Instructions to use ProbeX/Model-J__ResNet__model_idx_0563 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0563 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0563") 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("ProbeX/Model-J__ResNet__model_idx_0563") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0563", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6b1a8733e6379cac760d231c0666dd78f2d12b5c0a93ced8561eed53857c2cf
- Size of remote file:
- 5.37 kB
- SHA256:
- 7727130ad959f3f91440f239e44b11c83eb3482d1401e2473f2269cf5f4e167f
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