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--- |
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license: other |
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tags: |
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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: segformer-finetuned-lane-1k-steps |
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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-finetuned-lane-1k-steps |
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This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0548 |
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- Mean Iou: 0.0708 |
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- Mean Accuracy: 0.1236 |
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- Overall Accuracy: 0.1217 |
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- Accuracy Background: nan |
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- Accuracy Left: 0.1893 |
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- Accuracy Right: 0.0578 |
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- Iou Background: 0.0 |
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- Iou Left: 0.1581 |
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- Iou Right: 0.0544 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- training_steps: 1000 |
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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 Left | Accuracy Right | Iou Background | Iou Left | Iou Right | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:| |
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| 0.1 | 1.0 | 308 | 0.0862 | 0.0008 | 0.0013 | 0.0012 | nan | 0.0025 | 0.0 | 0.0 | 0.0025 | 0.0 | |
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| 0.0596 | 2.0 | 616 | 0.0597 | 0.0712 | 0.1126 | 0.1132 | nan | 0.0940 | 0.1313 | 0.0 | 0.0907 | 0.1228 | |
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| 0.0506 | 3.0 | 924 | 0.0551 | 0.0682 | 0.1171 | 0.1152 | nan | 0.1805 | 0.0536 | 0.0 | 0.1539 | 0.0508 | |
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| 0.0494 | 3.25 | 1000 | 0.0548 | 0.0708 | 0.1236 | 0.1217 | nan | 0.1893 | 0.0578 | 0.0 | 0.1581 | 0.0544 | |
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### Framework versions |
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- Transformers 4.28.0.dev0 |
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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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