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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-v-mesh-0
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-v-mesh-0
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/vehicle_segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Mean Iou: 0.4403
- Mean Accuracy: 0.8806
- Overall Accuracy: 0.8806
- Accuracy Background: nan
- Accuracy Windows: 0.8806
- Iou Background: 0.0
- Iou Windows: 0.8806
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Windows | Iou Background | Iou Windows |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.2932 | 0.16 | 20 | 0.3269 | 0.2578 | 0.5156 | 0.5156 | nan | 0.5156 | 0.0 | 0.5156 |
| 0.1417 | 0.31 | 40 | 0.1235 | 0.3790 | 0.7580 | 0.7580 | nan | 0.7580 | 0.0 | 0.7580 |
| 0.0952 | 0.47 | 60 | 0.1245 | 0.4606 | 0.9211 | 0.9211 | nan | 0.9211 | 0.0 | 0.9211 |
| 0.0778 | 0.62 | 80 | 0.0628 | 0.4042 | 0.8084 | 0.8084 | nan | 0.8084 | 0.0 | 0.8084 |
| 0.0448 | 0.78 | 100 | 0.0512 | 0.4161 | 0.8322 | 0.8322 | nan | 0.8322 | 0.0 | 0.8322 |
| 0.0323 | 0.94 | 120 | 0.0435 | 0.4167 | 0.8334 | 0.8334 | nan | 0.8334 | 0.0 | 0.8334 |
| 0.0337 | 1.09 | 140 | 0.0405 | 0.4131 | 0.8262 | 0.8262 | nan | 0.8262 | 0.0 | 0.8262 |
| 0.0586 | 1.25 | 160 | 0.0409 | 0.4509 | 0.9017 | 0.9017 | nan | 0.9017 | 0.0 | 0.9017 |
| 0.0591 | 1.41 | 180 | 0.0404 | 0.4310 | 0.8620 | 0.8620 | nan | 0.8620 | 0.0 | 0.8620 |
| 0.0953 | 1.56 | 200 | 0.0386 | 0.4366 | 0.8732 | 0.8732 | nan | 0.8732 | 0.0 | 0.8732 |
| 0.0607 | 1.72 | 220 | 0.0374 | 0.4414 | 0.8828 | 0.8828 | nan | 0.8828 | 0.0 | 0.8828 |
| 0.0387 | 1.88 | 240 | 0.0360 | 0.4403 | 0.8806 | 0.8806 | nan | 0.8806 | 0.0 | 0.8806 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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