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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 10.0, | |
| "test_accuracy": 0.8245614035087719, | |
| "test_defect_f1": 0.9019607843137255, | |
| "test_defect_precision": 0.8518518518518519, | |
| "test_defect_recall": 0.9583333333333334, | |
| "test_loss": 0.7126850485801697, | |
| "test_runtime": 3.2797, | |
| "test_samples_per_second": 17.38, | |
| "test_steps_per_second": 1.22 | |
| } |