|
--- |
|
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: 0.0236 |
|
- Mean Iou: 0.4952 |
|
- Mean Accuracy: 0.9904 |
|
- Overall Accuracy: 0.9904 |
|
- Accuracy Unlabeled: nan |
|
- Accuracy Tool: 0.9904 |
|
- Iou Unlabeled: 0.0 |
|
- Iou Tool: 0.9904 |
|
|
|
## 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 | Iou Unlabeled | Iou Tool | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| |
|
| 0.1696 | 1.18 | 20 | 0.3490 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
|
| 0.1045 | 2.35 | 40 | 0.0977 | 0.4878 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 | |
|
| 0.0871 | 3.53 | 60 | 0.0650 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 | |
|
| 0.0542 | 4.71 | 80 | 0.0652 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 | |
|
| 0.0507 | 5.88 | 100 | 0.0573 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 | |
|
| 0.071 | 7.06 | 120 | 0.0510 | 0.4941 | 0.9883 | 0.9883 | nan | 0.9883 | 0.0 | 0.9883 | |
|
| 0.0456 | 8.24 | 140 | 0.0487 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 | |
|
| 0.0489 | 9.41 | 160 | 0.0430 | 0.4934 | 0.9867 | 0.9867 | nan | 0.9867 | 0.0 | 0.9867 | |
|
| 0.048 | 10.59 | 180 | 0.0409 | 0.4940 | 0.9881 | 0.9881 | nan | 0.9881 | 0.0 | 0.9881 | |
|
| 0.0476 | 11.76 | 200 | 0.0347 | 0.4966 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 | |
|
| 0.0479 | 12.94 | 220 | 0.0367 | 0.4972 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 | |
|
| 0.0242 | 14.12 | 240 | 0.0342 | 0.4962 | 0.9925 | 0.9925 | nan | 0.9925 | 0.0 | 0.9925 | |
|
| 0.0277 | 15.29 | 260 | 0.0305 | 0.4967 | 0.9934 | 0.9934 | nan | 0.9934 | 0.0 | 0.9934 | |
|
| 0.0192 | 16.47 | 280 | 0.0318 | 0.4956 | 0.9913 | 0.9913 | nan | 0.9913 | 0.0 | 0.9913 | |
|
| 0.038 | 17.65 | 300 | 0.0284 | 0.4965 | 0.9929 | 0.9929 | nan | 0.9929 | 0.0 | 0.9929 | |
|
| 0.0244 | 18.82 | 320 | 0.0280 | 0.4953 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | 0.9906 | |
|
| 0.0273 | 20.0 | 340 | 0.0269 | 0.4955 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 | |
|
| 0.0174 | 21.18 | 360 | 0.0280 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 | |
|
| 0.0277 | 22.35 | 380 | 0.0270 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 | |
|
| 0.0269 | 23.53 | 400 | 0.0271 | 0.4950 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 | |
|
| 0.0372 | 24.71 | 420 | 0.0252 | 0.4939 | 0.9879 | 0.9879 | nan | 0.9879 | 0.0 | 0.9879 | |
|
| 0.023 | 25.88 | 440 | 0.0263 | 0.4935 | 0.9870 | 0.9870 | nan | 0.9870 | 0.0 | 0.9870 | |
|
| 0.0183 | 27.06 | 460 | 0.0257 | 0.4960 | 0.9920 | 0.9920 | nan | 0.9920 | 0.0 | 0.9920 | |
|
| 0.024 | 28.24 | 480 | 0.0256 | 0.4950 | 0.9900 | 0.9900 | nan | 0.9900 | 0.0 | 0.9900 | |
|
| 0.0145 | 29.41 | 500 | 0.0245 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 | |
|
| 0.0158 | 30.59 | 520 | 0.0250 | 0.4947 | 0.9895 | 0.9895 | nan | 0.9895 | 0.0 | 0.9895 | |
|
| 0.0169 | 31.76 | 540 | 0.0247 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 | |
|
| 0.018 | 32.94 | 560 | 0.0237 | 0.4965 | 0.9930 | 0.9930 | nan | 0.9930 | 0.0 | 0.9930 | |
|
| 0.0161 | 34.12 | 580 | 0.0237 | 0.4956 | 0.9913 | 0.9913 | nan | 0.9913 | 0.0 | 0.9913 | |
|
| 0.0191 | 35.29 | 600 | 0.0241 | 0.4951 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 | |
|
| 0.0133 | 36.47 | 620 | 0.0240 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 | |
|
| 0.0118 | 37.65 | 640 | 0.0244 | 0.4949 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 | |
|
| 0.0133 | 38.82 | 660 | 0.0229 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 | |
|
| 0.0198 | 40.0 | 680 | 0.0236 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 | |
|
| 0.0168 | 41.18 | 700 | 0.0234 | 0.4961 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | 0.9923 | |
|
| 0.0119 | 42.35 | 720 | 0.0233 | 0.4957 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 | |
|
| 0.0154 | 43.53 | 740 | 0.0243 | 0.4950 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 | |
|
| 0.0126 | 44.71 | 760 | 0.0242 | 0.4949 | 0.9898 | 0.9898 | nan | 0.9898 | 0.0 | 0.9898 | |
|
| 0.0128 | 45.88 | 780 | 0.0243 | 0.4955 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 | |
|
| 0.0116 | 47.06 | 800 | 0.0239 | 0.4953 | 0.9907 | 0.9907 | nan | 0.9907 | 0.0 | 0.9907 | |
|
| 0.0121 | 48.24 | 820 | 0.0239 | 0.4954 | 0.9909 | 0.9909 | nan | 0.9909 | 0.0 | 0.9909 | |
|
| 0.0164 | 49.41 | 840 | 0.0236 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.0 |
|
- Tokenizers 0.13.3 |
|
|
