Instructions to use ProbeX/Model-J__ResNet__model_idx_0100 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_0100 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_0100") 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_0100") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e55140d5a2940dece81eb9997ccdc5300896c3d6fb458c0d01a20d073ae4cc3b
- Size of remote file:
- 5.37 kB
- SHA256:
- 44820d9cff82342bceb0eb94f813fb9352908164c8432fac2938b6ec25420d7c
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