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# segformer-b0-finetuned-busigt2

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the kasumi222/busigt5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2904
- Mean Iou: 0.4458
- Mean Accuracy: 0.6980
- Overall Accuracy: 0.6969
- Per Category Iou: [0.0, 0.6551336334577343, 0.6821319425157643]
- Per Category Accuracy: [nan, 0.6913100552356098, 0.70464740289276]

## 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: 0.00013
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                              | Per Category Accuracy                         |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------:|:---------------------------------------------:|
| 0.1095        | 0.77  | 20   | 0.2086          | 0.4674   | 0.7410        | 0.7419           | [0.0, 0.6978460673452154, 0.704309291034096]  | [nan, 0.7461995349612959, 0.7357650020760118] |
| 0.1156        | 1.54  | 40   | 0.1980          | 0.4186   | 0.6721        | 0.6783           | [0.0, 0.6446507442278364, 0.6112330250576428] | [nan, 0.7089917293749448, 0.635300900559587]  |
| 0.1039        | 2.31  | 60   | 0.1987          | 0.3706   | 0.5810        | 0.5757           | [0.0, 0.5345322994102119, 0.5773860979625277] | [nan, 0.5495831330265778, 0.6123860258526792] |
| 0.0672        | 3.08  | 80   | 0.1960          | 0.4099   | 0.6407        | 0.6439           | [0.0, 0.6194380206711395, 0.6103561290824698] | [nan, 0.6596136450596995, 0.6218662960315686] |
| 0.0992        | 3.85  | 100  | 0.1969          | 0.4201   | 0.6684        | 0.6695           | [0.0, 0.6251984513525223, 0.6351366565306488] | [nan, 0.675036447653713, 0.661700391303438]   |
| 0.085         | 4.62  | 120  | 0.2075          | 0.4383   | 0.6997        | 0.6964           | [0.0, 0.6407576836532538, 0.6742246105299582] | [nan, 0.6804532655724195, 0.718889834811138]  |
| 0.0561        | 5.38  | 140  | 0.2037          | 0.4401   | 0.7033        | 0.7071           | [0.0, 0.6545188689920507, 0.665783897448558]  | [nan, 0.7263735810923504, 0.6801427547189345] |
| 0.0841        | 6.15  | 160  | 0.2119          | 0.3651   | 0.5891        | 0.5934           | [0.0, 0.5494216923933923, 0.5458843877102458] | [nan, 0.6146571565924632, 0.5634664881039569] |
| 0.1034        | 6.92  | 180  | 0.2371          | 0.3684   | 0.6193        | 0.6367           | [0.0, 0.6047004430113216, 0.5003660220404046] | [nan, 0.7229919452156935, 0.5156554415186935] |
| 0.0691        | 7.69  | 200  | 0.2266          | 0.4285   | 0.6991        | 0.7117           | [0.0, 0.6730686627556878, 0.6124621276402561] | [nan, 0.7742042834577688, 0.6240342690621383] |
| 0.0601        | 8.46  | 220  | 0.2106          | 0.4198   | 0.6674        | 0.6704           | [0.0, 0.6308213023617786, 0.6287108585057931] | [nan, 0.6851880267250091, 0.6497046776895365] |
| 0.0647        | 9.23  | 240  | 0.2234          | 0.4229   | 0.6746        | 0.6777           | [0.0, 0.6338885508159525, 0.6349404984513296] | [nan, 0.6928998204597407, 0.6563077167064432] |
| 0.0626        | 10.0  | 260  | 0.2322          | 0.3991   | 0.6540        | 0.6655           | [0.0, 0.6267222060572648, 0.570544858752452]  | [nan, 0.7227113522422911, 0.5852409330048426] |
| 0.0604        | 10.77 | 280  | 0.2021          | 0.4660   | 0.7283        | 0.7288           | [0.0, 0.6990308020264264, 0.6989818924111941] | [nan, 0.7310753774760368, 0.7255727204344536] |
| 0.0573        | 11.54 | 300  | 0.2227          | 0.4513   | 0.7014        | 0.6951           | [0.0, 0.6488805486358904, 0.7049138389320693] | [nan, 0.6638350976679388, 0.7389417956785915] |
| 0.0474        | 12.31 | 320  | 0.2108          | 0.4781   | 0.7468        | 0.7371           | [0.0, 0.6761855871787447, 0.7580093480444655] | [nan, 0.6890590324447889, 0.8044529075728725] |
| 0.0805        | 13.08 | 340  | 0.2257          | 0.4325   | 0.6902        | 0.6940           | [0.0, 0.6550347525850334, 0.6423545682885212] | [nan, 0.7128733309133007, 0.6675247882412931] |
| 0.0545        | 13.85 | 360  | 0.2155          | 0.4609   | 0.7230        | 0.7167           | [0.0, 0.6629649481906197, 0.7196967289093881] | [nan, 0.6853650161390015, 0.7606061073292577] |
| 0.0628        | 14.62 | 380  | 0.2397          | 0.4150   | 0.6561        | 0.6611           | [0.0, 0.6377593821077956, 0.6070948266377257] | [nan, 0.6861969841160831, 0.6259296622984148] |
| 0.0576        | 15.38 | 400  | 0.2177          | 0.4661   | 0.7274        | 0.7272           | [0.0, 0.6936915190759695, 0.7046022162863222] | [nan, 0.7263017649886684, 0.7284576609239519] |
| 0.0808        | 16.15 | 420  | 0.2263          | 0.4248   | 0.6707        | 0.6740           | [0.0, 0.6438773235874202, 0.6304024210524071] | [nan, 0.6904172594111472, 0.6510802419847774] |
| 0.0458        | 16.92 | 440  | 0.2342          | 0.4006   | 0.6449        | 0.6525           | [0.0, 0.6208902028936363, 0.5809796433249929] | [nan, 0.6898132977523129, 0.6000533044931062] |
| 0.0477        | 17.69 | 460  | 0.2683          | 0.3789   | 0.6170        | 0.6232           | [0.0, 0.5741692028709614, 0.5625631837395161] | [nan, 0.6539633266945951, 0.5800762342358019] |
| 0.0501        | 18.46 | 480  | 0.2364          | 0.4280   | 0.6700        | 0.6675           | [0.0, 0.6223049989658083, 0.6617065588280534] | [nan, 0.6552936905824757, 0.6846169180090992] |
| 0.039         | 19.23 | 500  | 0.2378          | 0.4500   | 0.7052        | 0.6986           | [0.0, 0.6391919313721981, 0.7106968345576296] | [nan, 0.665670921345669, 0.7446979100013106]  |
| 0.041         | 20.0  | 520  | 0.2477          | 0.4142   | 0.6612        | 0.6659           | [0.0, 0.6273087938535062, 0.6153514032911991] | [nan, 0.6890233206118104, 0.6333526433632052] |
| 0.0331        | 20.77 | 540  | 0.2488          | 0.4353   | 0.6814        | 0.6778           | [0.0, 0.6267198588955959, 0.6791644212315564] | [nan, 0.6603973431966015, 0.7023153313193633] |
| 0.0316        | 21.54 | 560  | 0.2468          | 0.4500   | 0.7025        | 0.6974           | [0.0, 0.6405571933079939, 0.7093320446678179] | [nan, 0.6719456081313097, 0.7331179494069875] |
| 0.0333        | 22.31 | 580  | 0.2477          | 0.4384   | 0.6899        | 0.6906           | [0.0, 0.6520329743081146, 0.6630535380613215] | [nan, 0.6937796658392771, 0.6860558089232162] |
| 0.0269        | 23.08 | 600  | 0.2603          | 0.4477   | 0.7018        | 0.6996           | [0.0, 0.6514078130357787, 0.6916101875532822] | [nan, 0.6888588892050193, 0.7147725032516842] |
| 0.033         | 23.85 | 620  | 0.2424          | 0.4499   | 0.7061        | 0.6986           | [0.0, 0.6447352671115818, 0.7048670621273163] | [nan, 0.6616131152687708, 0.750523958937919]  |
| 0.0555        | 24.62 | 640  | 0.2471          | 0.4342   | 0.6830        | 0.6823           | [0.0, 0.636756610371055, 0.6659104633164847]  | [nan, 0.6791280033749645, 0.6868014110272018] |
| 0.0583        | 25.38 | 660  | 0.2517          | 0.4434   | 0.6922        | 0.6879           | [0.0, 0.6386719513699022, 0.6913843141331489] | [nan, 0.6666374954624388, 0.7178391636040445] |
| 0.154         | 26.15 | 680  | 0.2535          | 0.4235   | 0.6597        | 0.6487           | [0.0, 0.5750726006840868, 0.695285501846172]  | [nan, 0.5943477194462704, 0.7250215035171054] |
| 0.0292        | 26.92 | 700  | 0.2768          | 0.3679   | 0.6035        | 0.6135           | [0.0, 0.5756677002657924, 0.5279750019379379] | [nan, 0.6631412677700708, 0.5438385402498483] |
| 0.0288        | 27.69 | 720  | 0.2455          | 0.4676   | 0.7235        | 0.7188           | [0.0, 0.6761224569996822, 0.7268002447671437] | [nan, 0.6954373227898398, 0.7515024928661187] |
| 0.0321        | 28.46 | 740  | 0.2618          | 0.4324   | 0.6745        | 0.6691           | [0.0, 0.6201514037000198, 0.6770266576179022] | [nan, 0.6425218048210974, 0.7064552401951121] |
| 0.0309        | 29.23 | 760  | 0.2742          | 0.3944   | 0.6348        | 0.6407           | [0.0, 0.6008533572398147, 0.5822751024176394] | [nan, 0.6701804232440864, 0.599451426280657]  |
| 0.0244        | 30.0  | 780  | 0.2667          | 0.4386   | 0.6819        | 0.6750           | [0.0, 0.6224630782821559, 0.693390305711243]  | [nan, 0.6412495217165226, 0.7224713681082742] |
| 0.0642        | 30.77 | 800  | 0.2501          | 0.4581   | 0.7121        | 0.7096           | [0.0, 0.6722145834845955, 0.7021141065136746] | [nan, 0.6976031865943273, 0.7265325317101161] |
| 0.0481        | 31.54 | 820  | 0.2685          | 0.4137   | 0.6689        | 0.6766           | [0.0, 0.6379976664903103, 0.6031984018650592] | [nan, 0.7145859291453688, 0.6231961550279683] |
| 0.0311        | 32.31 | 840  | 0.2570          | 0.4284   | 0.6804        | 0.6832           | [0.0, 0.6426329055663264, 0.6425854743219936] | [nan, 0.6969752862342657, 0.6639063603053335] |
| 0.0389        | 33.08 | 860  | 0.2795          | 0.3918   | 0.6456        | 0.6590           | [0.0, 0.6244554318979076, 0.5508200429573112] | [nan, 0.7254125011037311, 0.5658618862962298] |
| 0.0282        | 33.85 | 880  | 0.2568          | 0.4242   | 0.6759        | 0.6775           | [0.0, 0.6282787291971401, 0.6442735430594793] | [nan, 0.6857107537747603, 0.6660974613184492] |
| 0.0245        | 34.62 | 900  | 0.2635          | 0.4503   | 0.7043        | 0.7037           | [0.0, 0.6658605581388065, 0.6850412042515538] | [nan, 0.7008356961354695, 0.7076892832638209] |
| 0.0315        | 35.38 | 920  | 0.2769          | 0.4443   | 0.7038        | 0.7055           | [0.0, 0.6610872730365329, 0.6718978137221756] | [nan, 0.7138198907060935, 0.6938235070611933] |
| 0.0283        | 36.15 | 940  | 0.2697          | 0.4392   | 0.6920        | 0.6907           | [0.0, 0.6405508279799802, 0.6769668218170816] | [nan, 0.6841213809883544, 0.6998318265269149] |
| 0.0257        | 36.92 | 960  | 0.2712          | 0.4562   | 0.7099        | 0.7082           | [0.0, 0.6720494469697227, 0.6964887349332429] | [nan, 0.6999154296702542, 0.7197879714666775] |
| 0.0188        | 37.69 | 980  | 0.2857          | 0.4300   | 0.6763        | 0.6771           | [0.0, 0.6397832221652129, 0.6501046733477022] | [nan, 0.6811686795451647, 0.6713607293464362] |
| 0.0259        | 38.46 | 1000 | 0.2812          | 0.4368   | 0.6851        | 0.6838           | [0.0, 0.6396217765000503, 0.6707000380577134] | [nan, 0.6772780519391329, 0.6929027930893589] |
| 0.0169        | 39.23 | 1020 | 0.2795          | 0.4542   | 0.7084        | 0.7054           | [0.0, 0.6598929743362643, 0.7028156867427239] | [nan, 0.6906225043413423, 0.7260947520404938] |
| 0.0296        | 40.0  | 1040 | 0.2834          | 0.4470   | 0.7015        | 0.7013           | [0.0, 0.6608002641121026, 0.6801095152287282] | [nan, 0.7006602764723773, 0.7022773353480376] |
| 0.0183        | 40.77 | 1060 | 0.2874          | 0.4386   | 0.6909        | 0.6903           | [0.0, 0.6432231900832152, 0.6726091072738183] | [nan, 0.6874296310104291, 0.694422081276136]  |
| 0.0199        | 41.54 | 1080 | 0.2741          | 0.4594   | 0.7175        | 0.7154           | [0.0, 0.6721657359810768, 0.7061664449453671] | [nan, 0.7051238631569653, 0.7298866398455491] |
| 0.0162        | 42.31 | 1100 | 0.2883          | 0.4414   | 0.6921        | 0.6913           | [0.0, 0.6492915338226911, 0.6750215527697642] | [nan, 0.6870752597447193, 0.6971930338516571] |
| 0.0179        | 43.08 | 1120 | 0.2927          | 0.4425   | 0.6936        | 0.6927           | [0.0, 0.651082790586508, 0.6764744769464034]  | [nan, 0.6884633119781804, 0.6987260886947118] |
| 0.0228        | 43.85 | 1140 | 0.2954          | 0.4273   | 0.6807        | 0.6841           | [0.0, 0.6418083531582984, 0.6399672125377378] | [nan, 0.7006630235364526, 0.6608033559804007] |
| 0.0164        | 44.62 | 1160 | 0.2954          | 0.4264   | 0.6740        | 0.6756           | [0.0, 0.6356634502412776, 0.6436554266840772] | [nan, 0.6834636553611899, 0.6644801545389767] |
| 0.0158        | 45.38 | 1180 | 0.2906          | 0.4433   | 0.6956        | 0.6951           | [0.0, 0.6536928350497138, 0.6760836624911459] | [nan, 0.6927067410990219, 0.6985223421818058] |
| 0.0198        | 46.15 | 1200 | 0.2881          | 0.4441   | 0.6969        | 0.6961           | [0.0, 0.6527988151987781, 0.6794425179962712] | [nan, 0.6919179412716945, 0.7019810769049473] |
| 0.018         | 46.92 | 1220 | 0.2961          | 0.4350   | 0.6844        | 0.6839           | [0.0, 0.6395287774950378, 0.6655290939553297] | [nan, 0.6815206961845243, 0.6872821426644097] |
| 0.0179        | 47.69 | 1240 | 0.2898          | 0.4459   | 0.6987        | 0.6982           | [0.0, 0.6581945977423002, 0.6796217960953337] | [nan, 0.6955130632707722, 0.701934270273604]  |
| 0.0213        | 48.46 | 1260 | 0.2902          | 0.4469   | 0.7004        | 0.6998           | [0.0, 0.6595482974648909, 0.6811920247361126] | [nan, 0.6971510983350829, 0.7036303223269834] |
| 0.0227        | 49.23 | 1280 | 0.2888          | 0.4452   | 0.6967        | 0.6953           | [0.0, 0.6532891096762087, 0.6823149709479772] | [nan, 0.6885578894699147, 0.7047801134592744] |
| 0.0266        | 50.0  | 1300 | 0.2904          | 0.4458   | 0.6980        | 0.6969           | [0.0, 0.6551336334577343, 0.6821319425157643] | [nan, 0.6913100552356098, 0.70464740289276]   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-busigt2", "results": []}]}
transformers pytorch tensorboard segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b0-finetuned-busigt2
==============================


This model is a fine-tuned version of nvidia/mit-b1 on the kasumi222/busigt5 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2904
* Mean Iou: 0.4458
* Mean Accuracy: 0.6980
* Overall Accuracy: 0.6969
* Per Category Iou: [0.0, 0.6551336334577343, 0.6821319425157643]
* Per Category Accuracy: [nan, 0.6913100552356098, 0.70464740289276]


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: 0.00013
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00013
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00013
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00013
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1