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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segmentation_model_test
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. -->
# segmentation_model_test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0228
- Mean Iou: 0.4990
- Mean Accuracy: 0.5000
- Overall Accuracy: 0.9977
- Per Category Iou: [0.9976658568580316, 0.00038483740619588223]
- Per Category Accuracy: [0.9996102929714499, 0.00046175157765122367]
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:|
| 0.2991 | 0.2439 | 20 | 0.4820 | 0.4511 | 0.5010 | 0.9002 | [0.9002190670776196, 0.0019511332665075433] | [0.9017984806798052, 0.10021548406957057] |
| 0.2151 | 0.4878 | 40 | 0.2171 | 0.4792 | 0.5054 | 0.9560 | [0.9559757000209818, 0.0023380252240607505] | [0.9577409321311929, 0.05300908111436047] |
| 0.1517 | 0.7317 | 60 | 0.1387 | 0.4839 | 0.5006 | 0.9659 | [0.9658788431968783, 0.0019119587675839745] | [0.9676989446127423, 0.033584731414499] |
| 0.1488 | 0.9756 | 80 | 0.1050 | 0.4909 | 0.4991 | 0.9802 | [0.9801804275352807, 0.0015904464567297464] | [0.982060661522954, 0.01622287209481299] |
| 0.0917 | 1.2195 | 100 | 0.0828 | 0.4924 | 0.5034 | 0.9823 | [0.9822702579449976, 0.0024875496119523724] | [0.9841420602017377, 0.022718177620440202] |
| 0.0998 | 1.4634 | 120 | 0.0917 | 0.4915 | 0.5004 | 0.9812 | [0.981240710481487, 0.0018249920306024864] | [0.9831202984930748, 0.017623518547021703] |
| 0.1005 | 1.7073 | 140 | 0.0599 | 0.4979 | 0.4990 | 0.9956 | [0.9955563366152819, 0.0002021822200955648] | [0.9974966613047062, 0.00046175157765122367] |
| 0.0662 | 1.9512 | 160 | 0.0757 | 0.4970 | 0.4981 | 0.9939 | [0.9938520901192224, 0.00014614399080267152] | [0.9957890932572371, 0.00046175157765122367] |
| 0.0564 | 2.1951 | 180 | 0.0590 | 0.4982 | 0.4993 | 0.9959 | [0.9959318937190048, 0.00037537537537537537] | [0.9978723226736982, 0.0007849776820070802] |
| 0.0506 | 2.4390 | 200 | 0.0426 | 0.4989 | 0.4999 | 0.9974 | [0.9974019346864065, 0.00034575755480257246] | [0.9993458564193857, 0.00046175157765122367] |
| 0.0497 | 2.6829 | 220 | 0.0463 | 0.4983 | 0.4994 | 0.9964 | [0.9964040866211079, 0.0002498355249460772] | [0.998346063562852, 0.00046175157765122367] |
| 0.0397 | 2.9268 | 240 | 0.0500 | 0.4984 | 0.4995 | 0.9966 | [0.9966042672899276, 0.00026455959645842886] | [0.9985466343808574, 0.00046175157765122367] |
| 0.0382 | 3.1707 | 260 | 0.0461 | 0.4983 | 0.4994 | 0.9964 | [0.9963626606350271, 0.00024699082840723847] | [0.9983045568381324, 0.00046175157765122367] |
| 0.0369 | 3.4146 | 280 | 0.0407 | 0.4987 | 0.4998 | 0.9972 | [0.9971528096493613, 0.0003155138142465004] | [0.9990962458412993, 0.00046175157765122367] |
| 0.0291 | 3.6585 | 300 | 0.0350 | 0.4987 | 0.4997 | 0.9971 | [0.9970553103111885, 0.00030507026785169515] | [0.9989985564783473, 0.00046175157765122367] |
| 0.0438 | 3.9024 | 320 | 0.0440 | 0.4987 | 0.4998 | 0.9971 | [0.9971233951024839, 0.000312288554624473] | [0.9990667739659018, 0.00046175157765122367] |
| 0.0277 | 4.1463 | 340 | 0.0346 | 0.4988 | 0.4999 | 0.9974 | [0.9973527207347574, 0.00033933196846474905] | [0.9992965465504671, 0.00046175157765122367] |
| 0.0277 | 4.3902 | 360 | 0.0302 | 0.4989 | 0.5000 | 0.9975 | [0.9975251642646684, 0.0003629676232880027] | [0.999469326170287, 0.00046175157765122367] |
| 0.0255 | 4.6341 | 380 | 0.0330 | 0.4989 | 0.4999 | 0.9975 | [0.9974865539276777, 0.00035739388379933527] | [0.9994306405823162, 0.00046175157765122367] |
| 0.0239 | 4.8780 | 400 | 0.0280 | 0.4990 | 0.5000 | 0.9977 | [0.9976912875609835, 0.0003890747801727492] | [0.9996357732384688, 0.00046175157765122367] |
| 0.0286 | 5.1220 | 420 | 0.0282 | 0.4989 | 0.4999 | 0.9975 | [0.9974798442958035, 0.0003564427018356799] | [0.999423917873468, 0.00046175157765122367] |
| 0.0267 | 5.3659 | 440 | 0.0238 | 0.4991 | 0.5001 | 0.9977 | [0.9977252550723469, 0.00039488232506713] | [0.9996698069520132, 0.00046175157765122367] |
| 0.0186 | 5.6098 | 460 | 0.0299 | 0.4990 | 0.5000 | 0.9976 | [0.9976214654542921, 0.00037765776653196875] | [0.999565815049516, 0.00046175157765122367] |
| 0.0308 | 5.8537 | 480 | 0.0233 | 0.4991 | 0.5001 | 0.9977 | [0.9977203426632961, 0.00039403173268887255] | [0.9996648849687493, 0.00046175157765122367] |
| 0.0303 | 6.0976 | 500 | 0.0238 | 0.4990 | 0.5000 | 0.9976 | [0.9976369515242876, 0.0003801317790167258] | [0.9995813313016347, 0.00046175157765122367] |
| 0.028 | 6.3415 | 520 | 0.0241 | 0.4991 | 0.5001 | 0.9978 | [0.997765872308157, 0.0004020585397233837] | [0.9997105033502199, 0.00046175157765122367] |
| 0.0168 | 6.5854 | 540 | 0.0262 | 0.4990 | 0.5000 | 0.9976 | [0.9976165230915276, 0.0003768749528906309] | [0.999560863054159, 0.00046175157765122367] |
| 0.0186 | 6.8293 | 560 | 0.0268 | 0.4990 | 0.5000 | 0.9976 | [0.9976118503121866, 0.0003761378168961107] | [0.9995561811676397, 0.00046175157765122367] |
| 0.026 | 7.0732 | 580 | 0.0283 | 0.4990 | 0.5000 | 0.9976 | [0.9975737491883292, 0.00037023324694557573] | [0.9995180057852511, 0.00046175157765122367] |
| 0.0208 | 7.3171 | 600 | 0.0267 | 0.4990 | 0.5000 | 0.9975 | [0.9975480489019538, 0.0003663540445486518] | [0.9994922554093947, 0.00046175157765122367] |
| 0.0216 | 7.5610 | 620 | 0.0242 | 0.4990 | 0.5001 | 0.9977 | [0.9977005133048106, 0.0003906351727909581] | [0.9996450169631351, 0.00046175157765122367] |
| 0.0246 | 7.8049 | 640 | 0.0243 | 0.4990 | 0.5000 | 0.9976 | [0.9975747676145958, 0.00037038866116845276] | [0.9995190261964156, 0.00046175157765122367] |
| 0.0199 | 8.0488 | 660 | 0.0243 | 0.4991 | 0.5001 | 0.9977 | [0.9977276813231586, 0.0003953037909633553] | [0.9996722379315521, 0.00046175157765122367] |
| 0.0204 | 8.2927 | 680 | 0.0225 | 0.4990 | 0.5000 | 0.9977 | [0.9976660066266002, 0.0003848620910840282] | [0.9996104430319153, 0.00046175157765122367] |
| 0.021 | 8.5366 | 700 | 0.0242 | 0.4990 | 0.5000 | 0.9976 | [0.997612030034469, 0.00037616611495636473] | [0.9995563612401981, 0.00046175157765122367] |
| 0.0156 | 8.7805 | 720 | 0.0223 | 0.4990 | 0.5000 | 0.9977 | [0.9976639398203532, 0.00038452171906843205] | [0.9996083721974932, 0.00046175157765122367] |
| 0.0226 | 9.0244 | 740 | 0.0218 | 0.4990 | 0.5001 | 0.9977 | [0.997695900432897, 0.0003898534151159164] | [0.9996403951008019, 0.00046175157765122367] |
| 0.0204 | 9.2683 | 760 | 0.0228 | 0.4990 | 0.5000 | 0.9977 | [0.9976748729258625, 0.0003863291008834059] | [0.9996193266114648, 0.00046175157765122367] |
| 0.022 | 9.5122 | 780 | 0.0222 | 0.4990 | 0.5001 | 0.9977 | [0.9976952114974814, 0.0003897369275738876] | [0.9996397048226613, 0.00046175157765122367] |
| 0.0221 | 9.7561 | 800 | 0.0221 | 0.4990 | 0.5000 | 0.9977 | [0.9976826608914309, 0.00038762694782541285] | [0.9996271297556637, 0.00046175157765122367] |
| 0.0203 | 10.0 | 820 | 0.0228 | 0.4990 | 0.5000 | 0.9977 | [0.9976658568580316, 0.00038483740619588223] | [0.9996102929714499, 0.00046175157765122367] |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.0
- Datasets 2.4.0
- Tokenizers 0.20.3
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