Instructions to use ProbeX/Model-J__ResNet__model_idx_0380 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_0380 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_0380") 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_0380") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0380", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2720f1d1f932b3e0f8260e66f9e48cde32489541af40354d1db81f465622ea3
- Size of remote file:
- 171 MB
- SHA256:
- bbae7050cbf4496057a92a0827e6cb8c5a3f5ce36bed80af20a0d767073fe2c1
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