Instructions to use ProbeX/Model-J__ResNet__model_idx_0458 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_0458 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_0458") 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_0458") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0458", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 264638e4715447e17510dbc800be2b8a1094c6ba13e4c8e322d3a1824dfba6e9
- Size of remote file:
- 171 MB
- SHA256:
- 0502abc5f259459c52b7526da0391564cc6628a9d5423d4f1167668b6019b7c0
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