Instructions to use ProbeX/Model-J__ResNet__model_idx_0977 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_0977 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_0977") 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_0977") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0977", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edc7cf1bb406b8f2fddb98df636e5f1cf5b4494c9f75f2a92c41bf79fd4646b4
- Size of remote file:
- 171 MB
- SHA256:
- b3f1c1a7cd89a9f2435f1e70ddab1489840b75e82eeb7d10f76cc764eb99d8c6
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