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---
license: other
base_model: nvidia/segformer-b3-finetuned-ade-512-512
tags:
- generated_from_keras_callback
model-index:
- name: chribark/segformer-b3-finetuned-ade-512-512-finetuned-UAVid
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# chribark/segformer-b3-finetuned-ade-512-512-finetuned-UAVid
This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b3-finetuned-ade-512-512) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3207
- Validation Loss: 0.3852
- Validation Mean Iou: 0.0093
- Validation Mean Accuracy: 0.0378
- Validation Overall Accuracy: 0.0336
- Validation Accuracy Clutter: 0.0293
- Validation Accuracy Building: 0.0228
- Validation Accuracy Road: 0.0896
- Validation Accuracy Static Car: 0.0006
- Validation Accuracy Tree: 0.1118
- Validation Accuracy Vegetation: 0.0103
- Validation Accuracy Human: 0.0
- Validation Accuracy Moving Car: nan
- Validation Iou Clutter: 0.0199
- Validation Iou Building: 0.0037
- Validation Iou Road: 0.0072
- Validation Iou Static Car: 0.0006
- Validation Iou Tree: 0.0427
- Validation Iou Vegetation: 0.0000
- Validation Iou Human: 0.0
- Validation Iou Moving Car: 0.0
- Epoch: 9
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Clutter | Validation Accuracy Building | Validation Accuracy Road | Validation Accuracy Static Car | Validation Accuracy Tree | Validation Accuracy Vegetation | Validation Accuracy Human | Validation Accuracy Moving Car | Validation Iou Clutter | Validation Iou Building | Validation Iou Road | Validation Iou Static Car | Validation Iou Tree | Validation Iou Vegetation | Validation Iou Human | Validation Iou Moving Car | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:---------------------------:|:----------------------------:|:------------------------:|:------------------------------:|:------------------------:|:------------------------------:|:-------------------------:|:------------------------------:|:----------------------:|:-----------------------:|:-------------------:|:-------------------------:|:-------------------:|:-------------------------:|:--------------------:|:-------------------------:|:-----:|
| 1.2150 | 0.6691 | 0.0149 | 0.0576 | 0.0526 | 0.0355 | 0.0698 | 0.1084 | 0.0000 | 0.1892 | 0.0 | 0.0 | nan | 0.0241 | 0.0108 | 0.0100 | 0.0000 | 0.0739 | 0.0 | 0.0 | 0.0 | 0 |
| 0.6829 | 0.5768 | 0.0213 | 0.0817 | 0.0809 | 0.0750 | 0.0083 | 0.2008 | 0.0 | 0.2878 | 0.0 | 0.0 | nan | 0.0501 | 0.0014 | 0.0151 | 0.0 | 0.1035 | 0.0 | 0.0 | 0.0 | 1 |
| 0.5679 | 0.4969 | 0.0151 | 0.0710 | 0.0516 | 0.0295 | 0.0240 | 0.2164 | 0.0000 | 0.2230 | 0.0041 | 0.0 | nan | 0.0206 | 0.0039 | 0.0151 | 0.0000 | 0.0814 | 0.0000 | 0.0 | 0.0 | 2 |
| 0.5010 | 0.4654 | 0.0135 | 0.0553 | 0.0499 | 0.0437 | 0.0160 | 0.1503 | 0.0000 | 0.1773 | 0.0 | 0.0 | nan | 0.0301 | 0.0026 | 0.0096 | 0.0000 | 0.0660 | 0.0 | 0.0 | 0.0 | 3 |
| 0.4501 | 0.4507 | 0.0107 | 0.0504 | 0.0370 | 0.0343 | 0.0176 | 0.1737 | 0.0001 | 0.1191 | 0.0082 | 0.0 | nan | 0.0228 | 0.0028 | 0.0130 | 0.0001 | 0.0469 | 0.0000 | 0.0 | 0.0 | 4 |
| 0.4229 | 0.4257 | 0.0116 | 0.0436 | 0.0445 | 0.0515 | 0.0120 | 0.1162 | 0.0002 | 0.1222 | 0.0031 | 0.0 | nan | 0.0344 | 0.0020 | 0.0087 | 0.0002 | 0.0471 | 0.0000 | 0.0 | 0.0 | 5 |
| 0.3823 | 0.4131 | 0.0127 | 0.0504 | 0.0455 | 0.0374 | 0.0124 | 0.1251 | 0.0004 | 0.1705 | 0.0072 | 0.0 | nan | 0.0251 | 0.0020 | 0.0098 | 0.0004 | 0.0639 | 0.0000 | 0.0 | 0.0 | 6 |
| 0.3610 | 0.4006 | 0.0121 | 0.0518 | 0.0421 | 0.0300 | 0.0162 | 0.1272 | 0.0004 | 0.1675 | 0.0215 | 0.0 | nan | 0.0207 | 0.0026 | 0.0096 | 0.0004 | 0.0631 | 0.0001 | 0.0 | 0.0 | 7 |
| 0.3428 | 0.3923 | 0.0119 | 0.0465 | 0.0430 | 0.0327 | 0.0166 | 0.0977 | 0.0003 | 0.1670 | 0.0113 | 0.0 | nan | 0.0223 | 0.0027 | 0.0078 | 0.0003 | 0.0617 | 0.0000 | 0.0 | 0.0 | 8 |
| 0.3207 | 0.3852 | 0.0093 | 0.0378 | 0.0336 | 0.0293 | 0.0228 | 0.0896 | 0.0006 | 0.1118 | 0.0103 | 0.0 | nan | 0.0199 | 0.0037 | 0.0072 | 0.0006 | 0.0427 | 0.0000 | 0.0 | 0.0 | 9 |
### Framework versions
- Transformers 4.40.2
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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