Instructions to use ProbeX/Model-J__ResNet__model_idx_0777 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_0777 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_0777") 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_0777") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0777", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54dc4858f5cea9ba25a0226d82ff6fba7ce259936c89fb3b5b8faf607b296261
- Size of remote file:
- 171 MB
- SHA256:
- 3fa0a56133930a3af7d0e96d4e7fa092e69483b8b2e4501ef2448298f858f1f5
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