Instructions to use ProbeX/Model-J__ResNet__model_idx_0153 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_0153 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_0153") 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_0153") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0153", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d5d9cb8ee6849d3c200f0110ea425eedcf9bfbb68cbd8e3d035b3f1b7dd798b
- Size of remote file:
- 171 MB
- SHA256:
- ccd213ea7f2cab4e621be2299e4652dc059747bc739edb58792a3a0ff1793ca7
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