Instructions to use ProbeX/Model-J__ResNet__model_idx_0078 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_0078 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_0078") 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_0078") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0078", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d649e6d5eecefc30dfb1c26a9bae50967d06733b53cb3388a5ba3efb1b372f43
- Size of remote file:
- 5.37 kB
- SHA256:
- 583f854919fafb1f9edc929ef5cca9d9c6a5f41f73468164a233f6d465420095
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