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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-v-mesh-0 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-v-mesh-0 |
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This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/vehicle_segmentation dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0360 |
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- Mean Iou: 0.4403 |
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- Mean Accuracy: 0.8806 |
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- Overall Accuracy: 0.8806 |
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- Accuracy Background: nan |
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- Accuracy Windows: 0.8806 |
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- Iou Background: 0.0 |
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- Iou Windows: 0.8806 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Windows | Iou Background | Iou Windows | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:| |
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| 0.2932 | 0.16 | 20 | 0.3269 | 0.2578 | 0.5156 | 0.5156 | nan | 0.5156 | 0.0 | 0.5156 | |
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| 0.1417 | 0.31 | 40 | 0.1235 | 0.3790 | 0.7580 | 0.7580 | nan | 0.7580 | 0.0 | 0.7580 | |
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| 0.0952 | 0.47 | 60 | 0.1245 | 0.4606 | 0.9211 | 0.9211 | nan | 0.9211 | 0.0 | 0.9211 | |
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| 0.0778 | 0.62 | 80 | 0.0628 | 0.4042 | 0.8084 | 0.8084 | nan | 0.8084 | 0.0 | 0.8084 | |
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| 0.0448 | 0.78 | 100 | 0.0512 | 0.4161 | 0.8322 | 0.8322 | nan | 0.8322 | 0.0 | 0.8322 | |
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| 0.0323 | 0.94 | 120 | 0.0435 | 0.4167 | 0.8334 | 0.8334 | nan | 0.8334 | 0.0 | 0.8334 | |
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| 0.0337 | 1.09 | 140 | 0.0405 | 0.4131 | 0.8262 | 0.8262 | nan | 0.8262 | 0.0 | 0.8262 | |
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| 0.0586 | 1.25 | 160 | 0.0409 | 0.4509 | 0.9017 | 0.9017 | nan | 0.9017 | 0.0 | 0.9017 | |
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| 0.0591 | 1.41 | 180 | 0.0404 | 0.4310 | 0.8620 | 0.8620 | nan | 0.8620 | 0.0 | 0.8620 | |
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| 0.0953 | 1.56 | 200 | 0.0386 | 0.4366 | 0.8732 | 0.8732 | nan | 0.8732 | 0.0 | 0.8732 | |
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| 0.0607 | 1.72 | 220 | 0.0374 | 0.4414 | 0.8828 | 0.8828 | nan | 0.8828 | 0.0 | 0.8828 | |
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| 0.0387 | 1.88 | 240 | 0.0360 | 0.4403 | 0.8806 | 0.8806 | nan | 0.8806 | 0.0 | 0.8806 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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