Zero-Shot Image Classification
OpenCLIP
ONNX
Safetensors
Transformers.js
Transformers
English
clip
e-commerce
fashion
multimodal retrieval
custom_code
Instructions to use Findle/marqo-fashionCLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use Findle/marqo-fashionCLIP with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Findle/marqo-fashionCLIP') tokenizer = open_clip.get_tokenizer('hf-hub:Findle/marqo-fashionCLIP') - Transformers.js
How to use Findle/marqo-fashionCLIP with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('zero-shot-image-classification', 'Findle/marqo-fashionCLIP'); - Transformers
How to use Findle/marqo-fashionCLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Findle/marqo-fashionCLIP", 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("Findle/marqo-fashionCLIP", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoProcessor": "marqo_fashionCLIP.MarqoFashionImageProcessor" | |
| }, | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPFeatureExtractor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "size": { | |
| "shortest_edge": 224 | |
| } | |
| } |