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---
license: other
base_model: nvidia/mit-b2
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: model2
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. -->
# model2
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/isaid_sam_predicted dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2318
- Mean Iou: 0.2504
- Mean Accuracy: 0.3019
- Overall Accuracy: 0.4542
- Accuracy Background: nan
- Accuracy Ship: 0.6330
- Accuracy Small-vehicle: 0.4644
- Accuracy Tennis-court: 0.0280
- Accuracy Helicopter: nan
- Accuracy Basketball-court: 0.0
- Accuracy Ground-track-field: 0.6010
- Accuracy Swimming-pool: nan
- Accuracy Harbor: 0.4575
- Accuracy Soccer-ball-field: 0.7776
- Accuracy Plane: nan
- Accuracy Storage-tank: 0.0
- Accuracy Baseball-diamond: nan
- Accuracy Large-vehicle: 0.3594
- Accuracy Bridge: 0.0
- Accuracy Roundabout: 0.0
- Iou Background: 0.0
- Iou Ship: 0.5194
- Iou Small-vehicle: 0.4368
- Iou Tennis-court: 0.0280
- Iou Helicopter: nan
- Iou Basketball-court: 0.0
- Iou Ground-track-field: 0.5492
- Iou Swimming-pool: nan
- Iou Harbor: 0.3611
- Iou Soccer-ball-field: 0.7592
- Iou Plane: nan
- Iou Storage-tank: 0.0
- Iou Baseball-diamond: nan
- Iou Large-vehicle: 0.3508
- Iou Bridge: 0.0
- Iou Roundabout: 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 1345
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Ship | Accuracy Small-vehicle | Accuracy Tennis-court | Accuracy Helicopter | Accuracy Basketball-court | Accuracy Ground-track-field | Accuracy Swimming-pool | Accuracy Harbor | Accuracy Soccer-ball-field | Accuracy Plane | Accuracy Storage-tank | Accuracy Baseball-diamond | Accuracy Large-vehicle | Accuracy Bridge | Accuracy Roundabout | Iou Background | Iou Ship | Iou Small-vehicle | Iou Tennis-court | Iou Helicopter | Iou Basketball-court | Iou Ground-track-field | Iou Swimming-pool | Iou Harbor | Iou Soccer-ball-field | Iou Plane | Iou Storage-tank | Iou Baseball-diamond | Iou Large-vehicle | Iou Bridge | Iou Roundabout |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:|
| 1.1413 | 1.0 | 113 | 0.5054 | 0.0431 | 0.0841 | 0.0445 | nan | 0.0611 | 0.0179 | 0.0079 | nan | 0.0 | 0.0 | nan | 0.8374 | 0.0 | nan | 0.0 | nan | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0592 | 0.0178 | 0.0079 | nan | 0.0 | 0.0 | nan | 0.4319 | 0.0 | nan | 0.0 | nan | 0.0010 | 0.0 | 0.0 |
| 0.326 | 2.0 | 226 | 0.3240 | 0.0756 | 0.1192 | 0.2144 | nan | 0.0433 | 0.1690 | 0.0 | nan | 0.0 | 0.0 | nan | 0.8062 | 0.0 | nan | 0.0 | nan | 0.2926 | 0.0 | 0.0 | 0.0 | 0.0430 | 0.1614 | 0.0 | nan | 0.0 | 0.0 | nan | 0.4129 | 0.0 | nan | 0.0 | nan | 0.2904 | 0.0 | 0.0 |
| 0.1849 | 3.0 | 339 | 0.2807 | 0.1589 | 0.2164 | 0.3238 | nan | 0.3520 | 0.3125 | 0.0 | nan | 0.0 | 0.3563 | nan | 0.6252 | 0.4509 | nan | 0.0 | nan | 0.2835 | 0.0 | 0.0 | 0.0 | 0.3236 | 0.2894 | 0.0 | nan | 0.0 | 0.3265 | 0.0 | 0.3954 | 0.4506 | nan | 0.0 | nan | 0.2807 | 0.0 | 0.0 |
| 0.1341 | 4.0 | 452 | 0.2694 | 0.1618 | 0.2309 | 0.3055 | nan | 0.2089 | 0.3628 | 0.0188 | nan | 0.0 | 0.4866 | nan | 0.7552 | 0.5206 | nan | 0.0 | nan | 0.1866 | 0.0 | 0.0 | 0.0 | 0.2004 | 0.3303 | 0.0188 | nan | 0.0 | 0.4268 | 0.0 | 0.4221 | 0.5205 | nan | 0.0 | nan | 0.1840 | 0.0 | 0.0 |
| 0.1282 | 5.0 | 565 | 0.2631 | 0.2057 | 0.2726 | 0.3396 | nan | 0.4061 | 0.3347 | 0.0292 | nan | 0.0 | 0.6126 | nan | 0.6152 | 0.8252 | nan | 0.0 | nan | 0.1751 | 0.0 | 0.0 | 0.0 | 0.3667 | 0.3169 | 0.0292 | nan | 0.0 | 0.4767 | nan | 0.3995 | 0.7049 | nan | 0.0 | nan | 0.1745 | 0.0 | 0.0 |
| 0.1138 | 6.0 | 678 | 0.2418 | 0.1949 | 0.2558 | 0.3865 | nan | 0.2362 | 0.3709 | 0.0122 | nan | 0.0 | 0.6128 | nan | 0.6627 | 0.5823 | nan | 0.0 | nan | 0.3365 | 0.0 | 0.0 | 0.0 | 0.2249 | 0.3444 | 0.0122 | nan | 0.0 | 0.4625 | nan | 0.3921 | 0.5725 | nan | 0.0 | nan | 0.3301 | 0.0 | 0.0 |
| 0.1049 | 7.0 | 791 | 0.2345 | 0.2013 | 0.2623 | 0.4725 | nan | 0.3186 | 0.4071 | 0.0827 | nan | 0.0 | 0.1697 | nan | 0.7809 | 0.6140 | nan | 0.0 | nan | 0.5118 | 0.0 | 0.0 | 0.0 | 0.2927 | 0.3851 | 0.0827 | nan | 0.0 | 0.1679 | nan | 0.4702 | 0.5212 | nan | 0.0 | nan | 0.4961 | 0.0 | 0.0 |
| 0.0829 | 8.0 | 904 | 0.2351 | 0.2194 | 0.2818 | 0.4348 | nan | 0.1689 | 0.4289 | 0.0980 | nan | 0.0 | 0.5547 | nan | 0.7050 | 0.7860 | nan | 0.0 | nan | 0.3580 | 0.0 | 0.0 | 0.0 | 0.1619 | 0.4048 | 0.0980 | nan | 0.0 | 0.5205 | nan | 0.3967 | 0.7023 | nan | 0.0 | nan | 0.3490 | 0.0 | 0.0 |
| 0.0922 | 9.0 | 1017 | 0.2350 | 0.2549 | 0.3103 | 0.5060 | nan | 0.4729 | 0.4726 | 0.0572 | nan | 0.0 | 0.5679 | nan | 0.5794 | 0.7942 | nan | 0.0 | nan | 0.4690 | 0.0 | 0.0 | 0.0 | 0.4143 | 0.4398 | 0.0572 | nan | 0.0 | 0.5293 | nan | 0.4010 | 0.7613 | nan | 0.0 | nan | 0.4563 | 0.0 | 0.0 |
| 0.0717 | 10.0 | 1130 | 0.2399 | 0.2344 | 0.2871 | 0.4150 | nan | 0.4512 | 0.4155 | 0.0169 | nan | 0.0 | 0.5706 | nan | 0.6279 | 0.7676 | nan | 0.0 | nan | 0.3089 | 0.0 | 0.0 | 0.0 | 0.3995 | 0.3949 | 0.0169 | nan | 0.0 | 0.5351 | nan | 0.4246 | 0.7393 | nan | 0.0 | nan | 0.3023 | 0.0 | 0.0 |
| 0.0787 | 11.0 | 1243 | 0.2228 | 0.2578 | 0.3105 | 0.4726 | nan | 0.6679 | 0.4378 | 0.0666 | nan | 0.0 | 0.5865 | nan | 0.4684 | 0.7796 | nan | 0.0 | nan | 0.4087 | 0.0 | 0.0 | 0.0 | 0.5359 | 0.4172 | 0.0666 | nan | 0.0 | 0.5456 | nan | 0.3785 | 0.7528 | nan | 0.0 | nan | 0.3975 | 0.0 | 0.0 |
| 0.0787 | 11.9 | 1345 | 0.2318 | 0.2504 | 0.3019 | 0.4542 | nan | 0.6330 | 0.4644 | 0.0280 | nan | 0.0 | 0.6010 | nan | 0.4575 | 0.7776 | nan | 0.0 | nan | 0.3594 | 0.0 | 0.0 | 0.0 | 0.5194 | 0.4368 | 0.0280 | nan | 0.0 | 0.5492 | nan | 0.3611 | 0.7592 | nan | 0.0 | nan | 0.3508 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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