Instructions to use ProbeX/Model-J__ResNet__model_idx_0944 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_0944 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_0944") 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_0944") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0944", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1489fc8f39673ddc27132bb54cc167100c03f20433dbafb13cb3b7df177c6032
- Size of remote file:
- 171 MB
- SHA256:
- 9fbbdc7213ff8d8467f002cc5d82a6dbaf1d2bcffc1e6fb2aaefa7dff7a952ed
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