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: 1,137 Bytes
ea0524d | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 | # code in this file is adpated from the ALBEF repo (https://github.com/salesforce/ALBEF)
from torchvision import transforms
from .randaugment import RandomAugment
from PIL import Image
def square_transform(size=224):
return transforms.Compose(
[
transforms.Resize((size, size), interpolation=Image.BICUBIC),
transforms.ToTensor(),
]
)
def square_transform_randaug(size=224):
return transforms.Compose(
[
transforms.RandomResizedCrop(size, scale=(0.8, 1.0), interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(),
RandomAugment(
2,
7,
isPIL=True,
augs=[
"Identity",
"AutoContrast",
"Equalize",
"Brightness",
"Sharpness",
"ShearX",
"ShearY",
"TranslateX",
"TranslateY",
"Rotate",
],
),
transforms.ToTensor(),
]
)
|