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---
license: other
base_model: nvidia/mit-b3
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b2-seed-67-v1
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. -->
# segformer-b2-seed-67-v1
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the unreal-hug/REAL_DATASET_SEG_331 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4746
- Mean Iou: 0.2841
- Mean Accuracy: 0.3507
- Overall Accuracy: 0.6084
- Accuracy Unlabeled: nan
- Accuracy Lv: 0.7915
- Accuracy Rv: 0.4646
- Accuracy Ra: 0.4834
- Accuracy La: 0.6858
- Accuracy Vs: 0.0
- Accuracy As: 0.0
- Accuracy Mk: 0.0
- Accuracy Tk: nan
- Accuracy Asd: 0.3160
- Accuracy Vsd: 0.2747
- Accuracy Ak: 0.4910
- Iou Unlabeled: 0.0
- Iou Lv: 0.7252
- Iou Rv: 0.4232
- Iou Ra: 0.4411
- Iou La: 0.5427
- Iou Vs: 0.0
- Iou As: 0.0
- Iou Mk: 0.0
- Iou Tk: nan
- Iou Asd: 0.2832
- Iou Vsd: 0.2342
- Iou Ak: 0.4759
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Rv | Accuracy Ra | Accuracy La | Accuracy Vs | Accuracy As | Accuracy Mk | Accuracy Tk | Accuracy Asd | Accuracy Vsd | Accuracy Ak | Iou Unlabeled | Iou Lv | Iou Rv | Iou Ra | Iou La | Iou Vs | Iou As | Iou Mk | Iou Tk | Iou Asd | Iou Vsd | Iou Ak |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-------:|:-------:|:------:|
| 1.2449 | 5.88 | 100 | 1.1508 | 0.1187 | 0.1954 | 0.4575 | nan | 0.8193 | 0.0533 | 0.1371 | 0.5424 | 0.0 | 0.0 | 0.0 | nan | 0.0171 | 0.0155 | 0.3697 | 0.0 | 0.5501 | 0.0518 | 0.1253 | 0.3509 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0170 | 0.0148 | 0.3145 |
| 0.7118 | 11.76 | 200 | 0.7012 | 0.1534 | 0.2007 | 0.4466 | nan | 0.7352 | 0.1138 | 0.2300 | 0.5548 | 0.0 | 0.0 | 0.0 | nan | 0.0168 | 0.0284 | 0.3280 | 0.0 | 0.6079 | 0.1081 | 0.2084 | 0.4120 | 0.0 | 0.0 | 0.0 | nan | 0.0167 | 0.0276 | 0.3064 |
| 0.5567 | 17.65 | 300 | 0.5686 | 0.1896 | 0.2372 | 0.4810 | nan | 0.6994 | 0.2332 | 0.3522 | 0.5913 | 0.0 | 0.0 | 0.0 | nan | 0.0389 | 0.0765 | 0.3806 | 0.0 | 0.6382 | 0.2142 | 0.3023 | 0.4563 | 0.0 | 0.0 | 0.0 | nan | 0.0386 | 0.0714 | 0.3649 |
| 0.5054 | 23.53 | 400 | 0.5441 | 0.2473 | 0.3075 | 0.5803 | nan | 0.7991 | 0.4241 | 0.4885 | 0.5970 | 0.0 | 0.0 | 0.0 | nan | 0.1535 | 0.1388 | 0.4745 | 0.0 | 0.7215 | 0.3725 | 0.4107 | 0.4908 | 0.0 | 0.0 | 0.0 | nan | 0.1486 | 0.1228 | 0.4537 |
| 0.4344 | 29.41 | 500 | 0.5188 | 0.2706 | 0.3382 | 0.5967 | nan | 0.7810 | 0.4337 | 0.4668 | 0.7031 | 0.0 | 0.0 | 0.0 | nan | 0.2612 | 0.2644 | 0.4721 | 0.0 | 0.7121 | 0.3916 | 0.4164 | 0.5372 | 0.0 | 0.0 | 0.0 | nan | 0.2398 | 0.2236 | 0.4558 |
| 0.3796 | 35.29 | 600 | 0.5032 | 0.2669 | 0.3315 | 0.5911 | nan | 0.7953 | 0.4343 | 0.4050 | 0.6920 | 0.0 | 0.0 | 0.0 | nan | 0.2841 | 0.2321 | 0.4717 | 0.0 | 0.7196 | 0.3965 | 0.3778 | 0.5273 | 0.0 | 0.0 | 0.0 | nan | 0.2589 | 0.1996 | 0.4568 |
| 0.3888 | 41.18 | 700 | 0.4801 | 0.2798 | 0.3461 | 0.6037 | nan | 0.7862 | 0.4532 | 0.4667 | 0.6983 | 0.0 | 0.0 | 0.0 | nan | 0.3065 | 0.2590 | 0.4908 | 0.0 | 0.7192 | 0.4127 | 0.4292 | 0.5444 | 0.0 | 0.0 | 0.0 | nan | 0.2756 | 0.2216 | 0.4746 |
| 0.3467 | 47.06 | 800 | 0.4753 | 0.2822 | 0.3478 | 0.6061 | nan | 0.7919 | 0.4585 | 0.4857 | 0.6814 | 0.0 | 0.0 | 0.0 | nan | 0.3131 | 0.2640 | 0.4831 | 0.0 | 0.7259 | 0.4196 | 0.4424 | 0.5402 | 0.0 | 0.0 | 0.0 | nan | 0.2813 | 0.2262 | 0.4685 |
| 0.3757 | 52.94 | 900 | 0.4746 | 0.2841 | 0.3507 | 0.6084 | nan | 0.7915 | 0.4646 | 0.4834 | 0.6858 | 0.0 | 0.0 | 0.0 | nan | 0.3160 | 0.2747 | 0.4910 | 0.0 | 0.7252 | 0.4232 | 0.4411 | 0.5427 | 0.0 | 0.0 | 0.0 | nan | 0.2832 | 0.2342 | 0.4759 |
| 0.3616 | 58.82 | 1000 | 0.4788 | 0.2860 | 0.3537 | 0.6116 | nan | 0.7931 | 0.4687 | 0.4837 | 0.6922 | 0.0 | 0.0 | 0.0 | nan | 0.3193 | 0.2830 | 0.4970 | 0.0 | 0.7262 | 0.4259 | 0.4411 | 0.5449 | 0.0 | 0.0 | 0.0 | nan | 0.2856 | 0.2407 | 0.4817 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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