Instructions to use ProbeX/Model-J__ResNet__model_idx_0504 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_0504 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_0504") 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_0504") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0504", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acceb9f59e552a0d92bd5587ef1c8e8aee217d2f35f60f3b4d857766c631b849
- Size of remote file:
- 171 MB
- SHA256:
- c3978c0f7f0f867ef6aee6b614a89423acd9d1ddaac5a083cb079b0d175fc600
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