Instructions to use ProbeX/Model-J__ResNet__model_idx_0638 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_0638 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_0638") 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_0638") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0638", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c31a0a4280bf0301300cae9d3ee989af2d9d322bb30c4d39bcffed186be6709e
- Size of remote file:
- 171 MB
- SHA256:
- bbfdcff5244cc4b7021e239e1c5b7c513dd44c939c76ec97a7b1fcc6b2e3a6d9
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