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
| 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] | |