Instructions to use ProbeX/Model-J__ResNet__model_idx_0052 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_0052 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_0052") 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_0052") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0052", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3997043d21d529d49f608e1100d059a5612126adfd0b554ba6d8c27b4f95666
- Size of remote file:
- 171 MB
- SHA256:
- 23aefce6614eeead25acdab354b9b5de598e19e9b6df4856add0b65f014be866
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