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---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-19-2024
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-b0-finetuned-segments-SixrayKnife8-19-2024
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1817
- Mean Iou: 0.8160
- Mean Accuracy: 0.8823
- Overall Accuracy: 0.9881
- Accuracy Bkg: 0.9954
- Accuracy Gun: 0.7759
- Accuracy Knife: 0.8755
- Iou Bkg: 0.9890
- Iou Gun: 0.7014
- Iou Knife: 0.7574
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bkg | Accuracy Gun | Accuracy Knife | Iou Bkg | Iou Gun | Iou Knife |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:--------------:|:-------:|:-------:|:---------:|
| 0.4406 | 5.0 | 20 | 0.4093 | 0.7210 | 0.7883 | 0.9804 | 0.9938 | 0.6719 | 0.6991 | 0.9807 | 0.5730 | 0.6092 |
| 0.3699 | 10.0 | 40 | 0.3327 | 0.7327 | 0.7880 | 0.9819 | 0.9954 | 0.6559 | 0.7128 | 0.9824 | 0.5724 | 0.6432 |
| 0.31 | 15.0 | 60 | 0.3035 | 0.7698 | 0.8614 | 0.9842 | 0.9926 | 0.7207 | 0.8709 | 0.9853 | 0.6217 | 0.7023 |
| 0.2852 | 20.0 | 80 | 0.2649 | 0.7817 | 0.8711 | 0.9850 | 0.9928 | 0.7453 | 0.8752 | 0.9860 | 0.6423 | 0.7168 |
| 0.2583 | 25.0 | 100 | 0.2329 | 0.7936 | 0.8693 | 0.9863 | 0.9943 | 0.7497 | 0.8639 | 0.9873 | 0.6628 | 0.7307 |
| 0.2521 | 30.0 | 120 | 0.2194 | 0.7975 | 0.8778 | 0.9867 | 0.9942 | 0.7530 | 0.8862 | 0.9879 | 0.6731 | 0.7316 |
| 0.2357 | 35.0 | 140 | 0.2044 | 0.8042 | 0.8804 | 0.9871 | 0.9944 | 0.7635 | 0.8833 | 0.9881 | 0.6789 | 0.7456 |
| 0.2198 | 40.0 | 160 | 0.1929 | 0.8126 | 0.8789 | 0.9878 | 0.9953 | 0.7685 | 0.8728 | 0.9888 | 0.6937 | 0.7552 |
| 0.1909 | 45.0 | 180 | 0.1837 | 0.8151 | 0.8810 | 0.9880 | 0.9954 | 0.7726 | 0.8750 | 0.9890 | 0.6997 | 0.7568 |
| 0.1908 | 50.0 | 200 | 0.1817 | 0.8160 | 0.8823 | 0.9881 | 0.9954 | 0.7759 | 0.8755 | 0.9890 | 0.7014 | 0.7574 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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