| --- |
| license: other |
| base_model: nvidia/mit-b5 |
| tags: |
| - image-segmentation |
| - vision |
| - generated_from_trainer |
| model-index: |
| - name: ecc_segformerv3 |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # ecc_segformerv3 |
| |
| This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1344 |
| - Mean Iou: 0.0005 |
| - Mean Accuracy: 0.0010 |
| - Overall Accuracy: 0.0010 |
| - Accuracy Background: nan |
| - Accuracy Crack: 0.0010 |
| - Iou Background: 0.0 |
| - Iou Crack: 0.0010 |
| |
| ## 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: 0.0006 |
| - train_batch_size: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 5000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| |
| | 0.1306 | 1.0 | 1001 | 0.1114 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | |
| | 0.107 | 2.0 | 2002 | 0.1238 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | |
| | 0.1285 | 3.0 | 3003 | 0.1631 | 0.0024 | 0.0049 | 0.0049 | nan | 0.0049 | 0.0 | 0.0048 | |
| | 0.0887 | 4.0 | 4004 | 0.1083 | 0.0002 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 | |
| | 0.0828 | 5.0 | 5000 | 0.1344 | 0.0005 | 0.0010 | 0.0010 | nan | 0.0010 | 0.0 | 0.0010 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.32.0.dev0 |
| - Pytorch 2.0.1+cpu |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
