Zero-Shot Image Classification
Transformers
ONNX
Chinese
English
m2_encoder
feature-extraction
multimodal
image-text-retrieval
bilingual
chinese
english
vision-language
custom-code
custom_code
Eval Results (legacy)
Instructions to use malusama/M2-Encoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malusama/M2-Encoder-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="malusama/M2-Encoder-1B", 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("malusama/M2-Encoder-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 487 Bytes
ea0524d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | from .pixelbert import (
pixelbert_transform,
pixelbert_transform_randaug,
)
from .square_transform import (
square_transform,
square_transform_randaug,
)
_transforms = {
"pixelbert": pixelbert_transform,
"pixelbert_randaug": pixelbert_transform_randaug,
"square_transform": square_transform,
"square_transform_randaug": square_transform_randaug,
}
def keys_to_transforms(keys: list, size=224):
return [_transforms[key](size=size) for key in keys]
|