Instructions to use ProbeX/Model-J__ResNet__model_idx_0683 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_0683 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_0683") 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_0683") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0683", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 008596c213c2d01bf17e7de6158bbff16fe14e4b720fe8c92a0e26787e0d7175
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ab8721a6a783ea327b0f10ab1fe1ff398064dcbda3c0837d62c603628dc6633
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