OpenCLIP is an open-source implementation of OpenAI’s CLIP.
You can find OpenCLIP models by filtering at the left of the models page.
OpenCLIP models hosted on the Hub have a model card with useful information about the models. Thanks to OpenCLIP Hugging Face Hub integration, you can load OpenCLIP models with a few lines of code. You can also deploy these models using Inference Endpoints.
To get started, you can follow the OpenCLIP installation guide. You can also use the following one-line install through pip:
$ pip install open_clip_torch
All OpenCLIP models can easily be loaded from the Hub:
import open_clip
model, preprocess = open_clip.create_model_from_pretrained('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
Once loaded, you can encode the image and text to do zero-shot image classification:
import torch
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0)
text = tokenizer(["a diagram", "a dog", "a cat"])
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs)
It outputs the probability of each possible class:
Label probs: tensor([[0.0020, 0.0034, 0.9946]])
If you want to load a specific OpenCLIP model, you can click Use in OpenCLIP
in the model card and you will be given a working snippet!