Instructions to use ProbeX/Model-J__ResNet__model_idx_0221 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_0221 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_0221") 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_0221") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0221", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a846f16b81da92e2f9b361ad4a2f57d0552e6c83a6921fb078696053ecba633b
- Size of remote file:
- 171 MB
- SHA256:
- bc7d8de87580c3176dde0410348c6aaaeb2d96404d54d99fe8c7f61115dc0a7b
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