Instructions to use ProbeX/Model-J__ResNet__model_idx_0107 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_0107 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_0107") 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_0107") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0107", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cac3a280b10add0107c06726e2bbd0d1250a19063a16082b6848fd1fc62c21e
- Size of remote file:
- 171 MB
- SHA256:
- 3343b87cdbdf8838b00acca5b68a34132a5a3599ec7ec7bb28b52a232561dfa9
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