Instructions to use ProbeX/Model-J__ResNet__model_idx_0846 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_0846 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_0846") 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_0846") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0846") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b93225ec275d956719081962685cc82638222338c200d9e2058bd7933cd3c17
- Size of remote file:
- 5.37 kB
- SHA256:
- cc99ee2b9e37dc91a9180b2ece01f0f700d074340150cf36f6f6c3350802ebc6
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