--- license: other base_model: peldrak/segformer-b5-cityscapes-finetuned-coastTrain tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b5-cityscapes-finetuned-coastTrain-grCoastline results: [] --- # segformer-b5-cityscapes-finetuned-coastTrain-grCoastline This model is a fine-tuned version of [peldrak/segformer-b5-cityscapes-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b5-cityscapes-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset. It achieves the following results on the evaluation set: - Loss: 0.2369 - Mean Iou: 0.7581 - Mean Accuracy: 0.8319 - Overall Accuracy: 0.9386 - Accuracy Water: 0.9821 - Accuracy Whitewater: 0.4902 - Accuracy Sediment: 0.9136 - Accuracy Other Natural Terrain: 0.8026 - Accuracy Vegetation: 0.9297 - Accuracy Development: 0.7063 - Accuracy Unknown: 0.9989 - Iou Water: 0.9520 - Iou Whitewater: 0.3507 - Iou Sediment: 0.8649 - Iou Other Natural Terrain: 0.6679 - Iou Vegetation: 0.8298 - Iou Development: 0.6457 - Iou Unknown: 0.9956 - F1 Score: 0.9379 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:| | 0.4436 | 0.24 | 20 | 0.3559 | 0.6026 | 0.6838 | 0.8880 | 0.9773 | 0.0337 | 0.9374 | 0.5772 | 0.8436 | 0.4204 | 0.9972 | 0.8777 | 0.0293 | 0.7012 | 0.4913 | 0.7446 | 0.3805 | 0.9937 | 0.8816 | | 0.3897 | 0.49 | 40 | 0.2428 | 0.6524 | 0.7222 | 0.9165 | 0.9825 | 0.0 | 0.9378 | 0.7357 | 0.8910 | 0.5144 | 0.9944 | 0.9418 | 0.0 | 0.7760 | 0.5956 | 0.8037 | 0.4564 | 0.9931 | 0.9134 | | 0.5041 | 0.73 | 60 | 0.2645 | 0.6562 | 0.7196 | 0.9172 | 0.9864 | 0.0587 | 0.9317 | 0.6634 | 0.9456 | 0.4577 | 0.9934 | 0.9407 | 0.0581 | 0.7808 | 0.5880 | 0.8037 | 0.4299 | 0.9922 | 0.9118 | | 0.1355 | 0.98 | 80 | 0.1992 | 0.7084 | 0.7729 | 0.9324 | 0.9855 | 0.1154 | 0.9117 | 0.8215 | 0.8980 | 0.6808 | 0.9974 | 0.9470 | 0.1086 | 0.8482 | 0.6539 | 0.8245 | 0.5821 | 0.9943 | 0.9316 | | 0.1142 | 1.22 | 100 | 0.2597 | 0.6630 | 0.7299 | 0.9220 | 0.9852 | 0.0279 | 0.9498 | 0.8028 | 0.8860 | 0.4611 | 0.9965 | 0.9489 | 0.0278 | 0.7668 | 0.6533 | 0.8202 | 0.4298 | 0.9943 | 0.9184 | | 0.2346 | 1.46 | 120 | 0.2670 | 0.6708 | 0.7331 | 0.9239 | 0.9823 | 0.0530 | 0.9331 | 0.7716 | 0.9238 | 0.4729 | 0.9953 | 0.9497 | 0.0524 | 0.8044 | 0.6352 | 0.8128 | 0.4478 | 0.9936 | 0.9201 | | 0.4209 | 1.71 | 140 | 0.1952 | 0.7291 | 0.7955 | 0.9336 | 0.9688 | 0.2746 | 0.9536 | 0.8644 | 0.8830 | 0.6252 | 0.9989 | 0.9485 | 0.2476 | 0.8375 | 0.6754 | 0.8291 | 0.5719 | 0.9939 | 0.9328 | | 0.0803 | 1.95 | 160 | 0.2460 | 0.6772 | 0.7440 | 0.9228 | 0.9824 | 0.0952 | 0.9514 | 0.7835 | 0.8898 | 0.5101 | 0.9958 | 0.9458 | 0.0937 | 0.7832 | 0.6355 | 0.8190 | 0.4692 | 0.9943 | 0.9200 | | 0.3305 | 2.2 | 180 | 0.2127 | 0.7372 | 0.7992 | 0.9381 | 0.9869 | 0.1967 | 0.9330 | 0.7088 | 0.9337 | 0.8417 | 0.9935 | 0.9463 | 0.1844 | 0.8578 | 0.6514 | 0.8284 | 0.6996 | 0.9928 | 0.9366 | | 0.1355 | 2.44 | 200 | 0.1968 | 0.7219 | 0.7768 | 0.9387 | 0.9877 | 0.0983 | 0.9309 | 0.7413 | 0.9411 | 0.7414 | 0.9971 | 0.9480 | 0.0963 | 0.8611 | 0.6629 | 0.8333 | 0.6575 | 0.9941 | 0.9370 | | 0.0807 | 2.68 | 220 | 0.2531 | 0.6939 | 0.7569 | 0.9273 | 0.9826 | 0.1709 | 0.9534 | 0.7590 | 0.9194 | 0.5160 | 0.9969 | 0.9520 | 0.1628 | 0.8076 | 0.6326 | 0.8290 | 0.4785 | 0.9950 | 0.9242 | | 0.1226 | 2.93 | 240 | 0.2434 | 0.7382 | 0.8081 | 0.9315 | 0.9832 | 0.3932 | 0.9090 | 0.7441 | 0.9272 | 0.7026 | 0.9976 | 0.9340 | 0.3119 | 0.8604 | 0.6199 | 0.8192 | 0.6276 | 0.9947 | 0.9303 | | 0.0681 | 3.17 | 260 | 0.2265 | 0.7529 | 0.8220 | 0.9387 | 0.9802 | 0.3786 | 0.9371 | 0.7742 | 0.9194 | 0.7668 | 0.9974 | 0.9506 | 0.3042 | 0.8661 | 0.6614 | 0.8315 | 0.6617 | 0.9950 | 0.9380 | | 0.1304 | 3.41 | 280 | 0.2342 | 0.7360 | 0.8009 | 0.9348 | 0.9772 | 0.3403 | 0.9507 | 0.7835 | 0.9220 | 0.6358 | 0.9971 | 0.9486 | 0.2864 | 0.8471 | 0.6572 | 0.8302 | 0.5880 | 0.9947 | 0.9333 | | 0.1353 | 3.66 | 300 | 0.1970 | 0.7426 | 0.8071 | 0.9400 | 0.9803 | 0.2770 | 0.9285 | 0.8866 | 0.8962 | 0.6822 | 0.9987 | 0.9508 | 0.2259 | 0.8732 | 0.6883 | 0.8370 | 0.6278 | 0.9954 | 0.9397 | | 0.3968 | 3.9 | 320 | 0.2181 | 0.7551 | 0.8214 | 0.9389 | 0.9868 | 0.3918 | 0.9280 | 0.7945 | 0.9160 | 0.7343 | 0.9982 | 0.9512 | 0.3204 | 0.8645 | 0.6646 | 0.8297 | 0.6594 | 0.9962 | 0.9381 | | 0.0548 | 4.15 | 340 | 0.2025 | 0.7645 | 0.8313 | 0.9411 | 0.9804 | 0.4091 | 0.9287 | 0.7892 | 0.9208 | 0.7920 | 0.9987 | 0.9525 | 0.3393 | 0.8686 | 0.6729 | 0.8347 | 0.6878 | 0.9956 | 0.9406 | | 0.0653 | 4.39 | 360 | 0.2549 | 0.7399 | 0.8105 | 0.9327 | 0.9851 | 0.4396 | 0.9222 | 0.8119 | 0.9144 | 0.6029 | 0.9976 | 0.9528 | 0.3534 | 0.8455 | 0.6479 | 0.8239 | 0.5602 | 0.9957 | 0.9314 | | 0.1244 | 4.63 | 380 | 0.2580 | 0.7216 | 0.7821 | 0.9336 | 0.9925 | 0.2673 | 0.9136 | 0.7674 | 0.9405 | 0.5951 | 0.9984 | 0.9465 | 0.2327 | 0.8327 | 0.6598 | 0.8338 | 0.5501 | 0.9957 | 0.9312 | | 0.2298 | 4.88 | 400 | 0.2345 | 0.7441 | 0.8112 | 0.9368 | 0.9760 | 0.2900 | 0.9511 | 0.8680 | 0.8716 | 0.7250 | 0.9965 | 0.9520 | 0.2585 | 0.8639 | 0.6659 | 0.8229 | 0.6497 | 0.9956 | 0.9369 | | 0.0469 | 5.12 | 420 | 0.2614 | 0.7131 | 0.7813 | 0.9279 | 0.9854 | 0.3576 | 0.9420 | 0.8114 | 0.9099 | 0.4639 | 0.9986 | 0.9534 | 0.3075 | 0.8081 | 0.6597 | 0.8237 | 0.4432 | 0.9957 | 0.9244 | | 0.1398 | 5.37 | 440 | 0.2542 | 0.7344 | 0.8077 | 0.9318 | 0.9851 | 0.4151 | 0.9388 | 0.8538 | 0.8842 | 0.5789 | 0.9979 | 0.9561 | 0.3381 | 0.8258 | 0.6665 | 0.8241 | 0.5345 | 0.9957 | 0.9305 | | 0.0683 | 5.61 | 460 | 0.2496 | 0.7472 | 0.8213 | 0.9365 | 0.9856 | 0.5096 | 0.9419 | 0.8014 | 0.9301 | 0.5823 | 0.9980 | 0.9569 | 0.3788 | 0.8228 | 0.6847 | 0.8445 | 0.5467 | 0.9961 | 0.9346 | | 0.141 | 5.85 | 480 | 0.2251 | 0.7514 | 0.8174 | 0.9393 | 0.9873 | 0.3664 | 0.9389 | 0.8465 | 0.8977 | 0.6867 | 0.9982 | 0.9559 | 0.3151 | 0.8544 | 0.6856 | 0.8387 | 0.6140 | 0.9962 | 0.9387 | | 0.2161 | 6.1 | 500 | 0.2369 | 0.7581 | 0.8319 | 0.9386 | 0.9821 | 0.4902 | 0.9136 | 0.8026 | 0.9297 | 0.7063 | 0.9989 | 0.9520 | 0.3507 | 0.8649 | 0.6679 | 0.8298 | 0.6457 | 0.9956 | 0.9379 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2