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segformer-finetuned-rwymarkings-2-steps
Spatiallysaying/segformer-finetuned-rwymarkings-2-steps
segformer-finetuned-rwymarkings-2-steps is a image segmentation model from Spatiallysaying. 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
- segformer
- Access
- Public
- Created
- Jul 12, 2024
- Updated
- Jul 12, 2024
- SHA
- 0f3093ca
Base models
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. -->
segformer-finetuned-rwymarkings-2-steps
This model is a fine-tuned version of nvidia/mit-b0 on the Spatiallysaying/rwymarkings dataset. It achieves the following results on the evaluation set:
- Loss: 2.2162
- Mean Iou: 0.0387
- Mean Accuracy: 0.1129
- Overall Accuracy: 0.1050
- Accuracy Backgound : nan
- Accuracy Tdz: 0.0493
- Accuracy Aim: 0.2144
- Accuracy Desig: 0.0922
- Accuracy Rwythr: 0.1765
- Accuracy Thrbar: 0.0140
- Accuracy Disp: 0.2710
- Accuracy Chevron: 0.0023
- Accuracy Arrow: 0.0834
- Iou Backgound : 0.0
- Iou Tdz: 0.0399
- Iou Aim: 0.1158
- Iou Desig: 0.0443
- Iou Rwythr: 0.0980
- Iou Thrbar: 0.0131
- Iou Disp: 0.0266
- Iou Chevron: 0.0020
- Iou Arrow: 0.0085
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Backgound | Accuracy Tdz | Accuracy Aim | Accuracy Desig | Accuracy Rwythr | Accuracy Thrbar | Accuracy Disp | Accuracy Chevron | Accuracy Arrow | Iou Backgound | Iou Tdz | Iou Aim | Iou Desig | Iou Rwythr | Iou Thrbar | Iou Disp | Iou Chevron | Iou Arrow |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.2475 | 0.0455 | 2 | 2.2162 | 0.0387 | 0.1129 | 0.1050 | nan | 0.0493 | 0.2144 | 0.0922 | 0.1765 | 0.0140 | 0.2710 | 0.0023 | 0.0834 | 0.0 | 0.0399 | 0.1158 | 0.0443 | 0.0980 | 0.0131 | 0.0266 | 0.0020 | 0.0085 |
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Jul 12, 2024
- Updated
- Jul 12, 2024