Instructions to use ProbeX/Model-J__ResNet__model_idx_0768 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_0768 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_0768") 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_0768") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 467324cba658298e17ff5b78bf7c7cee547da47028b6b1a9a142ff0c5e33c8e7
- Size of remote file:
- 171 MB
- SHA256:
- 7640312ba5cfe79e0ad72ce362dd357bd706d0277e97140b6666dfa39f3dabda
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