Instructions to use ProbeX/Model-J__ResNet__model_idx_0302 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_0302 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_0302") 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_0302") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0302", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e95c58f2e1af057dc73e6a32f33d6ac7677bbfcfbffce6f1bf5bcf2f26fdbbc4
- Size of remote file:
- 171 MB
- SHA256:
- 71ecf9162e452f35784fea58f3e66cec1029f81fe4256c5683989b4d576c0b8f
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