Instructions to use ProbeX/Model-J__ResNet__model_idx_0717 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_0717 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_0717") 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_0717") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0717", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0e6b6fa1a6a1bd34b9e1bc6c6aa4b24a38754f5096f9f9cc5c30ded687b473a
- Size of remote file:
- 171 MB
- SHA256:
- 4c3ef7fb5e7211d5b21f1f9339f21c46575c654cdcebc2c08bf3ae48a7134e15
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