Instructions to use amaye15/aimv2-large-patch14-native-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaye15/aimv2-large-patch14-native-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cf06287769c6921948c9fd505a71630111acb8014bdd3772b8fa944c8b2b68c
- Size of remote file:
- 1.24 GB
- SHA256:
- fcd3a644dc6194d36e52071c7cd3427fc600f5b97f27a2b4c693bd0091c1afe3
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