| | --- |
| | license: other |
| | tags: |
| | - image-segmentation |
| | - vision |
| | - generated_from_trainer |
| | model-index: |
| | - name: mobilevit-small-10k-steps |
| | 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. --> |
| |
|
| | # mobilevit-small-10k-steps |
| |
|
| | This model is a fine-tuned version of [apple/deeplabv3-mobilevit-small](https://huggingface.co/apple/deeplabv3-mobilevit-small) on the Efferbach/lane_master2 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0821 |
| | - Mean Iou: 0.0 |
| | - Mean Accuracy: 0.0 |
| | - Overall Accuracy: 0.0 |
| | - Accuracy Background: nan |
| | - Accuracy Left: 0.0 |
| | - Accuracy Right: 0.0 |
| | - Iou Background: 0.0 |
| | - Iou Left: 0.0 |
| | - Iou Right: 0.0 |
| | |
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 1337 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: polynomial |
| | - training_steps: 10000 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:| |
| | | 0.5041 | 1.0 | 385 | 0.3382 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.1553 | 2.0 | 770 | 0.1387 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.1019 | 3.0 | 1155 | 0.1037 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0882 | 4.0 | 1540 | 0.0883 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0828 | 5.0 | 1925 | 0.0823 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0807 | 6.0 | 2310 | 0.0820 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0795 | 7.0 | 2695 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0786 | 8.0 | 3080 | 0.0784 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0777 | 9.0 | 3465 | 0.0786 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0771 | 10.0 | 3850 | 0.0774 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0773 | 11.0 | 4235 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0765 | 12.0 | 4620 | 0.0782 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0757 | 13.0 | 5005 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0756 | 14.0 | 5390 | 0.0774 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0754 | 15.0 | 5775 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0746 | 16.0 | 6160 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.074 | 17.0 | 6545 | 0.0779 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0736 | 18.0 | 6930 | 0.0792 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0737 | 19.0 | 7315 | 0.0801 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.073 | 20.0 | 7700 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0729 | 21.0 | 8085 | 0.0805 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0734 | 22.0 | 8470 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0726 | 23.0 | 8855 | 0.0811 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0726 | 24.0 | 9240 | 0.0816 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0721 | 25.0 | 9625 | 0.0822 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | | 0.0727 | 25.97 | 10000 | 0.0821 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0.dev0 |
| | - Pytorch 2.0.0+cu118 |
| | - Datasets 2.11.0 |
| | - Tokenizers 0.13.3 |
| | |
