---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-busigt2
  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. -->

# 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: 0.1923
- Mean Iou: 0.4456
- Mean Accuracy: 0.6990
- Overall Accuracy: 0.6980
- Per Category Iou: [0.0, 0.6613256012770924, 0.6755795107848668]
- Per Category Accuracy: [nan, 0.6930571879874813, 0.7049128240257888]

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                | Per Category Accuracy                          |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------:|:----------------------------------------------:|
| 0.44          | 0.77  | 20   | 0.4848          | 0.2588   | 0.4242        | 0.4165           | [0.0, 0.3504134718879466, 0.42602074549863]     | [nan, 0.3783013333071708, 0.47012910920923223] |
| 0.3746        | 1.54  | 40   | 0.3811          | 0.3111   | 0.5104        | 0.5161           | [0.0, 0.4842565751294053, 0.4491183491823408]   | [nan, 0.5445819066586871, 0.4761423846712137]  |
| 0.3319        | 2.31  | 60   | 0.3142          | 0.2399   | 0.3819        | 0.3648           | [0.0, 0.2677680368240358, 0.45190264244757206]  | [nan, 0.279724508717023, 0.4840686746895068]   |
| 0.2211        | 3.08  | 80   | 0.2530          | 0.2975   | 0.4750        | 0.4814           | [0.0, 0.4706420141972068, 0.4218805055880307]   | [nan, 0.5129361209493069, 0.4371271301147258]  |
| 0.2097        | 3.85  | 100  | 0.2433          | 0.2900   | 0.4480        | 0.4460           | [0.0, 0.43160250466254296, 0.43845878928113535] | [nan, 0.43601714952858417, 0.4600634808054265] |
| 0.1869        | 4.62  | 120  | 0.2363          | 0.3632   | 0.5993        | 0.6166           | [0.0, 0.6101054609134765, 0.4795020991617725]   | [nan, 0.7024776555770306, 0.4960379563233556]  |
| 0.1632        | 5.38  | 140  | 0.2386          | 0.4353   | 0.6988        | 0.7093           | [0.0, 0.6991547342129519, 0.6066344865447587]   | [nan, 0.7611584761643136, 0.6364771236719307]  |
| 0.1939        | 6.15  | 160  | 0.2166          | 0.3374   | 0.5337        | 0.5229           | [0.0, 0.46597040423540376, 0.5460862797699997]  | [nan, 0.4692625114052214, 0.5981270739467682]  |
| 0.2074        | 6.92  | 180  | 0.2209          | 0.3219   | 0.5524        | 0.5826           | [0.0, 0.5973031615126874, 0.3684272471225944]   | [nan, 0.7324595053322476, 0.37233573901062894] |
| 0.1243        | 7.69  | 200  | 0.2214          | 0.3890   | 0.6490        | 0.6624           | [0.0, 0.6202500066793128, 0.5468835150043967]   | [nan, 0.728845938760093, 0.5691879104528569]   |
| 0.1079        | 8.46  | 220  | 0.2349          | 0.3889   | 0.6489        | 0.6659           | [0.0, 0.6357100818960788, 0.5310789782806924]   | [nan, 0.7502934453088975, 0.5474674308445788]  |
| 0.1355        | 9.23  | 240  | 0.1988          | 0.4417   | 0.6835        | 0.6772           | [0.0, 0.6347203785731718, 0.6904229863937434]   | [nan, 0.6460698342931706, 0.7208882026363698]  |
| 0.1258        | 10.0  | 260  | 0.1985          | 0.4181   | 0.6597        | 0.6552           | [0.0, 0.5971057628808589, 0.6571229098113397]   | [nan, 0.6326629842926801, 0.6867490977333476]  |
| 0.1098        | 10.77 | 280  | 0.1959          | 0.4578   | 0.7091        | 0.7047           | [0.0, 0.6732406203240918, 0.7002946319094014]   | [nan, 0.6828090692358256, 0.7353112530851077]  |
| 0.097         | 11.54 | 300  | 0.1968          | 0.4401   | 0.6784        | 0.6719           | [0.0, 0.6352861327900514, 0.6850189737146176]   | [nan, 0.6398854081842887, 0.7169096389721925]  |
| 0.0844        | 12.31 | 320  | 0.1959          | 0.4164   | 0.6610        | 0.6637           | [0.0, 0.6274020120203341, 0.6218360931423603]   | [nan, 0.677014333787907, 0.645067517189047]    |
| 0.1543        | 13.08 | 340  | 0.2004          | 0.4261   | 0.6663        | 0.6626           | [0.0, 0.618565945734453, 0.6597620117886153]    | [nan, 0.6440009026067676, 0.6886109003283072]  |
| 0.0871        | 13.85 | 360  | 0.1967          | 0.4270   | 0.6699        | 0.6664           | [0.0, 0.619536460193106, 0.6615374800234315]    | [nan, 0.6488875371589471, 0.6909044252641271]  |
| 0.1012        | 14.62 | 380  | 0.1923          | 0.4456   | 0.6990        | 0.6980           | [0.0, 0.6613256012770924, 0.6755795107848668]   | [nan, 0.6930571879874813, 0.7049128240257888]  |


### Framework versions

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