Instructions to use ProbeX/Model-J__ResNet__model_idx_0770 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_0770 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_0770") 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_0770") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0770", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39cb12c1b8770eaab5e033c85b5a554557522e16cef1959a321c201d7b9c0fe9
- Size of remote file:
- 171 MB
- SHA256:
- 4245f957708cdb99cc23d1c7637990b4d11bbac6d5da2894cf180dd59896f730
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