Instructions to use ProbeX/Model-J__ResNet__model_idx_0159 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_0159 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_0159") 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_0159") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0159", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 274203df8c2af37ac7c30fff58dd165be89f2f64db183e9b4282277bbe6ebc06
- Size of remote file:
- 171 MB
- SHA256:
- 228bb43c2ba862cec72096453f46ba7df415de0cad6503d023bfcc2e5db1b5b4
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