Instructions to use ProbeX/Model-J__ResNet__model_idx_0990 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_0990 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_0990") 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_0990") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0990") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25fd917b81b6a51c393cf0fb06c05f4f8ac17806072a5c1e502f912ef78b3376
- Size of remote file:
- 5.37 kB
- SHA256:
- f1909296ec027a5c23c9d0275a3e09e7207371fa7924326fe7dddb0bd6891856
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