README.md

Image Feature Search Engine

A highly memory-efficient and lightning-fast image feature search engine for extremely large datesets built on FAISS with ResNet18 as the default feature extraction network.

Basic Usage

1. Declare global values

   THRESHOLD = 1
   SAVE_DIRECTORY = './binary_embeddings'
   DATA_FOLDER = './Flickr'
   ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])
   TRANSFORM = transforms.Compose([
           transforms.Resize((224,224)),
           transforms.ToTensor(),
           transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225]),
       ])
   CUDA = torch.cuda.is_available()
   BATCH_SIZE = 128
   

2. (Optional) Load data folder to extract feature vectors. Step not necessary if binarized feature vectors already exist.

   data = ImageFolder('./Flickr', transform=TRANSFORM)
   kwargs = {'num_workers': 1, 'pin_memory': True} if CUDA else {}
   data_loader = torch.utils.data.DataLoader(data, batch_size=BATCH_SIZE, **kwargs)
   

3. Instantiate SearchEngine object.

   search_engine = SearchEngine(data, cuda = CUDA, threshold = THRESHOLD, save_directory = SAVE_DIRECTORY, transform=TRANSFORM) 
   

4. Fit index (set load_embeddings to True if binarized feature vectors already exist, else False to extract and binarize feature vectors)

   search_engine.fit(data_loader = data_loader, load_embeddings = True, verbose = True)
   

5. Perform queries on target file

   FILENAME = "~/path/to/file/example.jpg"
   distances, paths = search_engine.query(FILENAME)