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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-SixrayKnife8-21-2024_saad5
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-SixrayKnife8-21-2024_saad5
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: 0.2923
- Mean Iou: 0.8066
- Mean Accuracy: 0.9078
- Overall Accuracy: 0.9857
- Accuracy Bkg: 0.9917
- Accuracy Knife: 0.8251
- Accuracy Gun: 0.9065
- Iou Bkg: 0.9869
- Iou Knife: 0.7133
- Iou Gun: 0.7196
## 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 Knife | Accuracy Gun | Iou Bkg | Iou Knife | Iou Gun |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.8263 | 5.0 | 20 | 0.9552 | 0.5735 | 0.8760 | 0.9414 | 0.9464 | 0.8112 | 0.8703 | 0.9424 | 0.4242 | 0.3540 |
| 0.6154 | 10.0 | 40 | 0.6184 | 0.6297 | 0.7711 | 0.9652 | 0.9800 | 0.6061 | 0.7272 | 0.9657 | 0.4854 | 0.4379 |
| 0.5165 | 15.0 | 60 | 0.5098 | 0.6805 | 0.8233 | 0.9714 | 0.9826 | 0.7375 | 0.7498 | 0.9720 | 0.5691 | 0.5003 |
| 0.4503 | 20.0 | 80 | 0.4561 | 0.7103 | 0.8818 | 0.9735 | 0.9805 | 0.7960 | 0.8690 | 0.9742 | 0.5898 | 0.5670 |
| 0.4154 | 25.0 | 100 | 0.3958 | 0.7526 | 0.8997 | 0.9791 | 0.9852 | 0.8206 | 0.8934 | 0.9800 | 0.6237 | 0.6540 |
| 0.3659 | 30.0 | 120 | 0.3529 | 0.7810 | 0.8969 | 0.9832 | 0.9899 | 0.7932 | 0.9076 | 0.9844 | 0.6814 | 0.6773 |
| 0.3616 | 35.0 | 140 | 0.3253 | 0.7949 | 0.8937 | 0.9848 | 0.9918 | 0.8004 | 0.8889 | 0.9858 | 0.6954 | 0.7035 |
| 0.3666 | 40.0 | 160 | 0.3110 | 0.8018 | 0.9085 | 0.9852 | 0.9911 | 0.8255 | 0.9087 | 0.9863 | 0.7079 | 0.7112 |
| 0.3082 | 45.0 | 180 | 0.2983 | 0.8011 | 0.9037 | 0.9852 | 0.9914 | 0.8195 | 0.9002 | 0.9863 | 0.6982 | 0.7189 |
| 0.3097 | 50.0 | 200 | 0.2923 | 0.8066 | 0.9078 | 0.9857 | 0.9917 | 0.8251 | 0.9065 | 0.9869 | 0.7133 | 0.7196 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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