Instructions to use ProbeX/Model-J__ResNet__model_idx_0865 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_0865 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_0865") 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_0865") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0865", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b292499dbbef6bf2b2a0941edc767314fac5941cce7d8fb9113ef44a3d303133
- Size of remote file:
- 171 MB
- SHA256:
- 1cf6764b1b71357d6d80a1b73ca7b599c9efb18cfcb9e691d45f986d70c74643
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