File size: 8,667 Bytes
5fd27b4 27878ad 5fd27b4 27878ad 5fd27b4 71bfc6a 5fd27b4 27878ad 5fd27b4 27878ad 5fd27b4 9493a15 71bfc6a 9493a15 5fd27b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
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: 0.1989
- Mean Iou: 0.4909
- Mean Accuracy: 0.5770
- Overall Accuracy: 0.9351
- Per Category Iou: [0.9330033577422647, 0.43802844257518975, 0.10174998526948427]
- Per Category Accuracy: [0.964764208993714, 0.619516063444351, 0.14676017920088064]
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------:|:---------------------------------------------------------------:|
| 0.3245 | 1.0 | 288 | 0.2058 | 0.3737 | 0.4006 | 0.9292 | [0.928254594835728, 0.19278478772636354, 0.0] | [0.9934403780274128, 0.20834926267819698, 0.0] |
| 0.2376 | 2.0 | 576 | 0.2186 | 0.4440 | 0.5394 | 0.9205 | [0.9174093139660287, 0.3952780826522951, 0.019450366169241784] | [0.9466914641620554, 0.6519016208812702, 0.019680530318053235] |
| 0.2267 | 3.0 | 864 | 0.1989 | 0.4456 | 0.5055 | 0.9349 | [0.932781347474167, 0.4035973409203434, 0.0003156374054605271] | [0.9714340031497313, 0.5447040039563373, 0.0003156655078774732] |
| 0.2227 | 4.0 | 1152 | 0.1916 | 0.4707 | 0.5302 | 0.9370 | [0.9346588332598218, 0.4138838160094419, 0.06341712806343888] | [0.9731864476801606, 0.54656085524886, 0.07078191965098726] |
| 0.2144 | 5.0 | 1440 | 0.1896 | 0.4891 | 0.5724 | 0.9328 | [0.9302444676641086, 0.43202079299936413, 0.10517087813054486] | [0.9610582832573524, 0.6347191782872807, 0.12137743477257919] |
| 0.2003 | 6.0 | 1728 | 0.2024 | 0.4630 | 0.5614 | 0.9260 | [0.9229095306993824, 0.428931900333899, 0.037186726485148515] | [0.9487448882036036, 0.6966673460182111, 0.03891184433643468] |
| 0.1941 | 7.0 | 2016 | 0.2073 | 0.4860 | 0.5888 | 0.9219 | [0.918639874525932, 0.4060024340936176, 0.13340049908214777] | [0.9461779703553824, 0.669512151538538, 0.15057649425124547] |
| 0.1867 | 8.0 | 2304 | 0.1807 | 0.4634 | 0.5301 | 0.9372 | [0.9349180068188713, 0.43659922555690217, 0.018734234161859927] | [0.9689566528873623, 0.602518494621371, 0.018875178573596604] |
| 0.1854 | 9.0 | 2592 | 0.1921 | 0.4725 | 0.5473 | 0.9345 | [0.9321974693686552, 0.43270280485825646, 0.052579663560228966] | [0.9642507962436797, 0.6216561173996741, 0.05587279489431276] |
| 0.18 | 10.0 | 2880 | 0.1847 | 0.4771 | 0.5591 | 0.9335 | [0.9311550048182651, 0.4407180368387474, 0.05928079306817498] | [0.9605858689552134, 0.6528219329451048, 0.06379276154708474] |
| 0.1838 | 11.0 | 3168 | 0.1887 | 0.4652 | 0.5400 | 0.9344 | [0.9321143595621934, 0.43344353554824266, 0.02996913887506222] | [0.9638921692510604, 0.6257803286194064, 0.030457674516485428] |
| 0.1852 | 12.0 | 3456 | 0.1873 | 0.4783 | 0.5588 | 0.9338 | [0.931356267339074, 0.43971228563091297, 0.06380926339455255] | [0.9613249758102673, 0.6471117095414763, 0.06799758799176032] |
| 0.1682 | 13.0 | 3744 | 0.1778 | 0.4578 | 0.5035 | 0.9411 | [0.939396147353813, 0.410371405648735, 0.023504333548772998] | [0.981291057821883, 0.5056175933375897, 0.023695148059264176] |
| 0.1793 | 14.0 | 4032 | 0.1728 | 0.4720 | 0.5296 | 0.9402 | [0.938163188412165, 0.436672912065328, 0.04117603263361246] | [0.974742378495531, 0.5715719389819965, 0.042525809702262676] |
| 0.178 | 15.0 | 4320 | 0.1825 | 0.4794 | 0.5492 | 0.9369 | [0.9348434866894885, 0.42443186583904435, 0.07881529998132747] | [0.9714614165049668, 0.5650121075899666, 0.11103331889905584] |
| 0.1651 | 16.0 | 4608 | 0.1912 | 0.4640 | 0.5322 | 0.9362 | [0.9340976310450665, 0.4306683229566104, 0.02708604107096439] | [0.9679043107574059, 0.6011619809118315, 0.02741028826736059] |
| 0.1761 | 17.0 | 4896 | 0.1757 | 0.4724 | 0.5310 | 0.9389 | [0.9368263731599995, 0.42525112196356074, 0.055183904634890216] | [0.9744989654092739, 0.5559049976770041, 0.06246130062283233] |
| 0.1633 | 18.0 | 5184 | 0.1917 | 0.4620 | 0.5384 | 0.9354 | [0.9330768754070563, 0.44480333780621734, 0.00820601828493007] | [0.9636645135753821, 0.6433031553142516, 0.008227538173267988] |
| 0.1641 | 19.0 | 5472 | 0.1875 | 0.5028 | 0.5812 | 0.9354 | [0.9331706683726091, 0.4415413111697262, 0.133770201971674] | [0.9634674000411962, 0.6392245347005364, 0.1408556154060956] |
| 0.1629 | 20.0 | 5760 | 0.1793 | 0.4675 | 0.5268 | 0.9393 | [0.9374054652680682, 0.4330992564780264, 0.03193276637035433] | [0.973550456833595, 0.5746419640278527, 0.032230257753028166] |
| 0.1562 | 21.0 | 6048 | 0.2019 | 0.4716 | 0.5546 | 0.9325 | [0.9300698286146659, 0.43671042563474527, 0.04809297394799092] | [0.9592354491418064, 0.6554619381223432, 0.049203349291978456] |
| 0.1626 | 22.0 | 6336 | 0.1970 | 0.4814 | 0.5547 | 0.9367 | [0.9344970508696149, 0.4450380854494638, 0.06452712765123607] | [0.9658097423660128, 0.6317005392634606, 0.066633751117982] |
| 0.1568 | 23.0 | 6624 | 0.1956 | 0.4917 | 0.5775 | 0.9355 | [0.9330854195306507, 0.4390415550387406, 0.10283427594052297] | [0.9658042305145758, 0.6119827953416446, 0.15474894474639514] |
| 0.1595 | 24.0 | 6912 | 0.1817 | 0.4769 | 0.5411 | 0.9383 | [0.9362300601150796, 0.4346106438489147, 0.05977008091938032] | [0.9712496965644727, 0.586809439959973, 0.06537513608016285] |
| 0.1539 | 25.0 | 7200 | 0.1893 | 0.4825 | 0.5650 | 0.9358 | [0.933537238237644, 0.4394361715176897, 0.07460055177704666] | [0.9667388784050233, 0.6055558721569863, 0.12278174158326487] |
| 0.1521 | 26.0 | 7488 | 0.1933 | 0.4841 | 0.5721 | 0.9355 | [0.9332085743268924, 0.44365656488299987, 0.07547064782962047] | [0.9657158949896331, 0.6132794230856533, 0.13717285114752506] |
| 0.1468 | 27.0 | 7776 | 0.2049 | 0.4861 | 0.5735 | 0.9327 | [0.9304477363857275, 0.43790721804897553, 0.08980648951011608] | [0.9595730176196706, 0.6520673697794168, 0.10889650623034679] |
| 0.1534 | 28.0 | 8064 | 0.1950 | 0.4976 | 0.5800 | 0.9355 | [0.9333228433994155, 0.4429051769820105, 0.11658642467846855] | [0.9637939610275141, 0.636643256405625, 0.13955248343767832] |
| 0.1478 | 29.0 | 8352 | 0.2049 | 0.4913 | 0.5841 | 0.9344 | [0.9322490633597582, 0.4375642417642195, 0.10407288263883356] | [0.9639606458995019, 0.6190808277013159, 0.169160289279109] |
| 0.1414 | 30.0 | 8640 | 0.1989 | 0.4909 | 0.5770 | 0.9351 | [0.9330033577422647, 0.43802844257518975, 0.10174998526948427] | [0.964764208993714, 0.619516063444351, 0.14676017920088064] |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|