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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 bfwggggg/image-with-puzzle dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2327 |
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- Mean Iou: 0.4736 |
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- Mean Accuracy: 0.9472 |
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- Overall Accuracy: 0.9472 |
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- Accuracy Unlabeled: nan |
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- Accuracy Missing-puzzle: 0.9472 |
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- Iou Unlabeled: 0.0 |
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- Iou Missing-puzzle: 0.9472 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Missing-puzzle | Iou Unlabeled | Iou Missing-puzzle | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------------------:|:-------------:|:------------------:| |
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| 0.5575 | 5.0 | 20 | 0.6601 | 0.4998 | 0.9996 | 0.9996 | nan | 0.9996 | 0.0 | 0.9996 | |
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| 0.365 | 10.0 | 40 | 0.5628 | 0.4980 | 0.9960 | 0.9960 | nan | 0.9960 | 0.0 | 0.9960 | |
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| 0.2788 | 15.0 | 60 | 0.3816 | 0.4812 | 0.9624 | 0.9624 | nan | 0.9624 | 0.0 | 0.9624 | |
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| 0.2527 | 20.0 | 80 | 0.3869 | 0.4806 | 0.9611 | 0.9611 | nan | 0.9611 | 0.0 | 0.9611 | |
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| 0.2145 | 25.0 | 100 | 0.2733 | 0.4663 | 0.9326 | 0.9326 | nan | 0.9326 | 0.0 | 0.9326 | |
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| 0.206 | 30.0 | 120 | 0.2672 | 0.4739 | 0.9478 | 0.9478 | nan | 0.9478 | 0.0 | 0.9478 | |
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| 0.1866 | 35.0 | 140 | 0.2351 | 0.4667 | 0.9334 | 0.9334 | nan | 0.9334 | 0.0 | 0.9334 | |
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| 0.1696 | 40.0 | 160 | 0.2099 | 0.4749 | 0.9497 | 0.9497 | nan | 0.9497 | 0.0 | 0.9497 | |
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| 0.1639 | 45.0 | 180 | 0.2058 | 0.4723 | 0.9445 | 0.9445 | nan | 0.9445 | 0.0 | 0.9445 | |
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| 0.1719 | 50.0 | 200 | 0.2327 | 0.4736 | 0.9472 | 0.9472 | nan | 0.9472 | 0.0 | 0.9472 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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