Instructions to use ProbeX/Model-J__ResNet__model_idx_0345 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_0345 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_0345") 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_0345") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0345", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 836873568911c6bfb6fa30dc09de2e07988247d77cf6004a47fa1d4b6d910e5d
- Size of remote file:
- 171 MB
- SHA256:
- 9b6bdceaba9d41ca34949613d2eae9e371206e05b1c77f7d518b08b522a719f4
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