---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-dots
  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-dots

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1328
- Mean Iou: 0.3201
- Mean Accuracy: 0.6402
- Overall Accuracy: 0.6402
- Accuracy Unlabeled: nan
- Accuracy Dots: 0.6402
- Iou Unlabeled: 0.0
- Iou Dots: 0.6402

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dots | Iou Unlabeled | Iou Dots |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
| 0.5642        | 4.0   | 20   | 0.6209          | 0.4838   | 0.9677        | 0.9677           | nan                | 0.9677        | 0.0           | 0.9677   |
| 0.4154        | 8.0   | 40   | 0.4119          | 0.2969   | 0.5939        | 0.5939           | nan                | 0.5939        | 0.0           | 0.5939   |
| 0.3246        | 12.0  | 60   | 0.2900          | 0.3123   | 0.6246        | 0.6246           | nan                | 0.6246        | 0.0           | 0.6246   |
| 0.2898        | 16.0  | 80   | 0.3168          | 0.4260   | 0.8520        | 0.8520           | nan                | 0.8520        | 0.0           | 0.8520   |
| 0.2419        | 20.0  | 100  | 0.2201          | 0.3446   | 0.6892        | 0.6892           | nan                | 0.6892        | 0.0           | 0.6892   |
| 0.2042        | 24.0  | 120  | 0.2199          | 0.3213   | 0.6426        | 0.6426           | nan                | 0.6426        | 0.0           | 0.6426   |
| 0.1662        | 28.0  | 140  | 0.1797          | 0.3002   | 0.6005        | 0.6005           | nan                | 0.6005        | 0.0           | 0.6005   |
| 0.1757        | 32.0  | 160  | 0.1611          | 0.2919   | 0.5839        | 0.5839           | nan                | 0.5839        | 0.0           | 0.5839   |
| 0.1473        | 36.0  | 180  | 0.1477          | 0.3219   | 0.6439        | 0.6439           | nan                | 0.6439        | 0.0           | 0.6439   |
| 0.1645        | 40.0  | 200  | 0.1448          | 0.3267   | 0.6534        | 0.6534           | nan                | 0.6534        | 0.0           | 0.6534   |
| 0.1576        | 44.0  | 220  | 0.1389          | 0.3377   | 0.6754        | 0.6754           | nan                | 0.6754        | 0.0           | 0.6754   |
| 0.1381        | 48.0  | 240  | 0.1328          | 0.3201   | 0.6402        | 0.6402           | nan                | 0.6402        | 0.0           | 0.6402   |


### Framework versions

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