Instructions to use ProbeX/Model-J__ResNet__model_idx_0771 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_0771 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_0771") 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_0771") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0771", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 331f8fec7a44555ab812df1d9574a231f04f1c78be38d83e268498aaaf35830c
- Size of remote file:
- 171 MB
- SHA256:
- 5f0b92bfd80a3de555bb570d41d508bedda6f4cbece67cc541d5d48e49f90ff1
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