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-3-e-30
  results: []
---

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# segformer-b0-scene-parse-150-lr-3-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: 0.1448
- Mean Iou: 0.5020
- Mean Accuracy: 0.5211
- Overall Accuracy: 0.9636
- Per Category Iou: [0.04038452943608308, 0.9635414972513529]
- Per Category Accuracy: [0.04908134789959329, 0.993061727806312]

## 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: 0.001
- 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  | 0.1656          | 0.4844   | 0.5           | 0.9688           | [0.0, 0.9687870873345269]                    | [0.0, 1.0]                                  |
| No log        | 2.0   | 224  | 0.1537          | 0.4844   | 0.5           | 0.9688           | [0.0, 0.9687870873345269]                    | [0.0, 1.0]                                  |
| No log        | 3.0   | 336  | 0.1432          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687868224249946]                    | [0.0, 0.9999997265554673]                   |
| No log        | 4.0   | 448  | 0.1475          | 0.4850   | 0.5005        | 0.9686           | [0.0013222566963458553, 0.9685858800631549]  | [0.0013324868788234735, 0.9997507279640349] |
| 0.2536        | 5.0   | 560  | 0.1711          | 0.4845   | 0.5001        | 0.9687           | [0.0003201696729864885, 0.9687338453238151]  | [0.0003208153122262885, 0.999935029579042]  |
| 0.2536        | 6.0   | 672  | 0.1638          | 0.4859   | 0.5015        | 0.9680           | [0.0039027635518112448, 0.9679476439113655]  | [0.004022922169186793, 0.9990080526133531]  |
| 0.2536        | 7.0   | 784  | 0.1410          | 0.4869   | 0.5025        | 0.9685           | [0.005323932455427736, 0.9684674246442314]   | [0.00540633211344301, 0.9995013465502566]   |
| 0.2536        | 8.0   | 896  | 0.1433          | 0.4844   | 0.5000        | 0.9688           | [0.0, 0.9687869283888075]                    | [0.0, 0.9999998359332805]                   |
| 0.2055        | 9.0   | 1008 | 0.1506          | 0.4852   | 0.5008        | 0.9687           | [0.0016878008192091351, 0.9687229148795367]  | [0.0016940406433959573, 0.999880887561577]  |
| 0.2055        | 10.0  | 1120 | 0.1453          | 0.4935   | 0.5096        | 0.9671           | [0.020029693488101304, 0.9670700472858372]   | [0.021548943855622924, 0.9975562262116929]  |
| 0.2055        | 11.0  | 1232 | 0.1517          | 0.4845   | 0.5001        | 0.9688           | [0.00011372817946646207, 0.9687905263352534] | [0.00011372817946646207, 1.0]               |
| 0.2055        | 12.0  | 1344 | 0.1431          | 0.4857   | 0.5013        | 0.9687           | [0.0027137294106124215, 0.9687079701798799]  | [0.002727778871680665, 0.9998331988350826]  |
| 0.2055        | 13.0  | 1456 | 0.1414          | 0.4933   | 0.5092        | 0.9672           | [0.019346379945589794, 0.9672062565458419]   | [0.020713805582525918, 0.9977227539320777]  |
| 0.1952        | 14.0  | 1568 | 0.3025          | 0.4616   | 0.5605        | 0.8715           | [0.052657461092701, 0.870574568072721]       | [0.2288363740061515, 0.8922046651387315]    |
| 0.1952        | 15.0  | 1680 | 0.1681          | 0.5006   | 0.5284        | 0.9534           | [0.04789077424916502, 0.9533115168989387]    | [0.07506229588337938, 0.9817203423700553]   |
| 0.1952        | 16.0  | 1792 | 0.1410          | 0.4898   | 0.5053        | 0.9683           | [0.011200534555931552, 0.9683138222802735]   | [0.011495033303684793, 0.9991528688378454]  |
| 0.1952        | 17.0  | 1904 | 0.1923          | 0.4976   | 0.5436        | 0.9374           | [0.05802705926695029, 0.9371260247893431]    | [0.12360895159592887, 0.9635867588094531]   |
| 0.184         | 18.0  | 2016 | 0.1869          | 0.5041   | 0.5434        | 0.9464           | [0.062043684016905756, 0.9461805831562268]   | [0.11365179486831295, 0.9732005216884172]   |
| 0.184         | 19.0  | 2128 | 0.1451          | 0.4945   | 0.5108        | 0.9667           | [0.0224270262537552, 0.9666655973811165]     | [0.02448211242454899, 0.9970476740698675]   |
| 0.184         | 20.0  | 2240 | 0.1495          | 0.5034   | 0.5236        | 0.9627           | [0.044245675510015896, 0.9625936882687055]   | [0.0553839259646526, 0.9918894164059282]    |
| 0.184         | 21.0  | 2352 | 0.1666          | 0.5074   | 0.5361        | 0.9560           | [0.05892929526242253, 0.9558424300678736]    | [0.0883447287837535, 0.9839176332566303]    |
| 0.184         | 22.0  | 2464 | 0.1359          | 0.4952   | 0.5117        | 0.9661           | [0.024373231478689413, 0.9660300331460354]   | [0.02716575797285461, 0.9963086081870168]   |
| 0.172         | 23.0  | 2576 | 0.1373          | 0.4947   | 0.5109        | 0.9667           | [0.022619186612181357, 0.9667099473494948]   | [0.024663738024592447, 0.9970877610383542]  |
| 0.172         | 24.0  | 2688 | 0.1447          | 0.5034   | 0.5235        | 0.9628           | [0.044081368434508966, 0.9626945273298088]   | [0.055020674764565694, 0.992004591243081]   |
| 0.172         | 25.0  | 2800 | 0.1408          | 0.5024   | 0.5209        | 0.9645           | [0.04033427475747473, 0.9644272946188437]    | [0.04782864048994779, 0.9940135882244722]   |
| 0.172         | 26.0  | 2912 | 0.1487          | 0.5049   | 0.5279        | 0.9602           | [0.049801967806652817, 0.9600934920373924]   | [0.06687047209076527, 0.9889578175713707]   |
| 0.1592        | 27.0  | 3024 | 0.1466          | 0.5061   | 0.5282        | 0.9614           | [0.05082699206223086, 0.9613120480339439]    | [0.0662356312083704, 0.9902326706714988]    |
| 0.1592        | 28.0  | 3136 | 0.1395          | 0.5022   | 0.5210        | 0.9641           | [0.04037355060044024, 0.9640515837293752]    | [0.04838200446765027, 0.9936091637606804]   |
| 0.1592        | 29.0  | 3248 | 0.1477          | 0.5054   | 0.5287        | 0.9601           | [0.05084271645010736, 0.9600046044343804]    | [0.06849152300704096, 0.9888161186145507]   |
| 0.1592        | 30.0  | 3360 | 0.1448          | 0.5020   | 0.5211        | 0.9636           | [0.04038452943608308, 0.9635414972513529]    | [0.04908134789959329, 0.993061727806312]    |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
