enczoo 0.1.3

Map images (as `PIL.Images`) to intermediate representations (as `np.ndarray`) from off-the-shelf vision models.

enczoo: easily extract image features from pretrained vision models

enczoo is a Python library with a single goal: to enable you to map images (as PIL.Images) to image features (as numpy arrays) as easily as possible. Features may be extracted from a zoo of popular, pretrained vision models, such as Imagenet-pretrained ResNet50 and CLIP ViT-B/16.

Installation

enczoo requires Python 3.12 or above, and it's recommended you use the wonderful uv to install it. Assuming you have uv, just run the following command in your project:

uv add enczoo

Usage

import enczoo
from PIL import Image

image = Image.open('my-image.png')
model = enczoo.ResNet50(layer_name='avgpool') 
features = model.compute_features(images=[image]) # np.ndarray
# Want another layer? Check out: print(enczoo.ResNet50.layer_names)

Why develop enczoo?

Under the hood, enczoo solves several tiny problems which make correctly computing image features more annoying and error-prone than it should be. For example, enczoo automatically:

• performs model-specific image transforms ("was it -1 to 1, 0 to 1, or 0-255...?"),
• ensures images are in RGB format
• puts the model in inference, not training, mode
• turns off autograd
• returns tensors as np.ndarray (no more .cpu().numpy())
• resizes the image while preserving aspect ratio
• and more!