Instructions to use ProbeX/Model-J__ResNet__model_idx_0395 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_0395 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_0395") 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_0395") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0395", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8f95f8aaa3daff0c785f30aab7301563c38a5eab0acbcdaa7194c48ea36344d
- Size of remote file:
- 5.37 kB
- SHA256:
- ec1d5a0ea7fffcc6272a9e11485fea902d48dc86185ebcc53f7a159b0206663f
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