| --- |
| license: other |
| base_model: nvidia/mit-b5 |
| tags: |
| - image-segmentation |
| - vision |
| - generated_from_trainer |
| model-index: |
| - name: FINAL_ecc_segformer |
| 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. --> |
|
|
| # FINAL_ecc_segformer |
|
|
| This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_exhaustive dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0749 |
| - Mean Iou: 0.1968 |
| - Mean Accuracy: 0.3939 |
| - Overall Accuracy: 0.3939 |
| - Accuracy Background: nan |
| - Accuracy Crack: 0.3939 |
| - Iou Background: 0.0 |
| - Iou Crack: 0.3936 |
| |
| ## 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: 6e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 1337 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: polynomial |
| - training_steps: 10000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| |
| | 0.0534 | 1.0 | 548 | 0.0614 | 0.1368 | 0.2750 | 0.2750 | nan | 0.2750 | 0.0 | 0.2736 | |
| | 0.058 | 2.0 | 1096 | 0.1018 | 0.2093 | 0.4238 | 0.4238 | nan | 0.4238 | 0.0 | 0.4186 | |
| | 0.0482 | 3.0 | 1644 | 0.0508 | 0.1791 | 0.4315 | 0.4315 | nan | 0.4315 | 0.0 | 0.3582 | |
| | 0.0338 | 4.0 | 2192 | 0.0569 | 0.1849 | 0.3716 | 0.3716 | nan | 0.3716 | 0.0 | 0.3698 | |
| | 0.0395 | 5.0 | 2740 | 0.0597 | 0.1745 | 0.3506 | 0.3506 | nan | 0.3506 | 0.0 | 0.3490 | |
| | 0.0372 | 6.0 | 3288 | 0.0509 | 0.2298 | 0.4635 | 0.4635 | nan | 0.4635 | 0.0 | 0.4597 | |
| | 0.0402 | 7.0 | 3836 | 0.0620 | 0.1751 | 0.3507 | 0.3507 | nan | 0.3507 | 0.0 | 0.3503 | |
| | 0.038 | 8.0 | 4384 | 0.0681 | 0.1905 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3810 | |
| | 0.0393 | 9.0 | 4932 | 0.0685 | 0.2213 | 0.4433 | 0.4433 | nan | 0.4433 | 0.0 | 0.4425 | |
| | 0.0376 | 10.0 | 5480 | 0.0590 | 0.1962 | 0.3929 | 0.3929 | nan | 0.3929 | 0.0 | 0.3924 | |
| | 0.0381 | 11.0 | 6028 | 0.0626 | 0.1891 | 0.3801 | 0.3801 | nan | 0.3801 | 0.0 | 0.3783 | |
| | 0.034 | 12.0 | 6576 | 0.0623 | 0.2061 | 0.4162 | 0.4162 | nan | 0.4162 | 0.0 | 0.4122 | |
| | 0.0301 | 13.0 | 7124 | 0.0831 | 0.1832 | 0.3669 | 0.3669 | nan | 0.3669 | 0.0 | 0.3664 | |
| | 0.034 | 14.0 | 7672 | 0.0636 | 0.2059 | 0.4119 | 0.4119 | nan | 0.4119 | 0.0 | 0.4118 | |
| | 0.0303 | 15.0 | 8220 | 0.0705 | 0.1931 | 0.3864 | 0.3864 | nan | 0.3864 | 0.0 | 0.3862 | |
| | 0.0338 | 16.0 | 8768 | 0.0685 | 0.2101 | 0.4206 | 0.4206 | nan | 0.4206 | 0.0 | 0.4202 | |
| | 0.0229 | 17.0 | 9316 | 0.0706 | 0.2099 | 0.4204 | 0.4204 | nan | 0.4204 | 0.0 | 0.4197 | |
| | 0.0337 | 18.0 | 9864 | 0.0742 | 0.1982 | 0.3968 | 0.3968 | nan | 0.3968 | 0.0 | 0.3965 | |
| | 0.0257 | 18.25 | 10000 | 0.0749 | 0.1968 | 0.3939 | 0.3939 | nan | 0.3939 | 0.0 | 0.3936 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.1 |
| - Pytorch 2.1.0+cpu |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
| |
