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parking-utcustom-train-SF-RGB-b0_5
sam1120/parking-utcustom-train-SF-RGB-b0_5
parking-utcustom-train-SF-RGB-b0_5 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
- 1fbc7f69
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-RGB-b0_5
This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/parking-utcustom-train dataset. It achieves the following results on the evaluation set:
- Loss: 1.0414
- Mean Iou: 0.2695
- Mean Accuracy: 0.8085
- Overall Accuracy: 0.8085
- Accuracy Unlabeled: nan
- Accuracy Parking: nan
- Accuracy Unparking: 0.8085
- Iou Unlabeled: 0.0
- Iou Parking: 0.0
- Iou Unparking: 0.8085
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: 4.25e-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: 120
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Parking | Accuracy Unparking | Iou Unlabeled | Iou Parking | Iou Unparking |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1636 | 20.0 | 20 | 1.1342 | 0.0522 | 0.1567 | 0.1567 | nan | nan | 0.1567 | 0.0 | 0.0 | 0.1567 |
| 0.9787 | 40.0 | 40 | 1.1195 | 0.2039 | 0.6116 | 0.6116 | nan | nan | 0.6116 | 0.0 | 0.0 | 0.6116 |
| 0.887 | 60.0 | 60 | 1.0823 | 0.2420 | 0.7259 | 0.7259 | nan | nan | 0.7259 | 0.0 | 0.0 | 0.7259 |
| 0.7959 | 80.0 | 80 | 0.9519 | 0.2693 | 0.8080 | 0.8080 | nan | nan | 0.8080 | 0.0 | 0.0 | 0.8080 |
| 0.7344 | 100.0 | 100 | 0.8902 | 0.2827 | 0.8481 | 0.8481 | nan | nan | 0.8481 | 0.0 | 0.0 | 0.8481 |
| 0.7391 | 120.0 | 120 | 1.0414 | 0.2695 | 0.8085 | 0.8085 | nan | nan | 0.8085 | 0.0 | 0.0 | 0.8085 |
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