sii-research/CausalRobot-400M

Model card for sii-research/CausalRobot-400M (based on SigLIP)

Model Details

• Model Type: Contrastive Image-Text, Zero-Shot Image Classification.

Usage

pip install open_clip_torch

Download the model from sii-research/CausalRobot-400M

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP')
checkpoint = torch.load(ckpt_path, map_location="cpu")
msg = clip_model.load_state_dict("/path/to/pytorch_model.bin", strict=False)
tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features = F.normalize(image_features, dim=-1)
    text_features = F.normalize(text_features, dim=-1)

    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)