Instructions to use ProbeX/Model-J__ResNet__model_idx_0596 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_0596 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_0596") 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_0596") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0596", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 256c7c8ed1134a8caba369c4a3158c4b057c49d3e871e1a394654d82928c624a
- Size of remote file:
- 5.37 kB
- SHA256:
- 299c4dba26c0eace2c036e3a81ff79e4e7afbe45413023cba40ae25f70c9ab52
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