transformers tensorboard safetensors segformer generated_from_trainer base_model:DiTo97/binarization-segformer-b3 license:openrail endpoints_compatible region:us
---
license: openrail
base_model: DiTo97/binarization-segformer-b3
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-scene-parse-150-lr-5-e-30
  results: []
---

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# segformer-b0-scene-parse-150-lr-5-e-30

This model is a fine-tuned version of [DiTo97/binarization-segformer-b3](https://huggingface.co/DiTo97/binarization-segformer-b3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Mean Iou: 0.4846
- Mean Accuracy: 0.5002
- Overall Accuracy: 0.9687
- Per Category Iou: [0.0004776099180272339, 0.9687318368688158]
- Per Category Accuracy: [0.0004786768150677956, 0.9999280293990062]

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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                        |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:|
| No log        | 1.0   | 112  | 2.2080          | 0.0208   | 0.4867        | 0.0409           | [0.030359692962016637, 0.011234027937699289] | [0.9620657111236344, 0.011247758028276789]   |
| No log        | 2.0   | 224  | 1.4759          | 0.0198   | 0.4977        | 0.0391           | [0.03105738195300228, 0.008579759290056214]  | [0.9867430286323423, 0.008583423879932913]   |
| No log        | 3.0   | 336  | 0.9545          | 0.2044   | 0.5106        | 0.3895           | [0.03167545114091724, 0.3770365875250204]    | [0.63983643511383, 0.38141170005716085]      |
| No log        | 4.0   | 448  | 0.9261          | 0.1630   | 0.5110        | 0.3102           | [0.03177509935992916, 0.2941818944688777]    | [0.7253108004426911, 0.29678542982527334]    |
| 1.4226        | 5.0   | 560  | 0.5223          | 0.4799   | 0.5020        | 0.9428           | [0.017040433028331244, 0.9427249005417263]   | [0.031779387701061236, 0.9721328848551324]   |
| 1.4226        | 6.0   | 672  | 0.2859          | 0.4844   | 0.5000        | 0.9661           | [0.0026066960283743228, 0.9661016634825252]  | [0.0028381121801182773, 0.997139715499558]   |
| 1.4226        | 7.0   | 784  | 0.3327          | 0.4845   | 0.5001        | 0.9666           | [0.0023741570079603156, 0.9666249471662205]  | [0.0025444558361227857, 0.9976889561878419]  |
| 1.4226        | 8.0   | 896  | 0.3413          | 0.4861   | 0.5020        | 0.9655           | [0.006810437487136094, 0.965477402345672]    | [0.0075824444429356125, 0.996347820132999]   |
| 0.4243        | 9.0   | 1008 | 0.2237          | 0.4845   | 0.5001        | 0.9670           | [0.002003100646207948, 0.9669488069696599]   | [0.002125189264059858, 0.998036285433216]    |
| 0.4243        | 10.0  | 1120 | 0.2312          | 0.4845   | 0.5001        | 0.9673           | [0.0017493474271832114, 0.9672550380074268]  | [0.001838322662122066, 0.9983613016047257]   |
| 0.4243        | 11.0  | 1232 | 0.2255          | 0.4846   | 0.5001        | 0.9683           | [0.0008165323590605036, 0.9682956004963261]  | [0.0008300459665537307, 0.9994667831613294]  |
| 0.4243        | 12.0  | 1344 | 0.2229          | 0.4845   | 0.5001        | 0.9681           | [0.0009280118466771697, 0.9681143324063521]  | [0.0009488664525634671, 0.9992759735664452]  |
| 0.4243        | 13.0  | 1456 | 0.1762          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687853389316134]                    | [0.0, 0.9999981952660845]                    |
| 0.2658        | 14.0  | 1568 | 0.1799          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687686496310763]                    | [0.0, 0.9999809682605274]                    |
| 0.2658        | 15.0  | 1680 | 0.1895          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687842263115777]                    | [0.0, 0.9999970467990473]                    |
| 0.2658        | 16.0  | 1792 | 0.1751          | 0.4846   | 0.5002        | 0.9687           | [0.0005055791248239351, 0.9686817929933391]  | [0.0005075332188130173, 0.9998754733598305]  |
| 0.2658        | 17.0  | 1904 | 0.1570          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687863985697428]                    | [0.0, 0.9999992890442151]                    |
| 0.2281        | 18.0  | 2016 | 0.2391          | 0.4892   | 0.5049        | 0.9676           | [0.010804445997179198, 0.9675856416536037]   | [0.01133886923635771, 0.9984063652637066]    |
| 0.2281        | 19.0  | 2128 | 0.1763          | 0.4846   | 0.5002        | 0.9687           | [0.00047390236595756206, 0.9687105941224816] | [0.00047528194403894595, 0.9999062085253005] |
| 0.2281        | 20.0  | 2240 | 0.1614          | 0.4844   | 0.5000        | 0.9688           | [8.31166363884634e-05, 0.96876793276621]     | [8.317434020681555e-05, 0.9999776322372291]  |
| 0.2281        | 21.0  | 2352 | 0.1576          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687801996866862]                    | [0.0, 0.999992890442151]                     |
| 0.2281        | 22.0  | 2464 | 0.1509          | 0.4848   | 0.5004        | 0.9687           | [0.0008725737882892481, 0.9686954205214706]  | [0.0008758767254432005, 0.9998780437384374]  |
| 0.2197        | 23.0  | 2576 | 0.1575          | 0.4844   | 0.5000        | 0.9688           | [0.00013731233980227023, 0.9687503427281783] | [0.00013749227666840936, 0.9999577801641586] |
| 0.2197        | 24.0  | 2688 | 0.1522          | 0.4847   | 0.5002        | 0.9687           | [0.0006022476015827608, 0.96869965961662]    | [0.0006042870431352313, 0.999890895631472]   |
| 0.2197        | 25.0  | 2800 | 0.1532          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687816831800673]                    | [0.0, 0.9999944217315339]                    |
| 0.2197        | 26.0  | 2912 | 0.1589          | 0.4844   | 0.5000        | 0.9688           | [7.462281558667101e-05, 0.9687624309087838]  | [7.46871626346915e-05, 0.9999722180354826]   |
| 0.2129        | 27.0  | 3024 | 0.1612          | 0.4845   | 0.5001        | 0.9687           | [0.0003791835028091742, 0.9687128062602468]  | [0.00038022555523115677, 0.9999114586603274] |
| 0.2129        | 28.0  | 3136 | 0.1526          | 0.4844   | 0.5000        | 0.9688           | [4.750029687685548e-05, 0.9687701927590581]  | [4.7528194403894597e-05, 0.9999810776383405] |
| 0.2129        | 29.0  | 3248 | 0.1501          | 0.4844   | 0.5000        | 0.9688           | [2.375603233535373e-05, 0.9687772095405226]  | [2.3764097201947298e-05, 0.999989062218694]  |
| 0.2129        | 30.0  | 3360 | 0.1499          | 0.4846   | 0.5002        | 0.9687           | [0.0004776099180272339, 0.9687318368688158]  | [0.0004786768150677956, 0.9999280293990062]  |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
