Instructions to use ProbeX/Model-J__ResNet__model_idx_0128 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_0128 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_0128") 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_0128") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ae00593eabb09bd6a8005780191278367ee13ce8132755147ffafe3817031d1
- Size of remote file:
- 5.37 kB
- SHA256:
- a75bb9aced0a2c9f0d4908ab91ff48fbe691eccc3bd55e13cc3e7112c4dea40c
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