Instructions to use ProbeX/Model-J__ResNet__model_idx_0358 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_0358 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_0358") 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_0358") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0358") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9acb4150020200ae60e55d6d6d378a2ca3f41489eae9a18f0f7607bc08ca1dd2
- Size of remote file:
- 5.37 kB
- SHA256:
- 4673dd0c1a1f71dcb541ecb06ae797cea8ebca99bdb593b64700b52bba8dc494
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