Instructions to use ProbeX/Model-J__ResNet__model_idx_0371 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_0371 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_0371") 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_0371") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0371") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57d2b876d2a62361987e8394f7046cb365b8e17ccf445fbb655c965f553d25e4
- Size of remote file:
- 5.37 kB
- SHA256:
- e60ee8049f2f4804954d9d8c613789d526928ae2362e1d051d0519d158c8312f
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