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---
license: other
base_model: nvidia/mit-b3
tags:
- generated_from_trainer
model-index:
- name: segformer_rust
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. -->
# segformer_rust
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1438
- Mean Iou: 0.7943
- Mean Accuracy: 0.8621
- Overall Accuracy: 0.9463
- Per Category Iou: [0.9404316292856895, 0.6481512759943856]
- Per Category Accuracy: [0.9766097421311045, 0.7476136089750051]
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:----------------------------------------:|
| 0.2683 | 1.0 | 514 | 0.1919 | 0.7319 | 0.8203 | 0.9254 | [0.9180806580950376, 0.5456807971830461] | [0.9632417779264837, 0.6772761097220785] |
| 0.2221 | 2.0 | 1028 | 0.1848 | 0.7247 | 0.7906 | 0.9285 | [0.9222502676042136, 0.5272319457425635] | [0.9780221080448669, 0.6032531950921799] |
| 0.2051 | 3.0 | 1542 | 0.1798 | 0.7523 | 0.8476 | 0.9301 | [0.9226057238240142, 0.5819673887381934] | [0.9598172268335441, 0.7353887410800054] |
| 0.1996 | 4.0 | 2056 | 0.1662 | 0.7530 | 0.8151 | 0.9366 | [0.9305887422755489, 0.5753549591140691] | [0.9802446445708194, 0.6499256145141094] |
| 0.1878 | 5.0 | 2570 | 0.1613 | 0.7695 | 0.8443 | 0.9386 | [0.9321977696827667, 0.6067612442121683] | [0.972541727740175, 0.7160658070940397] |
| 0.1848 | 6.0 | 3084 | 0.1579 | 0.7659 | 0.8299 | 0.9395 | [0.9335431699668292, 0.5982432303010027] | [0.9789606327074537, 0.6808199676860336] |
| 0.1739 | 7.0 | 3598 | 0.1543 | 0.7799 | 0.8516 | 0.9419 | [0.9357233119518715, 0.6240100443072397] | [0.9744037993799354, 0.7287987165292924] |
| 0.1742 | 8.0 | 4112 | 0.1607 | 0.7737 | 0.8667 | 0.9369 | [0.9296752366973121, 0.6176824708083143] | [0.9620707480253384, 0.7713870499643393] |
| 0.1631 | 9.0 | 4626 | 0.1553 | 0.7803 | 0.8636 | 0.9402 | [0.9335684843016983, 0.626952054351963] | [0.9678138198093251, 0.7593407854928076] |
| 0.1631 | 10.0 | 5140 | 0.1564 | 0.7679 | 0.8406 | 0.9386 | [0.9322118932460649, 0.6035637849191774] | [0.9738013828280326, 0.7073043940670594] |
| 0.1577 | 11.0 | 5654 | 0.1499 | 0.7836 | 0.8520 | 0.9434 | [0.9373956576514434, 0.6297874494960901] | [0.9763014923228973, 0.7277053858903674] |
| 0.1522 | 12.0 | 6168 | 0.1515 | 0.7781 | 0.8454 | 0.9422 | [0.9361248378060324, 0.6200926437502748] | [0.9769566337905029, 0.7138379976069665] |
| 0.1486 | 13.0 | 6682 | 0.1531 | 0.7766 | 0.8485 | 0.9410 | [0.9347900233191081, 0.618411826576312] | [0.9743083731504567, 0.7226475641364578] |
| 0.146 | 14.0 | 7196 | 0.1568 | 0.7835 | 0.8667 | 0.9412 | [0.9345511743761084, 0.6325140238903502] | [0.9679584694275379, 0.7654769806378111] |
| 0.1453 | 15.0 | 7710 | 0.1485 | 0.7837 | 0.8473 | 0.9442 | [0.938357945982732, 0.6290898844632448] | [0.9790292010707323, 0.7156414504087399] |
| 0.1439 | 16.0 | 8224 | 0.1492 | 0.7896 | 0.8623 | 0.9444 | [0.9382510027549076, 0.6409378390750833] | [0.973914066583286, 0.7506284726295933] |
| 0.1385 | 17.0 | 8738 | 0.1494 | 0.7790 | 0.8369 | 0.9439 | [0.938230866841315, 0.6196920559815114] | [0.9823649345853678, 0.6913889357135595] |
| 0.1375 | 18.0 | 9252 | 0.1515 | 0.7847 | 0.8549 | 0.9435 | [0.9373782356302373, 0.6321131725666339] | [0.975314658131637, 0.7344851844673589] |
| 0.1353 | 19.0 | 9766 | 0.1450 | 0.7929 | 0.8610 | 0.9459 | [0.9399812074698386, 0.6457742806194801] | [0.9764190916833184, 0.7456795800307741] |
| 0.1317 | 20.0 | 10280 | 0.1453 | 0.7906 | 0.8584 | 0.9453 | [0.9394006951099269, 0.6417152113828789] | [0.9765905703089925, 0.7402706100619615] |
| 0.1292 | 21.0 | 10794 | 0.1565 | 0.7788 | 0.8416 | 0.9431 | [0.9372341895291594, 0.6204583221235739] | [0.9795794314033889, 0.7035825637871398] |
| 0.1284 | 22.0 | 11308 | 0.1487 | 0.7879 | 0.8532 | 0.9450 | [0.9391595949869292, 0.6366277680932542] | [0.9780565365202, 0.7282789363894756] |
| 0.1279 | 23.0 | 11822 | 0.1461 | 0.7927 | 0.8629 | 0.9456 | [0.9395641382795358, 0.6458609695526066] | [0.9752807298744423, 0.7506032283294566] |
| 0.1262 | 24.0 | 12336 | 0.1436 | 0.7934 | 0.8633 | 0.9458 | [0.9398055111519206, 0.6469847970011983] | [0.9754426177792707, 0.7512221554580607] |
| 0.1223 | 25.0 | 12850 | 0.1465 | 0.7945 | 0.8622 | 0.9464 | [0.9404797675363489, 0.6484203969264617] | [0.9766238155760367, 0.7478641586538628] |
| 0.1234 | 26.0 | 13364 | 0.1435 | 0.7925 | 0.8570 | 0.9463 | [0.9405453729701521, 0.6444525239879496] | [0.9784625404961454, 0.735513574144182] |
| 0.1223 | 27.0 | 13878 | 0.1464 | 0.7937 | 0.8618 | 0.9461 | [0.9402290147962161, 0.6472276210560935] | [0.976462283595653, 0.7471743581526247] |
| 0.1196 | 28.0 | 14392 | 0.1450 | 0.7929 | 0.8589 | 0.9462 | [0.9403789254222912, 0.645380249186489] | [0.9776359685007636, 0.7400721898628863] |
| 0.1199 | 29.0 | 14906 | 0.1451 | 0.7919 | 0.8563 | 0.9462 | [0.9403753807784087, 0.6433302809940281] | [0.9784796056303284, 0.7341607320998506] |
| 0.1208 | 30.0 | 15420 | 0.1438 | 0.7943 | 0.8621 | 0.9463 | [0.9404316292856895, 0.6481512759943856] | [0.9766097421311045, 0.7476136089750051] |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|