Instructions to use ProbeX/Model-J__ResNet__model_idx_0883 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_0883 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_0883") 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_0883") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0883", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82765059453c391316090d09ce58496d1a0305a6b858679e20b058b9f7af5ce9
- Size of remote file:
- 171 MB
- SHA256:
- cf8941b75e852a43af3553a786576bb433e5da88388c7bd70953e746254ce5e0
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