Instructions to use ProbeX/Model-J__ResNet__model_idx_0318 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_0318 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_0318") 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_0318") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0318", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8cb1ae20f133b9a26130a88ec416555682950f890b6e1931ad92f860202c258
- Size of remote file:
- 171 MB
- SHA256:
- 1e055180069bcecf377a0c4d1feb728e6226408280001965e30e4211de2710f5
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