fastembed/docs/examples/Image_Embedding.ipynb

Image Embedding

As of version 0.3.0 fastembed supports computation of image embeddings.

The process is as easy and straightforward as with text embeddings. Let's see how it works.

In [5]:

from fastembed import ImageEmbedding

model = ImageEmbedding("Qdrant/resnet50-onnx")

embeddings_generator = model.embed(
    ["../../tests/misc/image.jpeg", "../../tests/misc/small_image.jpeg"]
)
embeddings_list = list(embeddings_generator)
embeddings_list

Out [5]:

Fetching 3 files: 100%|██████████| 3/3 [00:00<00:00, 47482.69it/s]

[array([0.        , 0.        , 0.        , ..., 0.        , 0.01139933,
        0.        ], dtype=float32),
 array([0.02169187, 0.        , 0.        , ..., 0.        , 0.00848291,
        0.        ], dtype=float32)]

Preprocessing

Preprocessing is encapsulated in the ImageEmbedding class, applied operations are identical to the ones provided by Hugging Face Transformers. You don't need to think about batching, opening/closing files, resizing images, etc., Fastembed will take care of it.

Supported models

List of supported image embedding models can either be found here or by calling the ImageEmbedding.list_supported_models() method.

In [6]:

ImageEmbedding.list_supported_models()

Out [6]:

[{'model': 'Qdrant/clip-ViT-B-32-vision',
  'dim': 512,
  'description': 'CLIP vision encoder based on ViT-B/32',
  'size_in_GB': 0.34,
  'sources': {'hf': 'Qdrant/clip-ViT-B-32-vision'},
  'model_file': 'model.onnx'},
 {'model': 'Qdrant/resnet50-onnx',
  'dim': 2048,
  'description': 'ResNet-50 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__.',
  'size_in_GB': 0.1,
  'sources': {'hf': 'Qdrant/resnet50-onnx'},
  'model_file': 'model.onnx'}]