Instructions to use ProbeX/Model-J__ResNet__model_idx_0479 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_0479 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_0479") 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_0479") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0479") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bf8f798884f9606d3a269bd11a8937764b90c5d61cde8dbe2c82f6d9de19219
- Size of remote file:
- 5.37 kB
- SHA256:
- c41cb0458a0fb00e649b925cd47050c1e5180f078a360270df16220be897eb13
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