Instructions to use ProbeX/Model-J__ResNet__model_idx_0089 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_0089 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_0089") 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_0089") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0089", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1277b425c6eaf161441ba1f8cb8a9239286a58bec6e052bcac1c90c2eec0de8
- Size of remote file:
- 171 MB
- SHA256:
- 65e82b481318f11f32f38e16284dccf4a6622a5ec63f9c83236a46d4b8eaed9a
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