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
| { | |
| "auto_map": { | |
| "AutoProcessor": "marqo_fashionSigLIP.MarqoFashionSigLIPProcessor" | |
| }, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_convert_rgb": true, | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "marqo_fashionSigLIP.MarqoFashionSigLIPProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ] | |
| } |