Instructions to use ProbeX/Model-J__ResNet__model_idx_0106 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_0106 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_0106") 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_0106") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0106", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad3efc5d82170be633c631be0acd6c7dcbbc74b2f3e47ab278bb1087bc4e319c
- Size of remote file:
- 171 MB
- SHA256:
- 83abf95969f14a73396074a019d01d0bcaab23d60e4bc9761bb52865cba62045
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