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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-SixrayKnife8-20-2024
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-SixrayKnife8-20-2024
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: 0.2632
- Mean Iou: 0.7518
- Mean Accuracy: 0.8442
- Overall Accuracy: 0.9846
- Accuracy Bkg: 0.9934
- Accuracy Knife: 0.6638
- Accuracy Gun: 0.8755
- Iou Bkg: 0.9864
- Iou Knife: 0.5722
- Iou Gun: 0.6969
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.7462 | 5.0 | 20 | 0.8680 | 0.5725 | 0.7955 | 0.9552 | 0.9653 | 0.6150 | 0.8064 | 0.9557 | 0.3394 | 0.4223 |
| 0.5675 | 10.0 | 40 | 0.5259 | 0.5797 | 0.6730 | 0.9685 | 0.9873 | 0.3829 | 0.6486 | 0.9690 | 0.3247 | 0.4455 |
| 0.5079 | 15.0 | 60 | 0.4394 | 0.6394 | 0.7578 | 0.9723 | 0.9859 | 0.5491 | 0.7385 | 0.9731 | 0.4658 | 0.4794 |
| 0.3976 | 20.0 | 80 | 0.3820 | 0.6781 | 0.7446 | 0.9792 | 0.9942 | 0.5443 | 0.6952 | 0.9802 | 0.4938 | 0.5601 |
| 0.3527 | 25.0 | 100 | 0.3454 | 0.7173 | 0.8050 | 0.9816 | 0.9928 | 0.6128 | 0.8094 | 0.9829 | 0.5373 | 0.6318 |
| 0.3571 | 30.0 | 120 | 0.3192 | 0.7336 | 0.8386 | 0.9826 | 0.9917 | 0.6508 | 0.8734 | 0.9843 | 0.5518 | 0.6646 |
| 0.3201 | 35.0 | 140 | 0.2858 | 0.7399 | 0.8390 | 0.9834 | 0.9924 | 0.6540 | 0.8706 | 0.9851 | 0.5637 | 0.6709 |
| 0.3205 | 40.0 | 160 | 0.2774 | 0.7482 | 0.8301 | 0.9846 | 0.9944 | 0.6447 | 0.8512 | 0.9864 | 0.5673 | 0.6911 |
| 0.2899 | 45.0 | 180 | 0.2677 | 0.7497 | 0.8399 | 0.9845 | 0.9937 | 0.6581 | 0.8679 | 0.9864 | 0.5679 | 0.6948 |
| 0.2672 | 50.0 | 200 | 0.2632 | 0.7518 | 0.8442 | 0.9846 | 0.9934 | 0.6638 | 0.8755 | 0.9864 | 0.5722 | 0.6969 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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