Zero-Shot Image Classification
OpenCLIP
ONNX
Safetensors
Transformers
Transformers.js
English
siglip
clip
e-commerce
fashion
multimodal retrieval
custom_code
Instructions to use pySilver/marqo-fashionSigLIP-ST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use pySilver/marqo-fashionSigLIP-ST with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:pySilver/marqo-fashionSigLIP-ST') tokenizer = open_clip.get_tokenizer('hf-hub:pySilver/marqo-fashionSigLIP-ST') - Transformers
How to use pySilver/marqo-fashionSigLIP-ST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="pySilver/marqo-fashionSigLIP-ST", trust_remote_code=True) 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, SigLIP processor = AutoProcessor.from_pretrained("pySilver/marqo-fashionSigLIP-ST", trust_remote_code=True) model = SigLIP.from_pretrained("pySilver/marqo-fashionSigLIP-ST", trust_remote_code=True) - Transformers.js
How to use pySilver/marqo-fashionSigLIP-ST with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('zero-shot-image-classification', 'pySilver/marqo-fashionSigLIP-ST'); - Notebooks
- Google Colab
- Kaggle
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