Instructions to use ProbeX/Model-J__ResNet__model_idx_0099 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_0099 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_0099") 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_0099") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0099", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02f5d863ec15a195108b5e04bd87b8c7e04f632f510668c649d0da25443e303c
- Size of remote file:
- 171 MB
- SHA256:
- f059456b4c8e65d4e0d8d5e6a8ef9c950ffbb21066a1b90c75cd205d6e61bdb7
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