Instructions to use ProbeX/Model-J__ResNet__model_idx_0445 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_0445 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_0445") 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_0445") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0445", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 772188412731d36dc19f86f7bfa4377bbf9956e8cab36d8f17df4983b53c3c01
- Size of remote file:
- 171 MB
- SHA256:
- 282cba9f640c628265b2f608b9bd4797c4f2358e231e5736149409be28145624
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