Instructions to use ProbeX/Model-J__ResNet__model_idx_0598 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_0598 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_0598") 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_0598") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0598", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1cbff26f0f36c77ecaa4c5d0da1088bc0b89d1c627dcfc8123c43277f18ac3d
- Size of remote file:
- 171 MB
- SHA256:
- ad7bac5f79f6dbc932096fd530fdd086fffc29433bbbad1a6e71e96811069c4d
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