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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: earthquake
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. -->
# earthquake
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the gokceKy/earthquake dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2868
- Mean Iou: 0.2379
- Mean Accuracy: 0.2922
- Overall Accuracy: 0.5492
- Accuracy Background: nan
- Accuracy Car: 0.0
- Accuracy Earthquake-roads: 0.4334
- Accuracy Other: 0.1291
- Accuracy Road: 0.6209
- Accuracy Road-cracks: 0.0
- Accuracy Sky: 0.8620
- Accuracy Wall: 0.0
- Iou Background: 0.0
- Iou Car: 0.0
- Iou Earthquake-roads: 0.3329
- Iou Other: 0.1229
- Iou Road: 0.5861
- Iou Road-cracks: 0.0
- Iou Sky: 0.8615
- Iou Wall: 0.0
## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Car | Accuracy Earthquake-roads | Accuracy Other | Accuracy Road | Accuracy Road-cracks | Accuracy Sky | Accuracy Wall | Iou Background | Iou Car | Iou Earthquake-roads | Iou Other | Iou Road | Iou Road-cracks | Iou Sky | Iou Wall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------------------:|:--------------:|:-------------:|:--------------------:|:------------:|:-------------:|:--------------:|:-------:|:--------------------:|:---------:|:--------:|:---------------:|:-------:|:--------:|
| 2.0651 | 0.1316 | 5 | 2.0768 | 0.0374 | 0.1237 | 0.1091 | nan | 0.0 | 0.6207 | 0.2450 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1556 | 0.1438 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8605 | 0.2632 | 10 | 2.0532 | 0.0563 | 0.1524 | 0.2410 | nan | 0.0 | 0.7513 | 0.1438 | 0.1716 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2042 | 0.0782 | 0.1683 | 0.0 | 0.0 | 0.0 |
| 1.7033 | 0.3947 | 15 | 2.0117 | 0.0892 | 0.1338 | 0.3878 | nan | 0.0 | 0.4356 | 0.0185 | 0.4531 | 0.0 | 0.0296 | 0.0 | 0.0 | 0.0 | 0.2332 | 0.0152 | 0.4359 | 0.0 | 0.0296 | 0.0 |
| 1.6843 | 0.5263 | 20 | 1.9639 | 0.1220 | 0.2025 | 0.4637 | nan | 0.0 | 0.6874 | 0.0325 | 0.4990 | 0.0 | 0.1983 | 0.0 | 0.0 | 0.0 | 0.2913 | 0.0309 | 0.4793 | 0.0 | 0.1748 | 0.0 |
| 1.7298 | 0.6579 | 25 | 1.8835 | 0.1234 | 0.1819 | 0.5114 | nan | 0.0 | 0.6336 | 0.0555 | 0.5840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3818 | 0.0542 | 0.5516 | 0.0 | 0.0 | 0.0 |
| 1.4833 | 0.7895 | 30 | 1.7512 | 0.1555 | 0.2118 | 0.6238 | nan | 0.0 | 0.6148 | 0.1323 | 0.7352 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4310 | 0.1293 | 0.6841 | 0.0 | 0.0 | 0.0 |
| 1.5078 | 0.9211 | 35 | 1.6947 | 0.2232 | 0.3260 | 0.6338 | nan | 0.0 | 0.6497 | 0.4078 | 0.6786 | 0.0 | 0.5459 | 0.0 | 0.0 | 0.0 | 0.4381 | 0.3286 | 0.6399 | 0.0 | 0.3788 | 0.0 |
| 1.2623 | 1.0526 | 40 | 1.6373 | 0.2083 | 0.3221 | 0.5803 | nan | 0.0 | 0.6784 | 0.3435 | 0.6017 | 0.0 | 0.6309 | 0.0 | 0.0 | 0.0 | 0.4245 | 0.2883 | 0.5717 | 0.0 | 0.3819 | 0.0 |
| 1.124 | 1.1842 | 45 | 1.4797 | 0.2175 | 0.3017 | 0.6133 | nan | 0.0 | 0.6023 | 0.2190 | 0.6795 | 0.0 | 0.6111 | 0.0 | 0.0 | 0.0 | 0.4482 | 0.2035 | 0.6431 | 0.0 | 0.4454 | 0.0 |
| 1.1958 | 1.3158 | 50 | 1.3304 | 0.2445 | 0.3053 | 0.6530 | nan | 0.0 | 0.5340 | 0.2660 | 0.7434 | 0.0 | 0.5935 | 0.0 | 0.0 | 0.0 | 0.4644 | 0.2522 | 0.6977 | 0.0 | 0.5413 | 0.0 |
| 1.6012 | 1.4474 | 55 | 1.2994 | 0.2460 | 0.3082 | 0.6439 | nan | 0.0 | 0.5639 | 0.3192 | 0.7202 | 0.0 | 0.5538 | 0.0 | 0.0 | 0.0 | 0.4683 | 0.3049 | 0.6765 | 0.0 | 0.5182 | 0.0 |
| 0.8773 | 1.5789 | 60 | 1.3010 | 0.2476 | 0.3222 | 0.6103 | nan | 0.0 | 0.6028 | 0.3035 | 0.6603 | 0.0 | 0.6891 | 0.0 | 0.0 | 0.0 | 0.4475 | 0.2968 | 0.6193 | 0.0 | 0.6169 | 0.0 |
| 1.2279 | 1.7105 | 65 | 1.4635 | 0.1929 | 0.3179 | 0.4692 | nan | 0.0 | 0.6939 | 0.2394 | 0.4448 | 0.0 | 0.8469 | 0.0 | 0.0 | 0.0 | 0.3517 | 0.2280 | 0.4246 | 0.0 | 0.5389 | 0.0 |
| 1.2859 | 1.8421 | 70 | 1.4540 | 0.2030 | 0.2891 | 0.4708 | nan | 0.0 | 0.5973 | 0.1369 | 0.4808 | 0.0 | 0.8089 | 0.0 | 0.0 | 0.0 | 0.4248 | 0.1346 | 0.4546 | 0.0 | 0.6098 | 0.0 |
| 1.6452 | 1.9737 | 75 | 1.4244 | 0.2336 | 0.3038 | 0.5933 | nan | 0.0 | 0.5942 | 0.1817 | 0.6531 | 0.0 | 0.6979 | 0.0 | 0.0 | 0.0 | 0.4350 | 0.1742 | 0.6117 | 0.0 | 0.6476 | 0.0 |
| 0.9851 | 2.1053 | 80 | 1.2722 | 0.2418 | 0.3033 | 0.6973 | nan | 0.0 | 0.4978 | 0.0674 | 0.8303 | 0.0 | 0.7273 | 0.0 | 0.0 | 0.0 | 0.4238 | 0.0668 | 0.7747 | 0.0 | 0.6693 | 0.0 |
| 0.9787 | 2.2368 | 85 | 1.2686 | 0.2403 | 0.2910 | 0.6700 | nan | 0.0 | 0.4831 | 0.0065 | 0.8018 | 0.0 | 0.7458 | 0.0 | 0.0 | 0.0 | 0.4308 | 0.0064 | 0.7577 | 0.0 | 0.7276 | 0.0 |
| 0.8809 | 2.3684 | 90 | 1.2555 | 0.2511 | 0.3109 | 0.6567 | nan | 0.0 | 0.6051 | 0.0292 | 0.7541 | 0.0 | 0.7878 | 0.0 | 0.0 | 0.0 | 0.5059 | 0.0288 | 0.7246 | 0.0 | 0.7497 | 0.0 |
| 1.0033 | 2.5 | 95 | 1.2597 | 0.2597 | 0.3421 | 0.6828 | nan | 0.0 | 0.7784 | 0.0607 | 0.7517 | 0.0 | 0.8040 | 0.0 | 0.0 | 0.0 | 0.5432 | 0.0594 | 0.7306 | 0.0 | 0.7441 | 0.0 |
| 0.9275 | 2.6316 | 100 | 1.2301 | 0.2695 | 0.3516 | 0.7156 | nan | 0.0 | 0.7590 | 0.1137 | 0.7954 | 0.0 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.5258 | 0.1102 | 0.7662 | 0.0 | 0.7541 | 0.0 |
| 0.8305 | 2.7632 | 105 | 1.1418 | 0.2914 | 0.3563 | 0.7436 | nan | 0.0 | 0.5627 | 0.2494 | 0.8540 | 0.0 | 0.8279 | 0.0 | 0.0 | 0.0 | 0.4717 | 0.2316 | 0.8070 | 0.0 | 0.8207 | 0.0 |
| 1.4194 | 2.8947 | 110 | 1.2171 | 0.2577 | 0.3141 | 0.6741 | nan | 0.0 | 0.3442 | 0.2353 | 0.8020 | 0.0 | 0.8172 | 0.0 | 0.0 | 0.0 | 0.3208 | 0.2179 | 0.7558 | 0.0 | 0.7671 | 0.0 |
| 0.9159 | 3.0263 | 115 | 1.5539 | 0.1992 | 0.2485 | 0.3971 | nan | 0.0 | 0.3474 | 0.1528 | 0.4246 | 0.0 | 0.8146 | 0.0 | 0.0 | 0.0 | 0.2509 | 0.1430 | 0.4104 | 0.0 | 0.7895 | 0.0 |
| 0.8513 | 3.1579 | 120 | 1.6732 | 0.1939 | 0.2713 | 0.3645 | nan | 0.0 | 0.6345 | 0.1103 | 0.3273 | 0.0 | 0.8272 | 0.0 | 0.0 | 0.0 | 0.3363 | 0.1081 | 0.3223 | 0.0 | 0.7842 | 0.0 |
| 0.655 | 3.2895 | 125 | 1.3317 | 0.2348 | 0.3000 | 0.5930 | nan | 0.0 | 0.4572 | 0.1112 | 0.6802 | 0.0 | 0.8512 | 0.0 | 0.0 | 0.0 | 0.3833 | 0.1091 | 0.6360 | 0.0 | 0.7501 | 0.0 |
| 1.1348 | 3.4211 | 130 | 1.2073 | 0.2544 | 0.3232 | 0.6840 | nan | 0.0 | 0.5067 | 0.0727 | 0.8010 | 0.0 | 0.8817 | 0.0 | 0.0 | 0.0 | 0.4064 | 0.0715 | 0.7402 | 0.0 | 0.8169 | 0.0 |
| 1.0625 | 3.5526 | 135 | 1.2138 | 0.2499 | 0.3055 | 0.6480 | nan | 0.0 | 0.4694 | 0.0793 | 0.7599 | 0.0 | 0.8299 | 0.0 | 0.0 | 0.0 | 0.3803 | 0.0778 | 0.7140 | 0.0 | 0.8275 | 0.0 |
| 0.9589 | 3.6842 | 140 | 1.3193 | 0.2404 | 0.3097 | 0.6012 | nan | 0.0 | 0.6269 | 0.0484 | 0.6678 | 0.0 | 0.8249 | 0.0 | 0.0 | 0.0 | 0.4164 | 0.0478 | 0.6404 | 0.0 | 0.8189 | 0.0 |
| 1.246 | 3.8158 | 145 | 1.3526 | 0.2097 | 0.2537 | 0.5136 | nan | 0.0 | 0.3748 | 0.0373 | 0.5993 | 0.0 | 0.7645 | 0.0 | 0.0 | 0.0 | 0.3062 | 0.0366 | 0.5704 | 0.0 | 0.7645 | 0.0 |
| 0.7836 | 3.9474 | 150 | 1.3824 | 0.1970 | 0.2338 | 0.4868 | nan | 0.0 | 0.2695 | 0.0250 | 0.5844 | 0.0 | 0.7579 | 0.0 | 0.0 | 0.0 | 0.2407 | 0.0248 | 0.5525 | 0.0 | 0.7579 | 0.0 |
| 0.8474 | 4.0789 | 155 | 1.4235 | 0.1998 | 0.2368 | 0.4725 | nan | 0.0 | 0.2587 | 0.0655 | 0.5606 | 0.0 | 0.7724 | 0.0 | 0.0 | 0.0 | 0.2365 | 0.0629 | 0.5275 | 0.0 | 0.7712 | 0.0 |
| 1.0723 | 4.2105 | 160 | 1.4177 | 0.2109 | 0.2510 | 0.4877 | nan | 0.0 | 0.2976 | 0.1024 | 0.5688 | 0.0 | 0.7884 | 0.0 | 0.0 | 0.0 | 0.2681 | 0.0978 | 0.5343 | 0.0 | 0.7868 | 0.0 |
| 1.1283 | 4.3421 | 165 | 1.4844 | 0.2020 | 0.2396 | 0.4298 | nan | 0.0 | 0.2979 | 0.1040 | 0.4875 | 0.0 | 0.7877 | 0.0 | 0.0 | 0.0 | 0.2671 | 0.1007 | 0.4621 | 0.0 | 0.7860 | 0.0 |
| 0.6614 | 4.4737 | 170 | 1.4177 | 0.2111 | 0.2522 | 0.4542 | nan | 0.0 | 0.3361 | 0.1011 | 0.5131 | 0.0 | 0.8151 | 0.0 | 0.0 | 0.0 | 0.2906 | 0.0980 | 0.4868 | 0.0 | 0.8136 | 0.0 |
| 1.0973 | 4.6053 | 175 | 1.5041 | 0.2078 | 0.2573 | 0.4288 | nan | 0.0 | 0.4572 | 0.0763 | 0.4572 | 0.0 | 0.8107 | 0.0 | 0.0 | 0.0 | 0.3406 | 0.0750 | 0.4386 | 0.0 | 0.8081 | 0.0 |
| 0.8756 | 4.7368 | 180 | 1.3542 | 0.2236 | 0.2753 | 0.4929 | nan | 0.0 | 0.4403 | 0.1089 | 0.5448 | 0.0 | 0.8332 | 0.0 | 0.0 | 0.0 | 0.3338 | 0.1051 | 0.5177 | 0.0 | 0.8324 | 0.0 |
| 0.6712 | 4.8684 | 185 | 1.3772 | 0.2232 | 0.2761 | 0.4811 | nan | 0.0 | 0.4525 | 0.1373 | 0.5231 | 0.0 | 0.8195 | 0.0 | 0.0 | 0.0 | 0.3373 | 0.1303 | 0.4993 | 0.0 | 0.8185 | 0.0 |
| 1.2096 | 5.0 | 190 | 1.2868 | 0.2379 | 0.2922 | 0.5492 | nan | 0.0 | 0.4334 | 0.1291 | 0.6209 | 0.0 | 0.8620 | 0.0 | 0.0 | 0.0 | 0.3329 | 0.1229 | 0.5861 | 0.0 | 0.8615 | 0.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.4.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.3
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