Instructions to use ProbeX/Model-J__ResNet__model_idx_0790 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_0790 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_0790") 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_0790") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0790", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11531903cc38e2caab10d3c3a083ae31cfa2cea897253a4e24ed4e81ad12f4d7
- Size of remote file:
- 171 MB
- SHA256:
- f48dd7908a4d16b0e84da56c9a23cea706247a12597ba3072c574a2c7beb5e7d
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