Image Classification
Transformers
Safetensors
vit
vision
defect-detection
manufacturing-quality-control
Generated from Trainer
Eval Results (legacy)
Instructions to use Dongjin1203/defect-classifier-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dongjin1203/defect-classifier-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dongjin1203/defect-classifier-vit-base") 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("Dongjin1203/defect-classifier-vit-base") model = AutoModelForImageClassification.from_pretrained("Dongjin1203/defect-classifier-vit-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b67c5dce0f3d71e409c60ee89b537519b6a70c23e929f2795c9f88ab73f1e5f
- Size of remote file:
- 5.33 kB
- SHA256:
- d4a15a4ee911828ff9a9d80e355f4fed9ee503ccfa8cc7e1b73c1c5a92b2cc6f
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