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 .utils import ( | |
| inception_normalize, | |
| MinMaxResize, | |
| ) | |
| from torchvision import transforms | |
| from .randaug import RandAugment | |
| def pixelbert_transform(size=800): | |
| longer = int((1333 / 800) * size) | |
| return transforms.Compose( | |
| [ | |
| MinMaxResize(shorter=size, longer=longer), | |
| transforms.ToTensor(), | |
| inception_normalize, | |
| ] | |
| ) | |
| def pixelbert_transform_randaug(size=800): | |
| longer = int((1333 / 800) * size) | |
| trs = transforms.Compose( | |
| [ | |
| MinMaxResize(shorter=size, longer=longer), | |
| transforms.ToTensor(), | |
| inception_normalize, | |
| ] | |
| ) | |
| trs.transforms.insert(0, RandAugment(2, 9)) | |
| return trs | |