based-EfficientNet-B0

Added a new fully connected layer to extract 512-dimensional features of the image for image retrieval.

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Image retrieval based EfficientNet-B0

Use the pre-trained model of the network, add a new fully connected layer, and train on your own data set. When performing feature extraction, the final classifier layer is removed, and 512-dimensional features of the image are obtained for retrieval. This project is a simple exercise.

Usage

  • Install with pip install efficientnet_pytorch. For more information, please go to https://github.com/lukemelas/EfficientNet-PyTorch

  • In main.py,To modify related parameters, please change the path of the training set and test set to your own path. This file is mainly for classification training to obtain a model suitable for your own data set.

    • train
      • BEAR
        • BEAR_0.jpg
        • BEAR_1.jpg
        • ...
      • CATS
        • CATS_0.jpg
        • CATS_1.jpg
        • ...
      • ...
    • val
      • BEAR
        • BEAR_100.jpg
        • BEAR_101.jpg
        • ...
      • CATS
        • CATS_100.jpg
        • CATS_101.jpg
        • ...
      • ...
  • extract_feats.py. Modify your own database path (database to be retrieved), and then run the program, the characteristics of all pictures in the database will be stored in a dictionary for later retrieval.

    • database
      • 0.jpg
      • 1.jpg
      • 2.jpg
      • 3.jpg
      • ...
  • retrieval.py. Enter the path of the picture you want to retrieve, then you will get the five images that the program thinks are most similar.

About

Added a new fully connected layer to extract 512-dimensional features of the image for image retrieval.


Languages

Language:Python 100.0%