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--- |
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license: other |
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base_model: nvidia/mit-b3 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: segformer_cracks_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer_cracks_v2 |
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This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0487 |
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- Mean Iou: 0.7770 |
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- Mean Accuracy: 0.8378 |
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- Overall Accuracy: 0.9803 |
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- Per Category Iou: [0.9797338315128605, 0.5741766454919031] |
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- Per Category Accuracy: [0.9922972168222592, 0.683379436844461] |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:----------------------------------------:| |
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| 0.0651 | 1.0 | 1541 | 0.0557 | 0.7543 | 0.8089 | 0.9785 | [0.9779508845325062, 0.5306877773356943] | [0.9928050418870356, 0.6249652426334233] | |
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| 0.0491 | 2.0 | 3082 | 0.0522 | 0.7629 | 0.8195 | 0.9792 | [0.9786365168660656, 0.547127323866261] | [0.9926515789371488, 0.6463984572343288] | |
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| 0.047 | 3.0 | 4623 | 0.0546 | 0.7519 | 0.7995 | 0.9787 | [0.9782301112609909, 0.5255903669562288] | [0.9938779990669149, 0.6050380673194482] | |
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| 0.0456 | 4.0 | 6164 | 0.0510 | 0.7671 | 0.8248 | 0.9795 | [0.9789779157265575, 0.5552760281623849] | [0.9925739681553413, 0.6570897409273347] | |
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| 0.0442 | 5.0 | 7705 | 0.0509 | 0.7714 | 0.8373 | 0.9794 | [0.978875460970978, 0.5639303830124743] | [0.9914357800230804, 0.6831791371245445] | |
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| 0.0439 | 6.0 | 9246 | 0.0502 | 0.7701 | 0.8254 | 0.9799 | [0.9794166328395213, 0.5607787508694683] | [0.9929877378064772, 0.6578722690116804] | |
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| 0.0433 | 7.0 | 10787 | 0.0500 | 0.7738 | 0.8361 | 0.9799 | [0.979329611841869, 0.5681732097856239] | [0.9920131586953994, 0.6802192080134767] | |
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| 0.0427 | 8.0 | 12328 | 0.0499 | 0.7775 | 0.8406 | 0.9802 | [0.9796585654955907, 0.575368007293997] | [0.9919935734749642, 0.6891110848654891] | |
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| 0.0424 | 9.0 | 13869 | 0.0495 | 0.7780 | 0.8411 | 0.9802 | [0.9797042588379468, 0.5763456606457494] | [0.9919931330972266, 0.6902882749336847] | |
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| 0.042 | 10.0 | 15410 | 0.0487 | 0.7770 | 0.8378 | 0.9803 | [0.9797338315128605, 0.5741766454919031] | [0.9922972168222592, 0.683379436844461] | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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