Instructions to use ProbeX/Model-J__ResNet__model_idx_0886 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_0886 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_0886") 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_0886") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0886", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc81f828d2ecbd9422756e2cc0526d4f095a40e999c63920ae31993fb1331120
- Size of remote file:
- 171 MB
- SHA256:
- 4144ce1f5be0563c4241c520367755f1d515628c516a9aeec2e465df0792dc0a
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