Instructions to use ProbeX/Model-J__ResNet__model_idx_0855 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_0855 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_0855") 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_0855") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0855", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b254deffbae3adc9b2155165a3b7445764b3fd56dada3472ff974d88d5acc5df
- Size of remote file:
- 171 MB
- SHA256:
- 9008425c323d7a03e2e05a1fcb7f74a6cbc3f57f73d16d7778b8606c2d8a8434
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