Instructions to use ProbeX/Model-J__ResNet__model_idx_0127 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_0127 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_0127") 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_0127") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0127", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ed5a453a307e80e2ed0f7304d57d5da4c5cd01600dace3b7bb7f853201afe07
- Size of remote file:
- 171 MB
- SHA256:
- 09864e705888fe05014ffbc488e05cac9b130afdaa5753785ad9f5da5e254cb6
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