Instructions to use ProbeX/Model-J__ResNet__model_idx_0630 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_0630 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_0630") 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_0630") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0630", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1f70f9758c339a4d87056f5966c70726d1daf44424e9a59682dd6c645faa515
- Size of remote file:
- 171 MB
- SHA256:
- cbddaeee0f2ddc3b39b71fe1efe0af310ce3b68bc9aff24f6300f084f25ad21b
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