Instructions to use ProbeX/Model-J__ResNet__model_idx_0509 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_0509 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_0509") 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_0509") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0509") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6a57fc78f04b49f634f3bd8aa6347c6c0e4340952a41e70475c1003412c872e
- Size of remote file:
- 5.37 kB
- SHA256:
- 2139d8cac290a012b0b4c05d16e921b6f567f189fe56bdedf70986d4b54e3cf8
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