Instructions to use ProbeX/Model-J__ResNet__model_idx_0285 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_0285 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_0285") 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_0285") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0285", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1296754e3596f706c08c9f9d5d0a4968d453134a7b3114c4eedd9dc725a2b9ec
- Size of remote file:
- 171 MB
- SHA256:
- 67796359ba9bac8c8632283f06609268ada4d6fb73b2dcb182702484ab9623d4
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