Instructions to use ProbeX/Model-J__ResNet__model_idx_0576 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_0576 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_0576") 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_0576") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0576", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e88e09470dc5fb824eb07df44ffeb005523d6d2da1b695ccce5a996f26a45d49
- Size of remote file:
- 171 MB
- SHA256:
- 9eb1cf70726104525c09f931b6c753ac0b2b35c2a19e28f16b91ad0691579332
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