Instructions to use ProbeX/Model-J__ResNet__model_idx_0261 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_0261 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_0261") 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_0261") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0261") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37cf4d7f5b4e08f232a8bdffa73a0588a13ab94c809c6515c383775a68f69c7d
- Size of remote file:
- 5.37 kB
- SHA256:
- 7755b54322493aa2c7a763aeed58eb65e91183aec1310c4e2ff0a9cba3905375
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