Instructions to use ProbeX/Model-J__ResNet__model_idx_0253 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_0253 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_0253") 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_0253") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0253", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9603089d7787084e8e8edd1eb90c5ff3e523dac0081ff1ac8a70410e8ff1291c
- Size of remote file:
- 5.37 kB
- SHA256:
- d3b5795305808a32dc0209267a3596e282edecf453ff6d12aee6b3c04ea1b861
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