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---
license: other
tags:
- generated_from_trainer
base_model: nvidia/mit-b0
model-index:
- name: Aerial-Drone-Image-Segmentation
results: []
pipeline_tag: image-segmentation
---
<!-- 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. -->
# Aerial-Drone-Image-Segmentation
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)
It achieves the following results on the evaluation set:
- Loss: 0.8852
- Mean Iou: 0.2994
- Mean Accuracy: 0.3923
- Overall Accuracy: 0.7774
## Model description
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Evaluation Results
{'mean_iou': 0.27989828118195953,
'mean_accuracy': 0.3712316062110093,
'overall_accuracy': 0.7671712239583334,
'per_category_iou': array([ nan, 0.8560476 , 0.32234631, 0.76880948, 0.57517691,
0.43877125, 0.00114888, 0.14091442, 0.51807365, 0.76964765,
0.27391949, 0. , 0. , 0. , 0. ,
0.05778175, 0. , 0.45566807, 0. , 0.25864545,
0.48767764, 0. , 0.23313364, nan]),
'per_category_accuracy': array([ nan, 0.96170675, 0.43993514, 0.86977593, 0.8149788 ,
0.49739671, 0.00114987, 0.14445379, 0.80978302, 0.88661108,
0.46787116, 0. , 0. , 0. , 0. ,
0.05947339, 0. , 0.55639324, 0. , 0.38358184,
0.761303 , 0. , 0.51268161, nan])}
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|
| 2.7923 | 1.25 | 20 | 2.8338 | 0.0954 | 0.1626 | 0.5529 |
| 2.219 | 2.5 | 40 | 2.1391 | 0.1036 | 0.1666 | 0.5929 |
| 1.9451 | 3.75 | 60 | 1.7919 | 0.1154 | 0.1782 | 0.6129 |
| 1.7558 | 5.0 | 80 | 1.6767 | 0.1300 | 0.1961 | 0.6396 |
| 1.6381 | 6.25 | 100 | 1.5817 | 0.1383 | 0.2055 | 0.6550 |
| 1.5338 | 7.5 | 120 | 1.4816 | 0.1464 | 0.2140 | 0.6729 |
| 1.4478 | 8.75 | 140 | 1.4231 | 0.1529 | 0.2219 | 0.6823 |
| 1.361 | 10.0 | 160 | 1.3300 | 0.1637 | 0.2315 | 0.6975 |
| 1.306 | 11.25 | 180 | 1.3034 | 0.1737 | 0.2419 | 0.7060 |
| 1.2611 | 12.5 | 200 | 1.2692 | 0.1755 | 0.2450 | 0.7093 |
| 1.2317 | 13.75 | 220 | 1.2190 | 0.1821 | 0.2501 | 0.7145 |
| 1.1868 | 15.0 | 240 | 1.2063 | 0.1862 | 0.2539 | 0.7188 |
| 1.1628 | 16.25 | 260 | 1.1832 | 0.1909 | 0.2612 | 0.7234 |
| 1.1149 | 17.5 | 280 | 1.1368 | 0.2048 | 0.2739 | 0.7317 |
| 1.1009 | 18.75 | 300 | 1.1117 | 0.2232 | 0.2938 | 0.7387 |
| 1.0532 | 20.0 | 320 | 1.0923 | 0.2315 | 0.2997 | 0.7414 |
| 1.0464 | 21.25 | 340 | 1.0821 | 0.2408 | 0.3147 | 0.7480 |
| 1.0278 | 22.5 | 360 | 1.0541 | 0.2517 | 0.3277 | 0.7530 |
| 0.9945 | 23.75 | 380 | 1.0352 | 0.2612 | 0.3398 | 0.7573 |
| 0.9729 | 25.0 | 400 | 1.0207 | 0.2671 | 0.3511 | 0.7609 |
| 0.9527 | 26.25 | 420 | 1.0067 | 0.2684 | 0.3547 | 0.7609 |
| 0.9494 | 27.5 | 440 | 0.9870 | 0.2713 | 0.3548 | 0.7627 |
| 0.9287 | 28.75 | 460 | 0.9729 | 0.2745 | 0.3619 | 0.7640 |
| 0.9089 | 30.0 | 480 | 0.9561 | 0.2791 | 0.3640 | 0.7680 |
| 0.9064 | 31.25 | 500 | 0.9500 | 0.2799 | 0.3712 | 0.7672 |
| 0.8681 | 32.5 | 520 | 0.9397 | 0.2845 | 0.3749 | 0.7696 |
| 0.8677 | 33.75 | 540 | 0.9340 | 0.2835 | 0.3737 | 0.7692 |
| 0.8663 | 35.0 | 560 | 0.9243 | 0.2862 | 0.3755 | 0.7716 |
| 0.8629 | 36.25 | 580 | 0.9173 | 0.2869 | 0.3766 | 0.7719 |
| 0.8542 | 37.5 | 600 | 0.9112 | 0.2908 | 0.3810 | 0.7740 |
| 0.8391 | 38.75 | 620 | 0.9050 | 0.2904 | 0.3812 | 0.7734 |
| 0.8392 | 40.0 | 640 | 0.9027 | 0.2917 | 0.3818 | 0.7734 |
| 0.8306 | 41.25 | 660 | 0.8949 | 0.2941 | 0.3841 | 0.7755 |
| 0.8213 | 42.5 | 680 | 0.8936 | 0.2958 | 0.3875 | 0.7760 |
| 0.8406 | 43.75 | 700 | 0.8910 | 0.2964 | 0.3879 | 0.7763 |
| 0.8254 | 45.0 | 720 | 0.8889 | 0.2981 | 0.3897 | 0.7764 |
| 0.8202 | 46.25 | 740 | 0.8880 | 0.2985 | 0.3917 | 0.7767 |
| 0.8013 | 47.5 | 760 | 0.8891 | 0.2989 | 0.3923 | 0.7767 |
| 0.8188 | 48.75 | 780 | 0.8861 | 0.2994 | 0.3926 | 0.7772 |
| 0.8089 | 50.0 | 800 | 0.8852 | 0.2994 | 0.3923 | 0.7774 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2 |