Instructions to use ProbeX/Model-J__ResNet__model_idx_0569 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_0569 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_0569") 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_0569") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0569") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fda8866303b3630972bc14eb74e574f26566c6a25f119f4cbcc1fd70558261d9
- Size of remote file:
- 5.37 kB
- SHA256:
- 858438cb3072c70eea784a88198b435db23398d1c3bd5415c4b115f656f14ca0
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