---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-oldapp-oct-1
  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-oldapp-oct-1

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/oldapptiles5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2630
- Mean Iou: 0.4984
- Mean Accuracy: 0.4989
- Overall Accuracy: 0.9967
- Accuracy Abnormality: 0.0
- Iou Abnormality: 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Abnormality | Iou Abnormality |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:|
| 0.6912        | 0.7143 | 10   | 0.6575          | 0.4714   | 0.4784        | 0.9426           | 0.0133               | 0.0002          |
| 0.5802        | 1.4286 | 20   | 0.5878          | 0.4942   | 0.4947        | 0.9884           | 0.0                  | 0.0             |
| 0.5498        | 2.1429 | 30   | 0.4770          | 0.4984   | 0.4989        | 0.9968           | 0.0                  | 0.0             |
| 0.6084        | 2.8571 | 40   | 0.4125          | 0.4971   | 0.4976        | 0.9941           | 0.0                  | 0.0             |
| 0.4675        | 3.5714 | 50   | 0.4355          | 0.4885   | 0.4992        | 0.9761           | 0.0213               | 0.0009          |
| 0.3863        | 4.2857 | 60   | 0.3699          | 0.4965   | 0.5005        | 0.9920           | 0.0081               | 0.0010          |
| 0.3954        | 5.0    | 70   | 0.3401          | 0.4983   | 0.4989        | 0.9967           | 0.0                  | 0.0             |
| 0.3286        | 5.7143 | 80   | 0.3279          | 0.4983   | 0.4988        | 0.9967           | 0.0                  | 0.0             |
| 0.3458        | 6.4286 | 90   | 0.2908          | 0.4974   | 0.4979        | 0.9948           | 0.0                  | 0.0             |
| 0.3559        | 7.1429 | 100  | 0.2693          | 0.4989   | 0.4994        | 0.9978           | 0.0                  | 0.0             |
| 0.3196        | 7.8571 | 110  | 0.2596          | 0.4977   | 0.4982        | 0.9954           | 0.0                  | 0.0             |
| 0.3109        | 8.5714 | 120  | 0.2915          | 0.4958   | 0.4963        | 0.9916           | 0.0                  | 0.0             |
| 0.2711        | 9.2857 | 130  | 0.2720          | 0.4991   | 0.4997        | 0.9983           | 0.0                  | 0.0             |
| 0.3051        | 10.0   | 140  | 0.2630          | 0.4984   | 0.4989        | 0.9967           | 0.0                  | 0.0             |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3