Instructions to use ProbeX/Model-J__ResNet__model_idx_0465 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_0465 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_0465") 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_0465") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0465", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03c92e6c42420ba6b821b20c1a569ae015fb8d0b06f1859c7446bfcf3f870f2a
- Size of remote file:
- 171 MB
- SHA256:
- 73afe420678f318524d6fbe5ed5b816f961b8eac7250f04fb92ea06a5bb23c6d
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