---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
  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-segments-sidewalk-2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6306
- Mean Iou: 0.1027
- Mean Accuracy: 0.1574
- Overall Accuracy: 0.6552
- Per Category Iou: [0.0, 0.40932069741697885, 0.6666047315185674, 0.0015527279135260222, 0.000557997451181134, 0.004734463745284192, 0.0, 0.00024311836753505628, 0.0, 0.0, 0.5448608416905849, 0.005644290758731727, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4689142754019952, 0.0, 0.00039031599380590526, 0.010175747938072128, 0.0, 0.0, 0.0, 0.0008842445754996234, 0.0, 0.0, 0.6689560919488968, 0.10178439680971307, 0.7089823411348399, 0.0, 0.0, 0.0, 0.0]
- Per Category Accuracy: [nan, 0.6798160901382586, 0.8601972223213155, 0.001563543652833044, 0.0005586801134972854, 0.004789605465686377, nan, 0.00024743825184288725, 0.0, 0.0, 0.8407289173400536, 0.012641370267169317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7574833533176979, 0.0, 0.00039110009377117975, 0.013959849889225483, 0.0, nan, 0.0, 0.0009309900323061499, 0.0, 0.0, 0.9337304207449932, 0.12865528611713883, 0.8019892660736478, 0.0, 0.0, 0.0, 0.0]

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                                                        | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 2.8872        | 0.5   | 20   | 3.1018          | 0.0995   | 0.1523        | 0.6415           | [0.0, 0.3982872425364927, 0.6582689116809847, 0.0, 0.00044314555867048773, 0.019651883205738383, 0.0, 0.0006528617866575068, 0.0, 0.0, 0.4861235900758522, 0.003961411405960721, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4437814560942763, 0.0, 1.1600860783870164e-06, 0.019965880301918204, 0.0, 0.0, 0.0, 0.0074026601990928, 0.0, 0.0, 0.666238976894996, 0.13012673492067245, 0.6486315429686865, 0.0, 2.0656177918545805e-05, 0.0001944735843164534, 0.0] | [nan, 0.6263716501798601, 0.8841421548179447, 0.0, 0.00044410334445801165, 0.020659891877382746, nan, 0.0006731258604635891, 0.0, 0.0, 0.8403154629142631, 0.017886412063596133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6324385775164868, 0.0, 1.160534402881839e-06, 0.06036834410935781, 0.0, nan, 0.0, 0.010232933175604348, 0.0, 0.0, 0.9320173945724101, 0.15828224740687694, 0.6884182010535304, 0.0, 2.3169780427714147e-05, 0.00019505205451704924, 0.0] |
| 2.6167        | 1.0   | 40   | 2.6306          | 0.1027   | 0.1574        | 0.6552           | [0.0, 0.40932069741697885, 0.6666047315185674, 0.0015527279135260222, 0.000557997451181134, 0.004734463745284192, 0.0, 0.00024311836753505628, 0.0, 0.0, 0.5448608416905849, 0.005644290758731727, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4689142754019952, 0.0, 0.00039031599380590526, 0.010175747938072128, 0.0, 0.0, 0.0, 0.0008842445754996234, 0.0, 0.0, 0.6689560919488968, 0.10178439680971307, 0.7089823411348399, 0.0, 0.0, 0.0, 0.0]                | [nan, 0.6798160901382586, 0.8601972223213155, 0.001563543652833044, 0.0005586801134972854, 0.004789605465686377, nan, 0.00024743825184288725, 0.0, 0.0, 0.8407289173400536, 0.012641370267169317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7574833533176979, 0.0, 0.00039110009377117975, 0.013959849889225483, 0.0, nan, 0.0, 0.0009309900323061499, 0.0, 0.0, 0.9337304207449932, 0.12865528611713883, 0.8019892660736478, 0.0, 0.0, 0.0, 0.0]                   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1