Instructions to use ProbeX/Model-J__ResNet__model_idx_0525 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_0525 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_0525") 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_0525") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0525") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb5d3f6b0846e49850c2de4c3b57714d9baad1546cbd219d063ea89b3cdb1d77
- Size of remote file:
- 5.37 kB
- SHA256:
- af97b985eb8c9ea7a1f14362aa4fb83c37d9941e5fbb7fb3ea48c7cdc8d20459
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