Instructions to use ProbeX/Model-J__ResNet__model_idx_0968 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_0968 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_0968") 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_0968") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0968", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b30a553b6939da3f7ddc9dbbcbab041f5829cdedd4b9acc6921d01a8b45da75
- Size of remote file:
- 171 MB
- SHA256:
- 6b8382f7eaff832a362bb5452503ff8924eb5187eebd4050a1001295d985c833
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