|
--- |
|
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: 0.0354 |
|
- Mean Iou: 0.3022 |
|
- Mean Accuracy: 0.6045 |
|
- Overall Accuracy: 0.6045 |
|
- Accuracy Unlabeled: nan |
|
- Accuracy Tool: nan |
|
- Accuracy Wear: 0.6045 |
|
- Iou Unlabeled: 0.0 |
|
- Iou Tool: nan |
|
- Iou Wear: 0.6045 |
|
|
|
## 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: 50 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:| |
|
| 0.8671 | 1.18 | 20 | 0.9263 | 0.4061 | 0.8122 | 0.8122 | nan | nan | 0.8122 | 0.0 | nan | 0.8122 | |
|
| 0.5691 | 2.35 | 40 | 0.5998 | 0.2895 | 0.5790 | 0.5790 | nan | nan | 0.5790 | 0.0 | nan | 0.5790 | |
|
| 0.4378 | 3.53 | 60 | 0.3948 | 0.3106 | 0.6213 | 0.6213 | nan | nan | 0.6213 | 0.0 | nan | 0.6213 | |
|
| 0.3842 | 4.71 | 80 | 0.3190 | 0.2679 | 0.5357 | 0.5357 | nan | nan | 0.5357 | 0.0 | nan | 0.5357 | |
|
| 0.3234 | 5.88 | 100 | 0.2883 | 0.3574 | 0.7148 | 0.7148 | nan | nan | 0.7148 | 0.0 | nan | 0.7148 | |
|
| 0.2731 | 7.06 | 120 | 0.2392 | 0.3456 | 0.6911 | 0.6911 | nan | nan | 0.6911 | 0.0 | nan | 0.6911 | |
|
| 0.2137 | 8.24 | 140 | 0.1850 | 0.1844 | 0.3688 | 0.3688 | nan | nan | 0.3688 | 0.0 | nan | 0.3688 | |
|
| 0.1798 | 9.41 | 160 | 0.1692 | 0.2757 | 0.5515 | 0.5515 | nan | nan | 0.5515 | 0.0 | nan | 0.5515 | |
|
| 0.1607 | 10.59 | 180 | 0.1338 | 0.2978 | 0.5956 | 0.5956 | nan | nan | 0.5956 | 0.0 | nan | 0.5956 | |
|
| 0.1399 | 11.76 | 200 | 0.1218 | 0.2906 | 0.5811 | 0.5811 | nan | nan | 0.5811 | 0.0 | nan | 0.5811 | |
|
| 0.1173 | 12.94 | 220 | 0.1030 | 0.2612 | 0.5224 | 0.5224 | nan | nan | 0.5224 | 0.0 | nan | 0.5224 | |
|
| 0.0922 | 14.12 | 240 | 0.0976 | 0.2817 | 0.5633 | 0.5633 | nan | nan | 0.5633 | 0.0 | nan | 0.5633 | |
|
| 0.081 | 15.29 | 260 | 0.0795 | 0.3154 | 0.6308 | 0.6308 | nan | nan | 0.6308 | 0.0 | nan | 0.6308 | |
|
| 0.0852 | 16.47 | 280 | 0.0716 | 0.2188 | 0.4377 | 0.4377 | nan | nan | 0.4377 | 0.0 | nan | 0.4377 | |
|
| 0.0709 | 17.65 | 300 | 0.0680 | 0.2691 | 0.5382 | 0.5382 | nan | nan | 0.5382 | 0.0 | nan | 0.5382 | |
|
| 0.073 | 18.82 | 320 | 0.0611 | 0.2830 | 0.5660 | 0.5660 | nan | nan | 0.5660 | 0.0 | nan | 0.5660 | |
|
| 0.0602 | 20.0 | 340 | 0.0592 | 0.2829 | 0.5657 | 0.5657 | nan | nan | 0.5657 | 0.0 | nan | 0.5657 | |
|
| 0.0547 | 21.18 | 360 | 0.0577 | 0.2842 | 0.5684 | 0.5684 | nan | nan | 0.5684 | 0.0 | nan | 0.5684 | |
|
| 0.0554 | 22.35 | 380 | 0.0537 | 0.2613 | 0.5226 | 0.5226 | nan | nan | 0.5226 | 0.0 | nan | 0.5226 | |
|
| 0.0515 | 23.53 | 400 | 0.0523 | 0.3076 | 0.6152 | 0.6152 | nan | nan | 0.6152 | 0.0 | nan | 0.6152 | |
|
| 0.0444 | 24.71 | 420 | 0.0487 | 0.3063 | 0.6126 | 0.6126 | nan | nan | 0.6126 | 0.0 | nan | 0.6126 | |
|
| 0.088 | 25.88 | 440 | 0.0467 | 0.3041 | 0.6082 | 0.6082 | nan | nan | 0.6082 | 0.0 | nan | 0.6082 | |
|
| 0.0472 | 27.06 | 460 | 0.0437 | 0.2623 | 0.5245 | 0.5245 | nan | nan | 0.5245 | 0.0 | nan | 0.5245 | |
|
| 0.0396 | 28.24 | 480 | 0.0474 | 0.3352 | 0.6704 | 0.6704 | nan | nan | 0.6704 | 0.0 | nan | 0.6704 | |
|
| 0.0351 | 29.41 | 500 | 0.0436 | 0.3060 | 0.6120 | 0.6120 | nan | nan | 0.6120 | 0.0 | nan | 0.6120 | |
|
| 0.0392 | 30.59 | 520 | 0.0428 | 0.2975 | 0.5951 | 0.5951 | nan | nan | 0.5951 | 0.0 | nan | 0.5951 | |
|
| 0.0317 | 31.76 | 540 | 0.0431 | 0.3253 | 0.6507 | 0.6507 | nan | nan | 0.6507 | 0.0 | nan | 0.6507 | |
|
| 0.0391 | 32.94 | 560 | 0.0404 | 0.2863 | 0.5726 | 0.5726 | nan | nan | 0.5726 | 0.0 | nan | 0.5726 | |
|
| 0.0309 | 34.12 | 580 | 0.0408 | 0.3215 | 0.6429 | 0.6429 | nan | nan | 0.6429 | 0.0 | nan | 0.6429 | |
|
| 0.0493 | 35.29 | 600 | 0.0381 | 0.2581 | 0.5162 | 0.5162 | nan | nan | 0.5162 | 0.0 | nan | 0.5162 | |
|
| 0.0321 | 36.47 | 620 | 0.0376 | 0.3147 | 0.6293 | 0.6293 | nan | nan | 0.6293 | 0.0 | nan | 0.6293 | |
|
| 0.0333 | 37.65 | 640 | 0.0372 | 0.3118 | 0.6236 | 0.6236 | nan | nan | 0.6236 | 0.0 | nan | 0.6236 | |
|
| 0.0295 | 38.82 | 660 | 0.0362 | 0.3036 | 0.6072 | 0.6072 | nan | nan | 0.6072 | 0.0 | nan | 0.6072 | |
|
| 0.0302 | 40.0 | 680 | 0.0365 | 0.3157 | 0.6314 | 0.6314 | nan | nan | 0.6314 | 0.0 | nan | 0.6314 | |
|
| 0.0272 | 41.18 | 700 | 0.0367 | 0.3012 | 0.6024 | 0.6024 | nan | nan | 0.6024 | 0.0 | nan | 0.6024 | |
|
| 0.0278 | 42.35 | 720 | 0.0353 | 0.2935 | 0.5870 | 0.5870 | nan | nan | 0.5870 | 0.0 | nan | 0.5870 | |
|
| 0.0283 | 43.53 | 740 | 0.0353 | 0.2970 | 0.5940 | 0.5940 | nan | nan | 0.5940 | 0.0 | nan | 0.5940 | |
|
| 0.0256 | 44.71 | 760 | 0.0355 | 0.3090 | 0.6181 | 0.6181 | nan | nan | 0.6181 | 0.0 | nan | 0.6181 | |
|
| 0.0365 | 45.88 | 780 | 0.0358 | 0.3008 | 0.6015 | 0.6015 | nan | nan | 0.6015 | 0.0 | nan | 0.6015 | |
|
| 0.025 | 47.06 | 800 | 0.0353 | 0.2965 | 0.5930 | 0.5930 | nan | nan | 0.5930 | 0.0 | nan | 0.5930 | |
|
| 0.0299 | 48.24 | 820 | 0.0361 | 0.3109 | 0.6219 | 0.6219 | nan | nan | 0.6219 | 0.0 | nan | 0.6219 | |
|
| 0.0239 | 49.41 | 840 | 0.0354 | 0.3022 | 0.6045 | 0.6045 | nan | nan | 0.6045 | 0.0 | nan | 0.6045 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.0 |
|
- Tokenizers 0.13.3 |
|
|
