Instructions to use arpanl/Model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Model2") 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("arpanl/Model2") model = AutoModelForImageClassification.from_pretrained("arpanl/Model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 412eeb6155570498571b896327a9f5da8b584fdb3ca2df82052f1f1a22b7fa50
- Size of remote file:
- 343 MB
- SHA256:
- 07ec438081252cfc4f29b9e61d5343c488d7a7df47c1bb46500961a7f4aea884
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