pytorch tensorboard segformer transformers vision image-segmentation generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
  results: []
---

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# segformer-b0-finetuned-segments-sidewalk-2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the userGagan/ResizedSample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3429
- Mean Iou: 0.8143
- Mean Accuracy: 0.9007
- Overall Accuracy: 0.9061
- Per Category Iou: [0.8822819675417668, 0.7774253195321242, 0.7832033563111727]
- Per Category Accuracy: [0.9319684170082266, 0.8657193844491432, 0.9044945609610779]

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                             | Per Category Accuracy                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------:|:------------------------------------------------------------:|
| 0.7949        | 0.5   | 20   | 0.8960          | 0.7129   | 0.8533        | 0.8427           | [0.7978191889735743, 0.6994730230171242, 0.6413103816527537] | [0.826874349660607, 0.8237981626592454, 0.9091007880329902]  |
| 0.4881        | 1.0   | 40   | 0.6195          | 0.7364   | 0.8610        | 0.8552           | [0.8041892620489134, 0.6981663805103046, 0.7069887055480671] | [0.8308827565320059, 0.887905283397269, 0.8642919506720577]  |
| 0.3115        | 1.5   | 60   | 0.4767          | 0.7352   | 0.8536        | 0.8588           | [0.8276338695141907, 0.7016825436162023, 0.6763414045904438] | [0.8633649830215921, 0.8776778472775076, 0.8196451790592317] |
| 0.5863        | 2.0   | 80   | 0.4895          | 0.7543   | 0.8748        | 0.8668           | [0.8156517914197925, 0.7259786638902507, 0.7213518497027839] | [0.8402281798360435, 0.8932153836673491, 0.8909222571543128] |
| 0.5182        | 2.5   | 100  | 0.4058          | 0.7904   | 0.8866        | 0.8919           | [0.860991170688589, 0.7583876635226005, 0.7518265397248736]  | [0.9088903949664655, 0.8761789935147187, 0.8746304338865427] |
| 0.4755        | 3.0   | 120  | 0.3683          | 0.7896   | 0.8861        | 0.8895           | [0.8547537413009911, 0.7465075384127533, 0.7674680941571024] | [0.8979683913158062, 0.8865259395690547, 0.8738060532025316] |
| 0.6616        | 3.5   | 140  | 0.3697          | 0.7915   | 0.8874        | 0.8898           | [0.8551700094228354, 0.7431970428539307, 0.7761922571371438] | [0.8899387313627766, 0.903193218309171, 0.8690639906770039]  |
| 0.5087        | 4.0   | 160  | 0.3367          | 0.8061   | 0.8987        | 0.8987           | [0.8640367246398447, 0.7643869962764198, 0.7899951558528526] | [0.9012200396208266, 0.8918889478830869, 0.902900133774502]  |
| 0.5478        | 4.5   | 180  | 0.3297          | 0.8131   | 0.8991        | 0.9040           | [0.8775309087721331, 0.7692790103652185, 0.792538025793261]  | [0.9196387801394476, 0.8895118205906903, 0.8882327151727265] |
| 0.389         | 5.0   | 200  | 0.3429          | 0.8143   | 0.9007        | 0.9061           | [0.8822819675417668, 0.7774253195321242, 0.7832033563111727] | [0.9319684170082266, 0.8657193844491432, 0.9044945609610779] |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
