Instructions to use ProbeX/Model-J__ResNet__model_idx_0242 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_0242 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_0242") 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_0242") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0242", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3071211bb03740524eddc5f1d8911c6a4d1e3fc7852edf28db9ac72b219e3b4
- Size of remote file:
- 5.37 kB
- SHA256:
- 522ed2c0764cb9ddaf179f76c51b6a07acf001b0ed9c8abd70650554fb0f1605
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