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mobilevit-small-10k-steps
Efferbach/mobilevit-small-10k-steps
mobilevit-small-10k-steps is a image segmentation model from Efferbach. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- other
- Model type
- mobilevit
- Access
- Public
- Created
- Apr 8, 2023
- Updated
- Apr 8, 2023
- SHA
- 7d10211f
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Image Segmentation
- Library
- transformers
- Type
- mobilevit
- License
- other
- Created
- Apr 8, 2023
- Updated
- Apr 8, 2023