Instructions to use ProbeX/Model-J__ResNet__model_idx_0286 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_0286 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_0286") 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_0286") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0286", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b9a07b118c9131d98641c90b5c8177404d0ad2facae336ad26c7b4b8e1952da
- Size of remote file:
- 171 MB
- SHA256:
- d1ea7ed05db3bd76431284087b2ca305b13a8acfed3a81aeee53e0abb9bb81a6
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