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parking-utcustom-train-SF-RGBD-b5_1
sam1120/parking-utcustom-train-SF-RGBD-b5_1
parking-utcustom-train-SF-RGBD-b5_1 is a image segmentation model from sam1120. 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
- Mar 4, 2024
- Updated
- Mar 4, 2024
- SHA
- bb1f698a
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. -->
parking-utcustom-train-SF-RGBD-b5_1
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/parking-utcustom-train dataset. It achieves the following results on the evaluation set:
- Loss: 0.0476
- Mean Iou: 0.4942
- Mean Accuracy: 0.9883
- Overall Accuracy: 0.9883
- Accuracy Unlabeled: nan
- Accuracy Parking: nan
- Accuracy Unparking: 0.9883
- Iou Unlabeled: nan
- Iou Parking: 0.0
- Iou Unparking: 0.9883
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 150
Training results
| Training Loss | Epoch | Step | Accuracy Parking | Accuracy Unlabeled | Accuracy Unparking | Iou Parking | Iou Unlabeled | Iou Unparking | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4573 | 20.0 | 20 | nan | nan | 0.9829 | 0.0 | 0.0 | 0.9829 | 0.3024 | 0.9829 | 0.3276 | 0.9829 |
| 0.2183 | 40.0 | 40 | nan | nan | 0.9953 | 0.0 | 0.0 | 0.9953 | 0.2365 | 0.9953 | 0.3318 | 0.9953 |
| 0.1266 | 60.0 | 60 | nan | nan | 1.0 | nan | nan | 1.0 | 0.0999 | 1.0 | 1.0 | 1.0 |
| 0.0929 | 80.0 | 80 | nan | nan | 0.9972 | 0.0 | nan | 0.9972 | 0.0590 | 0.9972 | 0.4986 | 0.9972 |
| 0.0649 | 100.0 | 100 | 0.0346 | 0.4992 | 0.9984 | 0.9984 | nan | nan | 0.9984 | nan | 0.0 | 0.9984 |
| 0.0537 | 120.0 | 120 | 0.0377 | 0.4980 | 0.9960 | 0.9960 | nan | nan | 0.9960 | nan | 0.0 | 0.9960 |
| 0.0536 | 140.0 | 140 | 0.0476 | 0.4942 | 0.9883 | 0.9883 | nan | nan | 0.9883 | nan | 0.0 | 0.9883 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Mar 4, 2024
- Updated
- Mar 4, 2024