|
--- |
|
license: other |
|
tags: |
|
- vision |
|
- image-segmentation |
|
- 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 the HorcruxNo13/new_wear dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0737 |
|
- Mean Iou: 0.3080 |
|
- Mean Accuracy: 0.6160 |
|
- Overall Accuracy: 0.6160 |
|
- Accuracy Unlabeled: nan |
|
- Accuracy Wear: 0.6160 |
|
- Iou Unlabeled: 0.0 |
|
- Iou Wear: 0.6160 |
|
|
|
## 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 Wear | Iou Unlabeled | Iou Wear | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| |
|
| 0.4694 | 3.33 | 20 | 0.4857 | 0.3178 | 0.6356 | 0.6356 | nan | 0.6356 | 0.0 | 0.6356 | |
|
| 0.3038 | 6.67 | 40 | 0.2805 | 0.3408 | 0.6816 | 0.6816 | nan | 0.6816 | 0.0 | 0.6816 | |
|
| 0.2303 | 10.0 | 60 | 0.2080 | 0.3408 | 0.6816 | 0.6816 | nan | 0.6816 | 0.0 | 0.6816 | |
|
| 0.1935 | 13.33 | 80 | 0.1870 | 0.3420 | 0.6841 | 0.6841 | nan | 0.6841 | 0.0 | 0.6841 | |
|
| 0.1697 | 16.67 | 100 | 0.1507 | 0.3405 | 0.6810 | 0.6810 | nan | 0.6810 | 0.0 | 0.6810 | |
|
| 0.1406 | 20.0 | 120 | 0.1377 | 0.3437 | 0.6874 | 0.6874 | nan | 0.6874 | 0.0 | 0.6874 | |
|
| 0.1363 | 23.33 | 140 | 0.1156 | 0.3301 | 0.6601 | 0.6601 | nan | 0.6601 | 0.0 | 0.6601 | |
|
| 0.117 | 26.67 | 160 | 0.1019 | 0.3376 | 0.6753 | 0.6753 | nan | 0.6753 | 0.0 | 0.6753 | |
|
| 0.0972 | 30.0 | 180 | 0.0935 | 0.3264 | 0.6529 | 0.6529 | nan | 0.6529 | 0.0 | 0.6529 | |
|
| 0.1076 | 33.33 | 200 | 0.0901 | 0.3292 | 0.6584 | 0.6584 | nan | 0.6584 | 0.0 | 0.6584 | |
|
| 0.0868 | 36.67 | 220 | 0.0806 | 0.3218 | 0.6436 | 0.6436 | nan | 0.6436 | 0.0 | 0.6436 | |
|
| 0.0866 | 40.0 | 240 | 0.0766 | 0.3183 | 0.6367 | 0.6367 | nan | 0.6367 | 0.0 | 0.6367 | |
|
| 0.0757 | 43.33 | 260 | 0.0750 | 0.3082 | 0.6165 | 0.6165 | nan | 0.6165 | 0.0 | 0.6165 | |
|
| 0.077 | 46.67 | 280 | 0.0750 | 0.3104 | 0.6207 | 0.6207 | nan | 0.6207 | 0.0 | 0.6207 | |
|
| 0.0765 | 50.0 | 300 | 0.0737 | 0.3080 | 0.6160 | 0.6160 | nan | 0.6160 | 0.0 | 0.6160 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.13.3 |
|
|
