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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-b0-finetuned-segments-sidewalk-oct-22 |
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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-b0-finetuned-segments-sidewalk-oct-22 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bricklerex/lane-detect dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0646 |
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- Mean Iou: 0.4681 |
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- Mean Accuracy: 0.9362 |
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- Overall Accuracy: 0.9362 |
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- Accuracy Unlabelled: nan |
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- Accuracy Lane: 0.9362 |
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- Iou Unlabelled: 0.0 |
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- Iou Lane: 0.9362 |
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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 Unlabelled | Accuracy Lane | Iou Unlabelled | Iou Lane | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------:| |
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| 0.0823 | 0.35 | 20 | 0.0933 | 0.4675 | 0.9349 | 0.9349 | nan | 0.9349 | 0.0 | 0.9349 | |
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| 0.0676 | 0.7 | 40 | 0.0737 | 0.4547 | 0.9093 | 0.9093 | nan | 0.9093 | 0.0 | 0.9093 | |
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| 0.0639 | 1.05 | 60 | 0.0659 | 0.4583 | 0.9166 | 0.9166 | nan | 0.9166 | 0.0 | 0.9166 | |
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| 0.0584 | 1.4 | 80 | 0.0831 | 0.4715 | 0.9429 | 0.9429 | nan | 0.9429 | 0.0 | 0.9429 | |
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| 0.0541 | 1.75 | 100 | 0.0646 | 0.4681 | 0.9362 | 0.9362 | nan | 0.9362 | 0.0 | 0.9362 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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