Instructions to use ProbeX/Model-J__ResNet__model_idx_0852 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_0852 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_0852") 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_0852") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0852") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a3fec9a0c30eccd0ffaec51d090c245238707ff552c4fd47ba829e95bd31cc4
- Size of remote file:
- 171 MB
- SHA256:
- 242ae0f43328fd29402ae7e8bef08a35c87278ad61c25205b378f7fb3c8a5dd1
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