transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
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: []
---

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# 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 bfwggggg/image-with-puzzle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2327
- Mean Iou: 0.4736
- Mean Accuracy: 0.9472
- Overall Accuracy: 0.9472
- Accuracy Unlabeled: nan
- Accuracy Missing-puzzle: 0.9472
- Iou Unlabeled: 0.0
- Iou Missing-puzzle: 0.9472

## 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 Missing-puzzle | Iou Unlabeled | Iou Missing-puzzle |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------------------:|:-------------:|:------------------:|
| 0.5575        | 5.0   | 20   | 0.6601          | 0.4998   | 0.9996        | 0.9996           | nan                | 0.9996                  | 0.0           | 0.9996             |
| 0.365         | 10.0  | 40   | 0.5628          | 0.4980   | 0.9960        | 0.9960           | nan                | 0.9960                  | 0.0           | 0.9960             |
| 0.2788        | 15.0  | 60   | 0.3816          | 0.4812   | 0.9624        | 0.9624           | nan                | 0.9624                  | 0.0           | 0.9624             |
| 0.2527        | 20.0  | 80   | 0.3869          | 0.4806   | 0.9611        | 0.9611           | nan                | 0.9611                  | 0.0           | 0.9611             |
| 0.2145        | 25.0  | 100  | 0.2733          | 0.4663   | 0.9326        | 0.9326           | nan                | 0.9326                  | 0.0           | 0.9326             |
| 0.206         | 30.0  | 120  | 0.2672          | 0.4739   | 0.9478        | 0.9478           | nan                | 0.9478                  | 0.0           | 0.9478             |
| 0.1866        | 35.0  | 140  | 0.2351          | 0.4667   | 0.9334        | 0.9334           | nan                | 0.9334                  | 0.0           | 0.9334             |
| 0.1696        | 40.0  | 160  | 0.2099          | 0.4749   | 0.9497        | 0.9497           | nan                | 0.9497                  | 0.0           | 0.9497             |
| 0.1639        | 45.0  | 180  | 0.2058          | 0.4723   | 0.9445        | 0.9445           | nan                | 0.9445                  | 0.0           | 0.9445             |
| 0.1719        | 50.0  | 200  | 0.2327          | 0.4736   | 0.9472        | 0.9472           | nan                | 0.9472                  | 0.0           | 0.9472             |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
