Instructions to use Taki3d/CrackDetectionLowRes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taki3d/CrackDetectionLowRes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Taki3d/CrackDetectionLowRes") 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("Taki3d/CrackDetectionLowRes") model = AutoModelForImageClassification.from_pretrained("Taki3d/CrackDetectionLowRes") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - image-classification | |
| - vision | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: CrackDetectionLowRes | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: imagefolder | |
| type: imagefolder | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9940476190476191 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # CrackDetectionLowRes | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0183 | |
| - Accuracy: 0.9940 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 1337 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Accuracy | Validation Loss | | |
| |:-------------:|:-----:|:----:|:--------:|:---------------:| | |
| | 0.0126 | 1.0 | 992 | 0.9879 | 0.0344 | | |
| | 0.0788 | 2.0 | 1904 | 0.9933 | 0.0220 | | |
| | 0.1336 | 3.0 | 2856 | 0.9933 | 0.0222 | | |
| | 0.0066 | 4.0 | 3808 | 0.9933 | 0.0190 | | |
| | 0.0528 | 5.0 | 4760 | 0.9940 | 0.0183 | | |
| ### Framework versions | |
| - Transformers 4.31.0.dev0 | |
| - Pytorch 2.0.1+cpu | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |