Instructions to use ProbeX/Model-J__ResNet__model_idx_0190 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_0190 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_0190") 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_0190") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0190", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 519c2f06ac224d4906a74703047a7409fa6703d744ca853df4f54d23a96a23ba
- Size of remote file:
- 171 MB
- SHA256:
- 10decac8f7f85e13b96d76c4cc59c535a1f9d17fb6197836e64ad8812584c2dd
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