Instructions to use ProbeX/Model-J__ResNet__model_idx_0322 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_0322 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_0322") 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_0322") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0322") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9676efbf8c4816fd070746e69364474ca2a2e6d459c6909d36f7c37c40f565fe
- Size of remote file:
- 5.37 kB
- SHA256:
- 9c8c524474ff9530e309746ac88ee8c02ae72d9396c8939a9e46d9baea0b35e2
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