---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
  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-oct-22

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the adsjnajefwnb/di dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1425
- Mean Iou: 0.4608
- Mean Accuracy: 0.6144
- Overall Accuracy: 0.9524
- Accuracy Unlabeled: nan
- Accuracy Anqvan: nan
- Accuracy Baohuxiang: nan
- Accuracy Biaozhi: nan
- Accuracy Dianlan: 0.9591
- Accuracy Miehuoqi: 0.8841
- Accuracy Zhaoming: 0.0
- Iou Unlabeled: 0.0
- Iou Anqvan: nan
- Iou Baohuxiang: nan
- Iou Biaozhi: nan
- Iou Dianlan: 0.9591
- Iou Miehuoqi: 0.8841
- Iou Zhaoming: 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Anqvan | Accuracy Baohuxiang | Accuracy Biaozhi | Accuracy Dianlan | Accuracy Miehuoqi | Accuracy Zhaoming | Iou Unlabeled | Iou Anqvan | Iou Baohuxiang | Iou Biaozhi | Iou Dianlan | Iou Miehuoqi | Iou Zhaoming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:----------------:|:----------------:|:-----------------:|:-----------------:|:-------------:|:----------:|:--------------:|:-----------:|:-----------:|:------------:|:------------:|
| 1.5348        | 10.0  | 20   | 1.8080          | 0.3181   | 0.6370        | 0.9720           | nan                | nan             | nan                 | nan              | 0.9776           | 0.9334            | 0.0               | 0.0           | nan        | 0.0            | 0.0         | 0.9759      | 0.9328       | 0.0          |
| 1.5003        | 20.0  | 40   | 1.5287          | 0.3865   | 0.6442        | 0.9726           | nan                | nan             | nan                 | nan              | 0.9773           | 0.9552            | 0.0               | 0.0           | nan        | nan            | 0.0         | 0.9773      | 0.9552       | 0.0          |
| 1.4909        | 30.0  | 60   | 1.2543          | 0.4770   | 0.6359        | 0.9695           | nan                | nan             | nan                 | nan              | 0.9750           | 0.9328            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9750      | 0.9328       | 0.0          |
| 1.331         | 40.0  | 80   | 1.1463          | 0.4671   | 0.6229        | 0.9613           | nan                | nan             | nan                 | nan              | 0.9677           | 0.9009            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9677      | 0.9009       | 0.0          |
| 1.0843        | 50.0  | 100  | 1.1425          | 0.4608   | 0.6144        | 0.9524           | nan                | nan             | nan                 | nan              | 0.9591           | 0.8841            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9591      | 0.8841       | 0.0          |


### Framework versions

- Transformers 4.46.3
- Pytorch 1.12.1+cu113
- Datasets 3.1.0
- Tokenizers 0.20.3