Instructions to use magicslabnu/clip_vit_small_patch16_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/clip_vit_small_patch16_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True)
model = AutoModelForImageClassification.from_pretrained("magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True)Quick Links
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="magicslabnu/clip_vit_small_patch16_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")