---
library_name: transformers
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-ce-head-batch1
  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-b5-finetuned-ce-head-batch1

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0440
- Mean Iou: 0.7859
- Mean Accuracy: 0.8788
- Overall Accuracy: 0.9823
- Accuracy Bg: 0.9897
- Accuracy Head: 0.7680
- Iou Bg: 0.9819
- Iou Head: 0.5899

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.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: 80

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Head | Iou Bg | Iou Head |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-------------:|:------:|:--------:|
| 0.0884        | 2.9412  | 100  | 0.1053          | 0.6562   | 0.7059        | 0.9673           | 0.9907      | 0.4211        | 0.9667 | 0.3456   |
| 0.1706        | 5.8824  | 200  | 0.0874          | 0.6241   | 0.6473        | 0.9660           | 0.9976      | 0.2969        | 0.9655 | 0.2828   |
| 0.0701        | 8.8235  | 300  | 0.0642          | 0.7050   | 0.7350        | 0.9749           | 0.9963      | 0.4736        | 0.9744 | 0.4357   |
| 0.0395        | 11.7647 | 400  | 0.0686          | 0.7212   | 0.7506        | 0.9742           | 0.9964      | 0.5048        | 0.9736 | 0.4688   |
| 0.0363        | 14.7059 | 500  | 0.0587          | 0.7770   | 0.8551        | 0.9765           | 0.9884      | 0.7217        | 0.9758 | 0.5783   |
| 0.1636        | 17.6471 | 600  | 0.0474          | 0.8091   | 0.9205        | 0.9800           | 0.9853      | 0.8556        | 0.9792 | 0.6389   |
| 0.0794        | 20.5882 | 700  | 0.0484          | 0.8021   | 0.8651        | 0.9799           | 0.9914      | 0.7389        | 0.9792 | 0.6251   |
| 0.0242        | 23.5294 | 800  | 0.0398          | 0.8238   | 0.8610        | 0.9845           | 0.9959      | 0.7261        | 0.9840 | 0.6635   |
| 0.0545        | 26.4706 | 900  | 0.0363          | 0.8395   | 0.8948        | 0.9855           | 0.9937      | 0.7960        | 0.9850 | 0.6941   |
| 0.0135        | 29.4118 | 1000 | 0.0381          | 0.8070   | 0.8337        | 0.9837           | 0.9973      | 0.6701        | 0.9833 | 0.6308   |
| 0.0212        | 32.3529 | 1100 | 0.0348          | 0.8396   | 0.8882        | 0.9856           | 0.9945      | 0.7819        | 0.9852 | 0.6939   |
| 0.0131        | 35.2941 | 1200 | 0.0405          | 0.8257   | 0.8536        | 0.9848           | 0.9971      | 0.7100        | 0.9844 | 0.6671   |
| 0.0212        | 38.2353 | 1300 | 0.0397          | 0.8341   | 0.9150        | 0.9841           | 0.9903      | 0.8398        | 0.9835 | 0.6848   |
| 0.0403        | 41.1765 | 1400 | 0.0335          | 0.8373   | 0.8855        | 0.9856           | 0.9946      | 0.7763        | 0.9851 | 0.6894   |
| 0.0611        | 44.1176 | 1500 | 0.0383          | 0.8352   | 0.8728        | 0.9847           | 0.9957      | 0.7498        | 0.9842 | 0.6863   |
| 0.0338        | 47.0588 | 1600 | 0.0305          | 0.8581   | 0.9177        | 0.9871           | 0.9933      | 0.8420        | 0.9866 | 0.7296   |
| 0.0286        | 50.0    | 1700 | 0.0462          | 0.8070   | 0.8293        | 0.9825           | 0.9978      | 0.6608        | 0.9819 | 0.6321   |
| 0.0459        | 52.9412 | 1800 | 0.0302          | 0.8634   | 0.9143        | 0.9878           | 0.9945      | 0.8341        | 0.9874 | 0.7393   |
| 0.0473        | 55.8824 | 1900 | 0.0389          | 0.8210   | 0.8524        | 0.9846           | 0.9967      | 0.7082        | 0.9842 | 0.6578   |
| 0.0059        | 58.8235 | 2000 | 0.0331          | 0.8454   | 0.8753        | 0.9868           | 0.9970      | 0.7536        | 0.9864 | 0.7044   |
| 0.0432        | 61.7647 | 2100 | 0.0412          | 0.8239   | 0.8554        | 0.9848           | 0.9967      | 0.7141        | 0.9843 | 0.6636   |
| 0.0153        | 64.7059 | 2200 | 0.0322          | 0.8465   | 0.8869        | 0.9866           | 0.9956      | 0.7781        | 0.9862 | 0.7068   |
| 0.0181        | 67.6471 | 2300 | 0.0335          | 0.8341   | 0.8716        | 0.9856           | 0.9959      | 0.7473        | 0.9852 | 0.6830   |
| 0.039         | 70.5882 | 2400 | 0.0347          | 0.8340   | 0.8794        | 0.9853           | 0.9949      | 0.7638        | 0.9848 | 0.6831   |
| 0.0212        | 73.5294 | 2500 | 0.0425          | 0.8014   | 0.8340        | 0.9816           | 0.9962      | 0.6718        | 0.9811 | 0.6218   |
| 0.0113        | 76.4706 | 2600 | 0.0318          | 0.8412   | 0.8837        | 0.9861           | 0.9953      | 0.7720        | 0.9856 | 0.6968   |
| 0.0642        | 79.4118 | 2700 | 0.0415          | 0.8153   | 0.8450        | 0.9831           | 0.9967      | 0.6932        | 0.9825 | 0.6481   |


### Framework versions

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