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
File size: 544 Bytes
fd70a75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"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
}
} |