Instructions to use ProbeX/Model-J__ResNet__model_idx_0177 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_0177 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_0177") 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_0177") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0177", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09d1fdc4515c8239c69a6038f5058e77569b4edaca8d2841485d1e1f3724117e
- Size of remote file:
- 171 MB
- SHA256:
- 4436f61f9574f89ca84b61ab838f4285500d0ac50950bd619b713cd4f345e17e
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