Instructions to use ProbeX/Model-J__ResNet__model_idx_0898 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_0898 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_0898") 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_0898") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0898", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fca6488b0dd49f36e179af6339e461c64487cc4576afb95d489d9f8bade4e0f
- Size of remote file:
- 171 MB
- SHA256:
- 12f7e5f697f503ee985d6c02a09a24e4111bba660d887f587f664c0044386c41
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