Instructions to use ProbeX/Model-J__ResNet__model_idx_0713 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_0713 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_0713") 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_0713") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0713") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ef3991166df45158fa3f9afa58262f50c7a124da1a25cc850700577118dff60
- Size of remote file:
- 171 MB
- SHA256:
- 0e178f9b79dcdf2f88660c1738a3423c3a06b6ffb034306b9cf67061f9aa5d42
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