Zero-Shot Image Classification
Transformers
PyTorch
Safetensors
English
clip
vision
language
fashion
ecommerce
Instructions to use McClain/fashion-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McClain/fashion-embedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="McClain/fashion-embedder") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("McClain/fashion-embedder") model = AutoModelForZeroShotImageClassification.from_pretrained("McClain/fashion-embedder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from McClain/fashion-embedder: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/McClain/fashion-embedder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://McClain/fashion-embedder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/McClain/fashion-embedder/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- 879fc6d996c4a9322f24a6564f3732acc3d1f1bafeef1dcc1be55418607dc605
- Size of remote file:
- 605 MB
- SHA256:
- 5adfac18a5eda0d68c975b9ddebc219836ca0280b37a1d0dd4e44725193a10b8
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