Instructions to use ProbeX/Model-J__ResNet__model_idx_0109 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_0109 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_0109") 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_0109") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0109", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2979c7e038dbfa6453d2f0e480102b4b3de7e16f7a51365e8c4b48248cd891ab
- Size of remote file:
- 171 MB
- SHA256:
- 66ed27636ed074cccb316f53cc2ee492fcf6ef6ec69970f9fa6a7a0879bd9c5c
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