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---
license: other
base_model: nvidia/mit-b2
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: model1
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. -->
# model1
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/i-SAID_custom_or_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1646
- Mean Iou: 0.2689
- Mean Accuracy: 0.3089
- Overall Accuracy: 0.3928
- Accuracy Background: nan
- Accuracy Ship: 0.7889
- Accuracy Small-vehicle: 0.3939
- Accuracy Tennis-court: 0.6399
- Accuracy Helicopter: nan
- Accuracy Basketball-court: 0.0
- Accuracy Ground-track-field: 0.4337
- Accuracy Swimming-pool: 0.6049
- Accuracy Harbor: 0.3386
- Accuracy Soccer-ball-field: 0.2551
- Accuracy Plane: 0.0001
- Accuracy Storage-tank: 0.0
- Accuracy Baseball-diamond: 0.5217
- Accuracy Large-vehicle: 0.3477
- Accuracy Bridge: 0.0
- Accuracy Roundabout: 0.0
- Iou Background: 0.0
- Iou Ship: 0.6137
- Iou Small-vehicle: 0.3354
- Iou Tennis-court: 0.6399
- Iou Helicopter: nan
- Iou Basketball-court: 0.0
- Iou Ground-track-field: 0.4084
- Iou Swimming-pool: 0.6049
- Iou Harbor: 0.3165
- Iou Soccer-ball-field: 0.2514
- Iou Plane: 0.0001
- Iou Storage-tank: 0.0
- Iou Baseball-diamond: 0.5217
- Iou Large-vehicle: 0.3418
- 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: 840
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:|
| 1.1466 | 1.0 | 105 | 0.3419 | 0.0260 | 0.0279 | 0.0687 | nan | 0.0068 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 |
| 0.3289 | 2.0 | 210 | 0.2301 | 0.1252 | 0.1441 | 0.2674 | nan | 0.5316 | 0.1793 | 0.6775 | nan | 0.0 | 0.0324 | 0.1854 | 0.1185 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2923 | 0.0 | 0.0 | 0.0 | 0.4189 | 0.1612 | 0.6752 | nan | 0.0 | 0.0321 | 0.1854 | 0.1157 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2898 | 0.0 | 0.0 |
| 0.1819 | 3.0 | 315 | 0.1965 | 0.1611 | 0.1937 | 0.3286 | nan | 0.7305 | 0.2842 | 0.4229 | nan | 0.0 | 0.3566 | 0.2424 | 0.1707 | 0.0739 | 0.0 | 0.0 | 0.0 | 0.4300 | 0.0 | 0.0 | 0.0 | 0.5605 | 0.2492 | 0.4229 | nan | 0.0 | 0.2817 | 0.2424 | 0.1637 | 0.0738 | 0.0 | 0.0 | 0.0 | 0.4223 | 0.0 | 0.0 |
| 0.1505 | 4.0 | 420 | 0.1760 | 0.1987 | 0.2352 | 0.3689 | nan | 0.7552 | 0.3079 | 0.5796 | nan | 0.0 | 0.4515 | 0.4367 | 0.2065 | 0.1437 | 0.0 | 0.0 | 0.0 | 0.4115 | 0.0 | 0.0 | 0.0 | 0.5715 | 0.2762 | 0.5790 | nan | 0.0 | 0.3752 | 0.4367 | 0.1957 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.4029 | 0.0 | 0.0 |
| 0.1269 | 5.0 | 525 | 0.1688 | 0.2239 | 0.2616 | 0.3561 | nan | 0.8249 | 0.3133 | 0.5309 | nan | 0.0 | 0.3966 | 0.6398 | 0.2513 | 0.1975 | 0.0003 | 0.0 | 0.1336 | 0.3738 | 0.0 | 0.0 | 0.0 | 0.6006 | 0.2833 | 0.5309 | nan | 0.0 | 0.3711 | 0.6398 | 0.2378 | 0.1957 | 0.0003 | 0.0 | 0.1336 | 0.3661 | 0.0 | 0.0 |
| 0.1012 | 6.0 | 630 | 0.1763 | 0.2563 | 0.3036 | 0.3830 | nan | 0.7977 | 0.4801 | 0.6774 | nan | 0.0 | 0.4913 | 0.7772 | 0.2993 | 0.2702 | 0.0 | 0.0 | 0.2024 | 0.2541 | 0.0 | 0.0 | 0.0 | 0.6060 | 0.3488 | 0.6774 | nan | 0.0 | 0.4359 | 0.7767 | 0.2816 | 0.2638 | 0.0 | 0.0 | 0.2024 | 0.2515 | 0.0 | 0.0 |
| 0.0996 | 7.0 | 735 | 0.1687 | 0.2515 | 0.2906 | 0.3644 | nan | 0.7947 | 0.3775 | 0.5884 | nan | 0.0 | 0.4452 | 0.5756 | 0.2734 | 0.2140 | 0.0 | 0.0 | 0.4769 | 0.3225 | 0.0 | 0.0 | 0.0 | 0.6093 | 0.3246 | 0.5884 | nan | 0.0 | 0.4081 | 0.5756 | 0.2599 | 0.2128 | 0.0 | 0.0 | 0.4769 | 0.3174 | 0.0 | 0.0 |
| 0.0945 | 8.0 | 840 | 0.1646 | 0.2689 | 0.3089 | 0.3928 | nan | 0.7889 | 0.3939 | 0.6399 | nan | 0.0 | 0.4337 | 0.6049 | 0.3386 | 0.2551 | 0.0001 | 0.0 | 0.5217 | 0.3477 | 0.0 | 0.0 | 0.0 | 0.6137 | 0.3354 | 0.6399 | nan | 0.0 | 0.4084 | 0.6049 | 0.3165 | 0.2514 | 0.0001 | 0.0 | 0.5217 | 0.3418 | 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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