Instructions to use ProbeX/Model-J__ResNet__model_idx_0897 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_0897 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_0897") 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_0897") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0897", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a4ae80d2c6d3272625561bf9340a38d8a1489d1541a180378d09f9bc86ab27a
- Size of remote file:
- 171 MB
- SHA256:
- 9ceee84295b70a1d87fe9214cb16b6d7bcb3fa13a46a76e451633f1f75a84420
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