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---
license: apache-2.0
base_model:
- timm/ViT-SO400M-14-SigLIP
pipeline_tag: zero-shot-image-classification
tags:
- causal
- clip
- siglip
---
Model card for `sii-research/CausalRobot-400M` (based on SigLIP)
Model Details
- Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
## Usage
```shell
pip install open_clip_torch
```
Download the model from [sii-research/CausalRobot-400M](https://huggingface.co/sii-research/CausalRobot-400M)
```python
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)
```
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