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# segformer-b0-finetuned-segments-construction-1

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yiming19/construction_place dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2796
- Mean Iou: 0.3218
- Mean Accuracy: 0.5305
- Overall Accuracy: 0.9276
- Accuracy Unlabeled: nan
- Accuracy Ruler: 0.8954
- Accuracy Socket: 0.0
- Accuracy Wall: 0.9644
- Accuracy Window: nan
- Accuracy Heater: nan
- Accuracy Floor: 0.6710
- Accuracy Ceiling: 0.0
- Accuracy Skirting: nan
- Accuracy Door: 0.6525
- Accuracy Light: nan
- Iou Unlabeled: nan
- Iou Ruler: 0.7222
- Iou Socket: 0.0
- Iou Wall: 0.9553
- Iou Window: 0.0
- Iou Heater: nan
- Iou Floor: 0.2630
- Iou Ceiling: 0.0
- Iou Skirting: 0.0
- Iou Door: 0.6342
- Iou Light: nan

## 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: 2
- eval_batch_size: 2
- 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 | Accuracy Unlabeled | Accuracy Ruler | Accuracy Socket | Accuracy Wall | Accuracy Window | Accuracy Heater | Accuracy Floor | Accuracy Ceiling | Accuracy Skirting | Accuracy Door | Accuracy Light | Iou Unlabeled | Iou Ruler | Iou Socket | Iou Wall | Iou Window | Iou Heater | Iou Floor | Iou Ceiling | Iou Skirting | Iou Door | Iou Light |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:---------------:|:-------------:|:---------------:|:---------------:|:--------------:|:----------------:|:-----------------:|:-------------:|:--------------:|:-------------:|:---------:|:----------:|:--------:|:----------:|:----------:|:---------:|:-----------:|:------------:|:--------:|:---------:|
| 1.8126        | 1.43  | 20   | 2.1233          | 0.1955   | 0.5160        | 0.8448           | nan                | 0.8191         | 0.0             | 0.8868        | nan             | nan             | 0.9618         | 0.0              | nan               | 0.4281        | nan            | 0.0           | 0.5555    | 0.0        | 0.8845   | 0.0        | 0.0        | 0.2971    | 0.0         | 0.0          | 0.4135   | 0.0       |
| 1.905         | 2.86  | 40   | 1.3611          | 0.1827   | 0.4921        | 0.8505           | nan                | 0.9275         | 0.0             | 0.9139        | nan             | nan             | 0.9627         | 0.0              | nan               | 0.1484        | nan            | nan           | 0.5404    | 0.0        | 0.9095   | 0.0        | 0.0        | 0.2289    | 0.0         | 0.0          | 0.1484   | 0.0       |
| 1.1072        | 4.29  | 60   | 1.0502          | 0.2327   | 0.5517        | 0.8903           | nan                | 0.9108         | 0.0             | 0.9266        | nan             | nan             | 0.9367         | 0.0              | nan               | 0.5360        | nan            | nan           | 0.5301    | 0.0        | 0.9206   | 0.0        | 0.0        | 0.3475    | 0.0         | 0.0          | 0.5284   | 0.0       |
| 1.0076        | 5.71  | 80   | 0.8802          | 0.2744   | 0.5609        | 0.9089           | nan                | 0.8208         | 0.0             | 0.9410        | nan             | nan             | 0.9532         | 0.0              | nan               | 0.6505        | nan            | nan           | 0.5500    | 0.0        | 0.9277   | 0.0        | 0.0        | 0.3688    | 0.0         | 0.0          | 0.6227   | nan       |
| 1.5533        | 7.14  | 100  | 0.8991          | 0.2846   | 0.5514        | 0.8878           | nan                | 0.8918         | 0.0             | 0.9243        | nan             | nan             | 0.9591         | 0.0              | nan               | 0.5332        | nan            | nan           | 0.5262    | 0.0        | 0.9169   | 0.0        | nan        | 0.3209    | 0.0         | 0.0          | 0.5132   | nan       |
| 0.9912        | 8.57  | 120  | 0.9340          | 0.2891   | 0.5652        | 0.8854           | nan                | 0.9478         | 0.0             | 0.9151        | nan             | nan             | 0.9438         | 0.0              | nan               | 0.5844        | nan            | nan           | 0.5059    | 0.0        | 0.9098   | 0.0        | nan        | 0.3424    | 0.0         | 0.0          | 0.5544   | nan       |
| 0.784         | 10.0  | 140  | 0.7017          | 0.3140   | 0.5984        | 0.9173           | nan                | 0.9136         | 0.0             | 0.9305        | nan             | nan             | 0.8971         | 0.0              | nan               | 0.8493        | nan            | nan           | 0.5324    | 0.0        | 0.9224   | 0.0        | 0.0        | 0.5805    | 0.0         | 0.0          | 0.7909   | nan       |
| 0.5636        | 11.43 | 160  | 0.6925          | 0.3573   | 0.5978        | 0.9280           | nan                | 0.8714         | 0.0             | 0.9412        | nan             | nan             | 0.8868         | 0.0              | nan               | 0.8876        | nan            | nan           | 0.5701    | 0.0        | 0.9308   | 0.0        | nan        | 0.5638    | 0.0         | 0.0          | 0.7935   | nan       |
| 1.0692        | 12.86 | 180  | 0.7313          | 0.2931   | 0.5724        | 0.8981           | nan                | 0.9587         | 0.0             | 0.9231        | nan             | nan             | 0.8880         | 0.0              | nan               | 0.6647        | nan            | nan           | 0.4988    | 0.0        | 0.9182   | 0.0        | nan        | 0.3342    | 0.0         | 0.0          | 0.5932   | nan       |
| 0.7603        | 14.29 | 200  | 0.6907          | 0.2577   | 0.5744        | 0.9001           | nan                | 0.9619         | 0.0             | 0.9251        | nan             | nan             | 0.8930         | 0.0              | nan               | 0.6661        | nan            | nan           | 0.4939    | 0.0        | 0.9208   | 0.0        | 0.0        | 0.3219    | 0.0         | 0.0          | 0.5824   | nan       |
| 0.9509        | 15.71 | 220  | 0.5110          | 0.3682   | 0.6069        | 0.9324           | nan                | 0.9355         | 0.0             | 0.9417        | nan             | nan             | 0.8453         | 0.0              | nan               | 0.9191        | nan            | nan           | 0.5671    | 0.0        | 0.9334   | 0.0        | nan        | 0.6050    | 0.0         | 0.0          | 0.8403   | nan       |
| 0.4254        | 17.14 | 240  | 0.5925          | 0.2961   | 0.5629        | 0.9023           | nan                | 0.9646         | 0.0             | 0.9295        | nan             | nan             | 0.8261         | 0.0              | nan               | 0.6569        | nan            | nan           | 0.5302    | 0.0        | 0.9243   | 0.0        | nan        | 0.3138    | 0.0         | 0.0          | 0.6009   | nan       |
| 0.3839        | 18.57 | 260  | 0.4226          | 0.3537   | 0.5479        | 0.9367           | nan                | 0.9108         | 0.0             | 0.9540        | nan             | nan             | 0.5102         | 0.0              | nan               | 0.9124        | nan            | nan           | 0.6643    | 0.0        | 0.9426   | 0.0        | nan        | 0.3868    | 0.0         | 0.0          | 0.8361   | nan       |
| 0.7441        | 20.0  | 280  | 0.5084          | 0.3533   | 0.5993        | 0.9277           | nan                | 0.9691         | 0.0             | 0.9391        | nan             | nan             | 0.8075         | 0.0              | nan               | 0.8801        | nan            | nan           | 0.5527    | 0.0        | 0.9333   | 0.0        | nan        | 0.5197    | 0.0         | 0.0          | 0.8208   | nan       |
| 0.4374        | 21.43 | 300  | 0.4683          | 0.3038   | 0.5549        | 0.9173           | nan                | 0.9662         | 0.0             | 0.9480        | nan             | nan             | 0.7594         | 0.0              | nan               | 0.6558        | nan            | nan           | 0.6024    | 0.0        | 0.9419   | 0.0        | nan        | 0.2804    | 0.0         | 0.0          | 0.6056   | nan       |
| 0.6224        | 22.86 | 320  | 0.4100          | 0.3810   | 0.5960        | 0.9374           | nan                | 0.9704         | 0.0             | 0.9460        | nan             | nan             | 0.7131         | 0.0              | nan               | 0.9467        | nan            | nan           | 0.5986    | 0.0        | 0.9401   | 0.0        | nan        | 0.6197    | 0.0         | 0.0          | 0.8898   | nan       |
| 0.4473        | 24.29 | 340  | 0.3933          | 0.3368   | 0.5431        | 0.9336           | nan                | 0.9212         | 0.0             | 0.9620        | nan             | nan             | 0.6197         | 0.0              | nan               | 0.7556        | nan            | nan           | 0.7221    | 0.0        | 0.9521   | 0.0        | nan        | 0.2958    | 0.0         | 0.0          | 0.7245   | nan       |
| 0.3364        | 25.71 | 360  | 0.4336          | 0.2976   | 0.5125        | 0.9134           | nan                | 0.9408         | 0.0             | 0.9544        | nan             | nan             | 0.6075         | 0.0              | nan               | 0.5721        | nan            | nan           | 0.6918    | 0.0        | 0.9481   | 0.0        | nan        | 0.1998    | 0.0         | 0.0          | 0.5411   | nan       |
| 0.281         | 27.14 | 380  | 0.3795          | 0.3689   | 0.5760        | 0.9420           | nan                | 0.9250         | 0.0             | 0.9589        | nan             | nan             | 0.6859         | 0.0              | nan               | 0.8863        | nan            | nan           | 0.7108    | 0.0        | 0.9518   | 0.0        | nan        | 0.4576    | 0.0         | 0.0          | 0.8305   | nan       |
| 0.3198        | 28.57 | 400  | 0.4023          | 0.3158   | 0.5143        | 0.9238           | nan                | 0.9120         | 0.0             | 0.9610        | nan             | nan             | 0.5580         | 0.0              | nan               | 0.6550        | nan            | nan           | 0.7210    | 0.0        | 0.9519   | 0.0        | nan        | 0.2238    | 0.0         | 0.0          | 0.6293   | nan       |
| 0.4624        | 30.0  | 420  | 0.3565          | 0.3770   | 0.5774        | 0.9475           | nan                | 0.9408         | 0.0             | 0.9613        | nan             | nan             | 0.6287         | 0.0              | nan               | 0.9337        | nan            | nan           | 0.6855    | 0.0        | 0.9539   | 0.0        | nan        | 0.4943    | 0.0         | 0.0          | 0.8827   | nan       |
| 0.2356        | 31.43 | 440  | 0.3940          | 0.3100   | 0.5349        | 0.9221           | nan                | 0.9268         | 0.0             | 0.9602        | nan             | nan             | 0.7187         | 0.0              | nan               | 0.6040        | nan            | nan           | 0.7005    | 0.0        | 0.9536   | 0.0        | nan        | 0.2474    | 0.0         | 0.0          | 0.5781   | nan       |
| 0.3931        | 32.86 | 460  | 0.3516          | 0.3162   | 0.5570        | 0.9258           | nan                | 0.9338         | 0.0             | 0.9598        | nan             | nan             | 0.8124         | 0.0              | nan               | 0.6362        | nan            | nan           | 0.6824    | 0.0        | 0.9542   | 0.0        | nan        | 0.2888    | 0.0         | 0.0          | 0.6040   | nan       |
| 0.2431        | 34.29 | 480  | 0.4011          | 0.2955   | 0.5291        | 0.9138           | nan                | 0.9242         | 0.0             | 0.9583        | nan             | nan             | 0.7864         | 0.0              | nan               | 0.5058        | nan            | nan           | 0.6954    | 0.0        | 0.9520   | 0.0        | nan        | 0.2331    | 0.0         | 0.0          | 0.4832   | nan       |
| 0.2131        | 35.71 | 500  | 0.2847          | 0.3764   | 0.5613        | 0.9487           | nan                | 0.8877         | 0.0             | 0.9679        | nan             | nan             | 0.6103         | 0.0              | nan               | 0.9020        | nan            | nan           | 0.7330    | 0.0        | 0.9571   | 0.0        | nan        | 0.4539    | 0.0         | 0.0          | 0.8669   | nan       |
| 0.4151        | 37.14 | 520  | 0.3176          | 0.3186   | 0.5239        | 0.9256           | nan                | 0.8930         | 0.0             | 0.9640        | nan             | nan             | 0.6505         | 0.0              | nan               | 0.6356        | nan            | nan           | 0.7251    | 0.0        | 0.9544   | 0.0        | nan        | 0.2507    | 0.0         | 0.0          | 0.6187   | nan       |
| 0.2408        | 38.57 | 540  | 0.3267          | 0.3071   | 0.5361        | 0.9208           | nan                | 0.9264         | 0.0             | 0.9600        | nan             | nan             | 0.7441         | 0.0              | nan               | 0.5859        | nan            | nan           | 0.6868    | 0.0        | 0.9538   | 0.0        | nan        | 0.2526    | 0.0         | 0.0          | 0.5635   | nan       |
| 0.2274        | 40.0  | 560  | 0.2875          | 0.3396   | 0.5471        | 0.9349           | nan                | 0.9098         | 0.0             | 0.9626        | nan             | nan             | 0.6456         | 0.0              | nan               | 0.7649        | nan            | nan           | 0.7018    | 0.0        | 0.9547   | 0.0        | nan        | 0.3216    | 0.0         | 0.0          | 0.7387   | nan       |
| 0.2452        | 41.43 | 580  | 0.2998          | 0.3181   | 0.5357        | 0.9279           | nan                | 0.9089         | 0.0             | 0.9642        | nan             | nan             | 0.6932         | 0.0              | nan               | 0.6480        | nan            | nan           | 0.7057    | 0.0        | 0.9562   | 0.0        | nan        | 0.2578    | 0.0         | 0.0          | 0.6252   | nan       |
| 0.2922        | 42.86 | 600  | 0.2957          | 0.3131   | 0.5246        | 0.9255           | nan                | 0.9056         | 0.0             | 0.9643        | nan             | nan             | 0.6535         | 0.0              | nan               | 0.6242        | nan            | nan           | 0.7103    | 0.0        | 0.9563   | 0.0        | nan        | 0.2347    | 0.0         | 0.0          | 0.6037   | nan       |
| 0.3704        | 44.29 | 620  | 0.3290          | 0.3172   | 0.5429        | 0.9247           | nan                | 0.9246         | 0.0             | 0.9583        | nan             | nan             | 0.7123         | 0.0              | nan               | 0.6621        | nan            | nan           | 0.6856    | 0.0        | 0.9527   | 0.0        | nan        | 0.2707    | 0.0         | 0.0          | 0.6286   | nan       |
| 0.2482        | 45.71 | 640  | 0.2995          | 0.3251   | 0.5368        | 0.9276           | nan                | 0.9018         | 0.0             | 0.9617        | nan             | nan             | 0.6795         | 0.0              | nan               | 0.6779        | nan            | nan           | 0.7154    | 0.0        | 0.9538   | 0.0        | nan        | 0.2790    | 0.0         | 0.0          | 0.6528   | nan       |
| 0.2798        | 47.14 | 660  | 0.2808          | 0.3323   | 0.5374        | 0.9319           | nan                | 0.8938         | 0.0             | 0.9644        | nan             | nan             | 0.6554         | 0.0              | nan               | 0.7110        | nan            | nan           | 0.7218    | 0.0        | 0.9554   | 0.0        | nan        | 0.2919    | 0.0         | 0.0          | 0.6894   | nan       |
| 0.2746        | 48.57 | 680  | 0.2695          | 0.3265   | 0.5341        | 0.9299           | nan                | 0.8947         | 0.0             | 0.9642        | nan             | nan             | 0.6597         | 0.0              | nan               | 0.6861        | nan            | nan           | 0.7198    | 0.0        | 0.9554   | 0.0        | nan        | 0.2735    | 0.0         | 0.0          | 0.6633   | nan       |
| 0.2169        | 50.0  | 700  | 0.2796          | 0.3218   | 0.5305        | 0.9276           | nan                | 0.8954         | 0.0             | 0.9644        | nan             | nan             | 0.6710         | 0.0              | nan               | 0.6525        | nan            | nan           | 0.7222    | 0.0        | 0.9553   | 0.0        | nan        | 0.2630    | 0.0         | 0.0          | 0.6342   | nan       |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.0.dev0

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-construction-1", "results": []}]}
transformers pytorch segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b0-finetuned-segments-construction-1
==============================================


This model is a fine-tuned version of nvidia/mit-b0 on the yiming19/construction\_place dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2796
* Mean Iou: 0.3218
* Mean Accuracy: 0.5305
* Overall Accuracy: 0.9276
* Accuracy Unlabeled: nan
* Accuracy Ruler: 0.8954
* Accuracy Socket: 0.0
* Accuracy Wall: 0.9644
* Accuracy Window: nan
* Accuracy Heater: nan
* Accuracy Floor: 0.6710
* Accuracy Ceiling: 0.0
* Accuracy Skirting: nan
* Accuracy Door: 0.6525
* Accuracy Light: nan
* Iou Unlabeled: nan
* Iou Ruler: 0.7222
* Iou Socket: 0.0
* Iou Wall: 0.9553
* Iou Window: 0.0
* Iou Heater: nan
* Iou Floor: 0.2630
* Iou Ceiling: 0.0
* Iou Skirting: 0.0
* Iou Door: 0.6342
* Iou Light: nan


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: 2
* eval\_batch\_size: 2
* 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 4.25.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.0.dev0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* 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 4.25.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.0.dev0
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* 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 4.25.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.0.dev0
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* 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 4.25.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.0.dev0