Instructions to use ProbeX/Model-J__ResNet__model_idx_0935 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_0935 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_0935") 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_0935") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0935", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f0bcc2fe776568400e82e4e6e194ef2ea345ae9fc37a021acef303a5b056447
- Size of remote file:
- 171 MB
- SHA256:
- 497f21f8130b324c00b5931c3477f09c756c4b358588a7e6d86aa9cfcb91e735
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