|
--- |
|
license: other |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: segformer-b0-finetuned-segments-toolwear |
|
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. --> |
|
|
|
# segformer-b0-finetuned-segments-toolwear |
|
|
|
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1338 |
|
- Mean Iou: 0.4591 |
|
- Mean Accuracy: 0.7164 |
|
- Overall Accuracy: 0.9595 |
|
- Accuracy Unlabeled: nan |
|
- Accuracy Wear: 0.4489 |
|
- Accuracy Tool: 0.9838 |
|
- Iou Unlabeled: 0.0 |
|
- Iou Wear: 0.4154 |
|
- Iou Tool: 0.9618 |
|
|
|
## 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: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wear | Accuracy Tool | Iou Unlabeled | Iou Wear | Iou Tool | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:| |
|
| 0.5488 | 1.82 | 20 | 0.7199 | 0.3293 | 0.5153 | 0.9405 | nan | 0.0476 | 0.9830 | 0.0 | 0.0475 | 0.9404 | |
|
| 0.5195 | 3.64 | 40 | 0.3507 | 0.3622 | 0.5634 | 0.9239 | nan | 0.1667 | 0.9600 | 0.0 | 0.1629 | 0.9236 | |
|
| 0.2738 | 5.45 | 60 | 0.2569 | 0.4662 | 0.7496 | 0.9435 | nan | 0.5363 | 0.9629 | 0.0 | 0.4547 | 0.9438 | |
|
| 0.2461 | 7.27 | 80 | 0.2220 | 0.4491 | 0.7057 | 0.9482 | nan | 0.4389 | 0.9725 | 0.0 | 0.3982 | 0.9492 | |
|
| 0.1999 | 9.09 | 100 | 0.1962 | 0.4492 | 0.7084 | 0.9597 | nan | 0.4319 | 0.9848 | 0.0 | 0.3860 | 0.9616 | |
|
| 0.2004 | 10.91 | 120 | 0.1890 | 0.4031 | 0.6239 | 0.9537 | nan | 0.2610 | 0.9867 | 0.0 | 0.2539 | 0.9553 | |
|
| 0.4753 | 12.73 | 140 | 0.1704 | 0.4360 | 0.6760 | 0.9494 | nan | 0.3753 | 0.9768 | 0.0 | 0.3562 | 0.9518 | |
|
| 0.1606 | 14.55 | 160 | 0.1579 | 0.4483 | 0.7028 | 0.9580 | nan | 0.4222 | 0.9835 | 0.0 | 0.3822 | 0.9625 | |
|
| 0.1388 | 16.36 | 180 | 0.1519 | 0.4829 | 0.7940 | 0.9565 | nan | 0.6152 | 0.9728 | 0.0 | 0.4900 | 0.9586 | |
|
| 0.138 | 18.18 | 200 | 0.1374 | 0.5120 | 0.8119 | 0.9643 | nan | 0.6443 | 0.9795 | 0.0 | 0.5693 | 0.9668 | |
|
| 0.1078 | 20.0 | 220 | 0.1400 | 0.4541 | 0.7066 | 0.9606 | nan | 0.4271 | 0.9860 | 0.0 | 0.3985 | 0.9638 | |
|
| 0.1426 | 21.82 | 240 | 0.1323 | 0.4530 | 0.7053 | 0.9581 | nan | 0.4272 | 0.9834 | 0.0 | 0.3978 | 0.9611 | |
|
| 0.3498 | 23.64 | 260 | 0.1338 | 0.4591 | 0.7164 | 0.9595 | nan | 0.4489 | 0.9838 | 0.0 | 0.4154 | 0.9618 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
