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---
base_model: nvidia/mit-b0
license: other
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: 0.9038
- Mean Iou: 0.4486
- Mean Accuracy: 0.8693
- Overall Accuracy: 0.8708
- Accuracy Bkg: 0.8709
- Accuracy Gun: 0.8680
- Accuracy Knife: 0.8689
- Iou Bkg: 0.8700
- Iou Gun: 0.1670
- Iou Knife: 0.3088
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:--------------:|:-------:|:-------:|:---------:|
| 1.0919 | 1.4286 | 20 | 1.1033 | 0.1925 | 0.6509 | 0.3960 | 0.3783 | 0.8281 | 0.7462 | 0.3783 | 0.0314 | 0.1677 |
| 1.0576 | 2.8571 | 40 | 1.0473 | 0.3242 | 0.7738 | 0.6681 | 0.6608 | 0.8826 | 0.7781 | 0.6607 | 0.0600 | 0.2518 |
| 0.9844 | 4.2857 | 60 | 0.9913 | 0.3823 | 0.8265 | 0.7783 | 0.7750 | 0.8873 | 0.8171 | 0.7748 | 0.0928 | 0.2793 |
| 0.9604 | 5.7143 | 80 | 0.9385 | 0.4206 | 0.8552 | 0.8396 | 0.8386 | 0.8813 | 0.8457 | 0.8381 | 0.1334 | 0.2902 |
| 0.9418 | 7.1429 | 100 | 0.9073 | 0.4389 | 0.8651 | 0.8592 | 0.8588 | 0.8795 | 0.8570 | 0.8581 | 0.1520 | 0.3065 |
| 0.9029 | 8.5714 | 120 | 0.8989 | 0.4474 | 0.8672 | 0.8683 | 0.8684 | 0.8712 | 0.8620 | 0.8677 | 0.1621 | 0.3123 |
| 0.9277 | 10.0 | 140 | 0.9038 | 0.4486 | 0.8693 | 0.8708 | 0.8709 | 0.8680 | 0.8689 | 0.8700 | 0.1670 | 0.3088 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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