Zero-Shot Image Classification
OpenCLIP
ONNX
Safetensors
Transformers
Transformers.js
English
siglip
clip
e-commerce
fashion
multimodal retrieval
custom_code
Instructions to use Marqo/marqo-fashionSigLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use Marqo/marqo-fashionSigLIP with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP') tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP') - Transformers
How to use Marqo/marqo-fashionSigLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Marqo/marqo-fashionSigLIP", 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 AutoModel model = AutoModel.from_pretrained("Marqo/marqo-fashionSigLIP", trust_remote_code=True, dtype="auto") - Transformers.js
How to use Marqo/marqo-fashionSigLIP with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('zero-shot-image-classification', 'Marqo/marqo-fashionSigLIP'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 565a36806fbdfcb80f3a08e3a3460482322dcea1a3c05ac4ca42a1a216addb00
- Size of remote file:
- 111 MB
- SHA256:
- 9e45a29f61825b0fdc4ab2648c84791d6af1b63fe86ce7bd7c7fee43fc3b1c4d
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