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parking-utcustom-train-SF-RGBD-b5_4
sam1120/parking-utcustom-train-SF-RGBD-b5_4
parking-utcustom-train-SF-RGBD-b5_4 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
- 6bfc72d1
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_4
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: 0.0283
- Mean Iou: 1.0
- Mean Accuracy: 1.0
- Overall Accuracy: 1.0
- Accuracy Unlabeled: nan
- Accuracy Parking: nan
- Accuracy Unparking: 1.0
- Iou Unlabeled: nan
- Iou Parking: nan
- Iou Unparking: 1.0
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: 5.5e-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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4818 | 20.0 | 20 | nan | nan | 0.9878 | 0.0 | nan | 0.9878 | 0.3775 | 0.9878 | 0.4939 | 0.9878 |
| 0.1939 | 40.0 | 40 | nan | nan | 1.0 | nan | nan | 1.0 | 0.1955 | 1.0 | 1.0 | 1.0 |
| 0.1272 | 60.0 | 60 | nan | nan | 1.0 | nan | nan | 1.0 | 0.0813 | 1.0 | 1.0 | 1.0 |
| 0.0999 | 80.0 | 80 | nan | nan | 1.0 | nan | nan | 1.0 | 0.0399 | 1.0 | 1.0 | 1.0 |
| 0.0669 | 100.0 | 100 | 0.0324 | 1.0 | 1.0 | 1.0 | nan | nan | 1.0 | nan | nan | 1.0 |
| 0.0562 | 120.0 | 120 | 0.0310 | 1.0 | 1.0 | 1.0 | nan | nan | 1.0 | nan | nan | 1.0 |
| 0.0673 | 140.0 | 140 | 0.0283 | 1.0 | 1.0 | 1.0 | nan | nan | 1.0 | nan | nan | 1.0 |
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