Instructions to use ProbeX/Model-J__ResNet__model_idx_0664 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_0664 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_0664") 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_0664") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0664", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0e99704253539d81f64e6b9fce853f2bbfd600762b840be7eb4b2070a95184a
- Size of remote file:
- 171 MB
- SHA256:
- 73fbd56011e0c95af44d946eafcffb42eae5c00b2410c12308b896969008e064
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