Instructions to use ProbeX/Model-J__ResNet__model_idx_0596 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_0596 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_0596") 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_0596") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0596", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6be3e8dc2524e806fc395a052343abf4c77821465a86e88e1b9e5a138acf061f
- Size of remote file:
- 171 MB
- SHA256:
- 8f54cc3ddc27e399ec85ad80f0788bafe95cfd27822a719cb9c81782ab5b1bc7
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