| | --- |
| | license: other |
| | base_model: nvidia/mit-b0 |
| | tags: |
| | - vision |
| | - image-segmentation |
| | - generated_from_trainer |
| | model-index: |
| | - name: segformerSAAD1 |
| | 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. --> |
| |
|
| | # segformerSAAD1 |
| |
|
| | This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixGUN dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6923 |
| | - Mean Iou: 0.6077 |
| | - Mean Accuracy: 0.8342 |
| | - Overall Accuracy: 0.9596 |
| | - Accuracy Bkg: 0.9679 |
| | - Accuracy Knife: 0.7929 |
| | - Accuracy Gun: 0.7418 |
| | - Iou Bkg: 0.9593 |
| | - Iou Knife: 0.4467 |
| | - Iou Gun: 0.4171 |
| |
|
| | ## 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: 23 |
| | - eval_batch_size: 23 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:| |
| | | 0.819 | 20.0 | 20 | 0.9549 | 0.5466 | 0.8951 | 0.9364 | 0.9392 | 0.9027 | 0.8434 | 0.9365 | 0.3263 | 0.3771 | |
| | | 0.6984 | 40.0 | 40 | 0.6923 | 0.6077 | 0.8342 | 0.9596 | 0.9679 | 0.7929 | 0.7418 | 0.9593 | 0.4467 | 0.4171 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.42.4 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.21.0 |
| | - Tokenizers 0.19.1 |
| |
|
