Instructions to use ProbeX/Model-J__ResNet__model_idx_0885 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_0885 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_0885") 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_0885") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0885", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f45d418f7749b63f13a1ae5fbf8c33485d9817a37d8e4f726a95b25cad91cc77
- Size of remote file:
- 171 MB
- SHA256:
- aeed3708aacbf0953a06a56e8a93edabd53b5db8653969ae3836e1b26e32c755
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