transformers tensorboard safetensors segformer generated_from_trainer base_model:DiTo97/binarization-segformer-b3 license:openrail endpoints_compatible region:us
---
license: openrail
base_model: DiTo97/binarization-segformer-b3
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-scene-parse-150
  results: []
---

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# segformer-b0-scene-parse-150

This model is a fine-tuned version of [DiTo97/binarization-segformer-b3](https://huggingface.co/DiTo97/binarization-segformer-b3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1587
- Mean Iou: 0.5023
- Mean Accuracy: 0.5190
- Overall Accuracy: 0.9675
- Per Category Iou: [0.037137244396821145, 0.9674848565106324]
- Per Category Accuracy: [0.042339407512979745, 0.9957297061214373]

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                           | Per Category Accuracy                       |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------:|:-------------------------------------------:|
| No log        | 1.0   | 133  | 1.3526          | 0.0201   | 0.5031        | 0.0397           | [0.02975604398011791, 0.01052369675464703] | [0.9955767077267638, 0.010525115803790754]  |
| No log        | 2.0   | 266  | 0.3525          | 0.4867   | 0.5015        | 0.9688           | [0.004574905664611646, 0.9688262452629481] | [0.004842376734873935, 0.9982177109197249]  |
| No log        | 3.0   | 399  | 0.2574          | 0.4889   | 0.5038        | 0.9688           | [0.008923098533993973, 0.9688274858196525] | [0.00948454307916794, 0.9980818849540622]   |
| 0.6609        | 4.0   | 532  | 0.1598          | 0.4864   | 0.5012        | 0.9704           | [0.0024469999746512434, 0.970368841777746] | [0.0024568190301672943, 0.9998776738732703] |
| 0.6609        | 5.0   | 665  | 0.1452          | 0.4856   | 0.5004        | 0.9704           | [0.000846027983221573, 0.9703602128333251] | [0.0008483491126268282, 0.9999163630668406] |
| 0.6609        | 6.0   | 798  | 0.1447          | 0.4905   | 0.5053        | 0.9699           | [0.011117650258900796, 0.9698808507647041] | [0.01144253283111066, 0.9991091658009873]   |
| 0.6609        | 7.0   | 931  | 0.1675          | 0.4998   | 0.5157        | 0.9684           | [0.031259421511502704, 0.9683566316036148] | [0.03448030133360481, 0.9968589340278906]   |
| 0.4063        | 8.0   | 1064 | 0.1428          | 0.4930   | 0.5078        | 0.9697           | [0.01628017129826309, 0.9696496379088535]  | [0.016970375648987072, 0.9987075843853725]  |
| 0.4063        | 9.0   | 1197 | 0.1923          | 0.5086   | 0.5297        | 0.9641           | [0.053108550932340105, 0.9640450905222431] | [0.0680308120397706, 0.9914344815058413]    |
| 0.4063        | 10.0  | 1330 | 0.1587          | 0.5023   | 0.5190        | 0.9675           | [0.037137244396821145, 0.9674848565106324] | [0.042339407512979745, 0.9957297061214373]  |


### Framework versions

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