Instructions to use ProbeX/Model-J__ResNet__model_idx_0085 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_0085 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_0085") 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_0085") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0085") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 766145f54718269a2176aa274d811b0aa86939415afa0373dcb4979e45da66d6
- Size of remote file:
- 5.37 kB
- SHA256:
- 7746897da59589f73b0391c5a07f288f85ea28810887bb03671cad823929eba1
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