đ segformer-finetuned-lane-10k-steps
This model is a fine - tuned version of nvidia/segformer-b0-finetuned-cityscapes-512-1024 on the Efferbach/lane_master dataset, used for image segmentation tasks.
đ Quick Start
This model is a fine-tuned version of nvidia/segformer-b0-finetuned-cityscapes-512-1024 on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0365
- Mean Iou: 0.4899
- Mean Accuracy: 0.7371
- Overall Accuracy: 0.7371
- Accuracy Background: nan
- Accuracy Left: 0.7394
- Accuracy Right: 0.7348
- Iou Background: 0.0
- Iou Left: 0.7371
- Iou Right: 0.7325
đ Documentation
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Mean Iou |
Mean Accuracy |
Overall Accuracy |
Accuracy Background |
Accuracy Left |
Accuracy Right |
Iou Background |
Iou Left |
Iou Right |
0.0792 |
1.0 |
308 |
0.0714 |
0.0148 |
0.0229 |
0.0225 |
nan |
0.0373 |
0.0085 |
0.0 |
0.0362 |
0.0083 |
0.0437 |
2.0 |
616 |
0.0502 |
0.1687 |
0.2775 |
0.2784 |
nan |
0.2492 |
0.3058 |
0.0 |
0.2343 |
0.2718 |
0.0326 |
3.0 |
924 |
0.0445 |
0.2614 |
0.4441 |
0.4479 |
nan |
0.3134 |
0.5748 |
0.0 |
0.3100 |
0.4742 |
0.0224 |
4.0 |
1232 |
0.0370 |
0.4048 |
0.6098 |
0.6100 |
nan |
0.6043 |
0.6153 |
0.0 |
0.6031 |
0.6113 |
0.0184 |
5.0 |
1540 |
0.0346 |
0.3820 |
0.5858 |
0.5870 |
nan |
0.5421 |
0.6295 |
0.0 |
0.5400 |
0.6060 |
0.0159 |
6.0 |
1848 |
0.0319 |
0.4367 |
0.6567 |
0.6573 |
nan |
0.6343 |
0.6791 |
0.0 |
0.6341 |
0.6760 |
0.0139 |
7.0 |
2156 |
0.0317 |
0.4555 |
0.6855 |
0.6860 |
nan |
0.6691 |
0.7019 |
0.0 |
0.6680 |
0.6986 |
0.0129 |
8.0 |
2464 |
0.0321 |
0.4348 |
0.6533 |
0.6535 |
nan |
0.6479 |
0.6588 |
0.0 |
0.6474 |
0.6571 |
0.0122 |
9.0 |
2772 |
0.0275 |
0.4541 |
0.6827 |
0.6830 |
nan |
0.6710 |
0.6943 |
0.0 |
0.6697 |
0.6927 |
0.0111 |
10.0 |
3080 |
0.0305 |
0.4609 |
0.6928 |
0.6927 |
nan |
0.6969 |
0.6887 |
0.0 |
0.6963 |
0.6865 |
0.011 |
11.0 |
3388 |
0.0286 |
0.4646 |
0.6988 |
0.6991 |
nan |
0.6890 |
0.7087 |
0.0 |
0.6883 |
0.7055 |
0.0103 |
12.0 |
3696 |
0.0298 |
0.4693 |
0.7058 |
0.7062 |
nan |
0.6939 |
0.7177 |
0.0 |
0.6932 |
0.7148 |
0.0097 |
13.0 |
4004 |
0.0293 |
0.4717 |
0.7090 |
0.7087 |
nan |
0.7184 |
0.6996 |
0.0 |
0.7176 |
0.6975 |
0.0093 |
14.0 |
4312 |
0.0330 |
0.4537 |
0.6835 |
0.6836 |
nan |
0.6775 |
0.6894 |
0.0 |
0.6768 |
0.6843 |
0.009 |
15.0 |
4620 |
0.0331 |
0.4804 |
0.7226 |
0.7226 |
nan |
0.7194 |
0.7257 |
0.0 |
0.7178 |
0.7234 |
0.0088 |
16.0 |
4928 |
0.0315 |
0.4890 |
0.7355 |
0.7357 |
nan |
0.7275 |
0.7435 |
0.0 |
0.7259 |
0.7411 |
0.0086 |
17.0 |
5236 |
0.0338 |
0.4813 |
0.7234 |
0.7234 |
nan |
0.7224 |
0.7243 |
0.0 |
0.7216 |
0.7223 |
0.0085 |
18.0 |
5544 |
0.0348 |
0.4743 |
0.7129 |
0.7126 |
nan |
0.7225 |
0.7033 |
0.0 |
0.7217 |
0.7012 |
0.0083 |
19.0 |
5852 |
0.0357 |
0.4812 |
0.7245 |
0.7244 |
nan |
0.7281 |
0.7210 |
0.0 |
0.7254 |
0.7183 |
0.0081 |
20.0 |
6160 |
0.0334 |
0.4829 |
0.7271 |
0.7269 |
nan |
0.7337 |
0.7205 |
0.0 |
0.7305 |
0.7182 |
0.0079 |
21.0 |
6468 |
0.0359 |
0.4773 |
0.7177 |
0.7177 |
nan |
0.7184 |
0.7170 |
0.0 |
0.7174 |
0.7146 |
0.0077 |
22.0 |
6776 |
0.0351 |
0.4874 |
0.7332 |
0.7329 |
nan |
0.7440 |
0.7223 |
0.0 |
0.7432 |
0.7190 |
0.0075 |
23.0 |
7084 |
0.0344 |
0.4855 |
0.7296 |
0.7292 |
nan |
0.7437 |
0.7156 |
0.0 |
0.7425 |
0.7141 |
0.0077 |
24.0 |
7392 |
0.0362 |
0.4799 |
0.7216 |
0.7216 |
nan |
0.7236 |
0.7196 |
0.0 |
0.7223 |
0.7174 |
0.0071 |
25.0 |
7700 |
0.0391 |
0.4775 |
0.7179 |
0.7180 |
nan |
0.7173 |
0.7186 |
0.0 |
0.7161 |
0.7163 |
0.0077 |
26.0 |
8008 |
0.0339 |
0.4895 |
0.7367 |
0.7366 |
nan |
0.7405 |
0.7329 |
0.0 |
0.7388 |
0.7297 |
0.0069 |
27.0 |
8316 |
0.0344 |
0.4858 |
0.7305 |
0.7305 |
nan |
0.7291 |
0.7318 |
0.0 |
0.7278 |
0.7297 |
0.0069 |
28.0 |
8624 |
0.0361 |
0.4844 |
0.7283 |
0.7282 |
nan |
0.7324 |
0.7243 |
0.0 |
0.7309 |
0.7221 |
0.007 |
29.0 |
8932 |
0.0371 |
0.4837 |
0.7273 |
0.7270 |
nan |
0.7360 |
0.7186 |
0.0 |
0.7345 |
0.7166 |
0.007 |
30.0 |
9240 |
0.0366 |
0.4854 |
0.7305 |
0.7303 |
nan |
0.7379 |
0.7231 |
0.0 |
0.7353 |
0.7208 |
0.0067 |
31.0 |
9548 |
0.0367 |
0.4866 |
0.7322 |
0.7321 |
nan |
0.7357 |
0.7286 |
0.0 |
0.7335 |
0.7263 |
0.0068 |
32.0 |
9856 |
0.0364 |
0.4883 |
0.7348 |
0.7347 |
nan |
0.7377 |
0.7318 |
0.0 |
0.7355 |
0.7295 |
0.0067 |
32.47 |
10000 |
0.0365 |
0.4899 |
0.7371 |
0.7371 |
nan |
0.7394 |
0.7348 |
0.0 |
0.7371 |
0.7325 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
đ License
other