Instructions to use ariG23498/tw.resnet50.a1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ariG23498/tw.resnet50.a1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ariG23498/tw.resnet50.a1_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ariG23498/tw.resnet50.a1_in1k") model = AutoModelForImageClassification.from_pretrained("ariG23498/tw.resnet50.a1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K