|
--- |
|
license: other |
|
tags: |
|
- vision |
|
- image-segmentation |
|
- generated_from_trainer |
|
model-index: |
|
- name: parking-utcustom-train-SF-RGBD-b5_1 |
|
results: [] |
|
--- |
|
|
|
<!-- 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](https://huggingface.co/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 |
|
|
