Instructions to use ProbeX/Model-J__ResNet__model_idx_0600 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_0600 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_0600") 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_0600") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23393da7e08a0fff8f1946cb5e4b1ff63892ada804bd8e51e9d50f9a36be851b
- Size of remote file:
- 171 MB
- SHA256:
- 0834a199b8cd81b0bb083d05f4cbeb329e856cb7c064df2d49f725b4b491980f
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