Instructions to use ProbeX/Model-J__ResNet__model_idx_0675 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_0675 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_0675") 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_0675") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0675", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b56e0a3d0d406e132c6a8e5855eb364b9e176771ace21af5ad9bc6469eb04b45
- Size of remote file:
- 5.37 kB
- SHA256:
- 43c793c3b5ffccbbb6da0ef9545c3d73ce93af2542ee3043f0e8da7181f65d06
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