Instructions to use ProbeX/Model-J__ResNet__model_idx_0496 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_0496 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_0496") 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_0496") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0496") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61775f1cbf125fa211287dbeca6584a637d7f2ed5d448e60ceea5303efc8ff7c
- Size of remote file:
- 5.37 kB
- SHA256:
- 4681f33edf02436aa2277b29b8e38330a66d76da0363811b106d7ccb80e027c0
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