--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-lipid-droplets-v2 results: [] --- # segformer-b0-finetuned-lipid-droplets-v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the jhaberbe/lipid-droplets-v3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1451 - Mean Iou: 0.3767 - Mean Accuracy: 0.7533 - Overall Accuracy: 0.7533 - Accuracy Unlabeled: nan - Accuracy Lipid: 0.7533 - Iou Unlabeled: 0.0 - Iou Lipid: 0.7533 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lipid | Iou Unlabeled | Iou Lipid | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:| | 0.5878 | 1.82 | 20 | 0.6215 | 0.2190 | 0.4381 | 0.4381 | nan | 0.4381 | 0.0 | 0.4381 | | 0.4506 | 3.64 | 40 | 0.4553 | 0.2542 | 0.5084 | 0.5084 | nan | 0.5084 | 0.0 | 0.5084 | | 0.3435 | 5.45 | 60 | 0.3476 | 0.2703 | 0.5405 | 0.5405 | nan | 0.5405 | 0.0 | 0.5405 | | 0.2931 | 7.27 | 80 | 0.2191 | 0.2148 | 0.4296 | 0.4296 | nan | 0.4296 | 0.0 | 0.4296 | | 0.2361 | 9.09 | 100 | 0.2199 | 0.2829 | 0.5659 | 0.5659 | nan | 0.5659 | 0.0 | 0.5659 | | 0.2739 | 10.91 | 120 | 0.2493 | 0.2668 | 0.5336 | 0.5336 | nan | 0.5336 | 0.0 | 0.5336 | | 0.1993 | 12.73 | 140 | 0.2123 | 0.3617 | 0.7234 | 0.7234 | nan | 0.7234 | 0.0 | 0.7234 | | 0.1226 | 14.55 | 160 | 0.0776 | 0.1070 | 0.2140 | 0.2140 | nan | 0.2140 | 0.0 | 0.2140 | | 0.1348 | 16.36 | 180 | 0.1453 | 0.3343 | 0.6686 | 0.6686 | nan | 0.6686 | 0.0 | 0.6686 | | 0.0939 | 18.18 | 200 | 0.1973 | 0.3632 | 0.7265 | 0.7265 | nan | 0.7265 | 0.0 | 0.7265 | | 0.1426 | 20.0 | 220 | 0.1698 | 0.2628 | 0.5256 | 0.5256 | nan | 0.5256 | 0.0 | 0.5256 | | 0.0722 | 21.82 | 240 | 0.1061 | 0.3520 | 0.7040 | 0.7040 | nan | 0.7040 | 0.0 | 0.7040 | | 0.0764 | 23.64 | 260 | 0.1653 | 0.2990 | 0.5979 | 0.5979 | nan | 0.5979 | 0.0 | 0.5979 | | 0.0551 | 25.45 | 280 | 0.1072 | 0.3103 | 0.6206 | 0.6206 | nan | 0.6206 | 0.0 | 0.6206 | | 0.0437 | 27.27 | 300 | 0.2012 | 0.3494 | 0.6988 | 0.6988 | nan | 0.6988 | 0.0 | 0.6988 | | 0.0514 | 29.09 | 320 | 0.1825 | 0.3777 | 0.7553 | 0.7553 | nan | 0.7553 | 0.0 | 0.7553 | | 0.1095 | 30.91 | 340 | 0.0897 | 0.3392 | 0.6785 | 0.6785 | nan | 0.6785 | 0.0 | 0.6785 | | 0.0682 | 32.73 | 360 | 0.1785 | 0.3504 | 0.7008 | 0.7008 | nan | 0.7008 | 0.0 | 0.7008 | | 0.0422 | 34.55 | 380 | 0.1167 | 0.3444 | 0.6887 | 0.6887 | nan | 0.6887 | 0.0 | 0.6887 | | 0.0538 | 36.36 | 400 | 0.2332 | 0.4529 | 0.9057 | 0.9057 | nan | 0.9057 | 0.0 | 0.9057 | | 0.0347 | 38.18 | 420 | 0.1698 | 0.3115 | 0.6231 | 0.6231 | nan | 0.6231 | 0.0 | 0.6231 | | 0.0496 | 40.0 | 440 | 0.1201 | 0.3278 | 0.6555 | 0.6555 | nan | 0.6555 | 0.0 | 0.6555 | | 0.0681 | 41.82 | 460 | 0.1830 | 0.3916 | 0.7831 | 0.7831 | nan | 0.7831 | 0.0 | 0.7831 | | 0.0498 | 43.64 | 480 | 0.1848 | 0.4086 | 0.8172 | 0.8172 | nan | 0.8172 | 0.0 | 0.8172 | | 0.0365 | 45.45 | 500 | 0.1234 | 0.3741 | 0.7481 | 0.7481 | nan | 0.7481 | 0.0 | 0.7481 | | 0.0258 | 47.27 | 520 | 0.2000 | 0.4300 | 0.8599 | 0.8599 | nan | 0.8599 | 0.0 | 0.8599 | | 0.0355 | 49.09 | 540 | 0.1273 | 0.3907 | 0.7814 | 0.7814 | nan | 0.7814 | 0.0 | 0.7814 | | 0.0476 | 50.91 | 560 | 0.1827 | 0.4322 | 0.8644 | 0.8644 | nan | 0.8644 | 0.0 | 0.8644 | | 0.0472 | 52.73 | 580 | 0.1014 | 0.3420 | 0.6839 | 0.6839 | nan | 0.6839 | 0.0 | 0.6839 | | 0.0467 | 54.55 | 600 | 0.1330 | 0.3758 | 0.7516 | 0.7516 | nan | 0.7516 | 0.0 | 0.7516 | | 0.0802 | 56.36 | 620 | 0.0698 | 0.3073 | 0.6147 | 0.6147 | nan | 0.6147 | 0.0 | 0.6147 | | 0.0558 | 58.18 | 640 | 0.1291 | 0.4088 | 0.8176 | 0.8176 | nan | 0.8176 | 0.0 | 0.8176 | | 0.0443 | 60.0 | 660 | 0.1097 | 0.4060 | 0.8119 | 0.8119 | nan | 0.8119 | 0.0 | 0.8119 | | 0.0442 | 61.82 | 680 | 0.1112 | 0.3944 | 0.7887 | 0.7887 | nan | 0.7887 | 0.0 | 0.7887 | | 0.0422 | 63.64 | 700 | 0.1837 | 0.4288 | 0.8576 | 0.8576 | nan | 0.8576 | 0.0 | 0.8576 | | 0.0216 | 65.45 | 720 | 0.1140 | 0.3735 | 0.7470 | 0.7470 | nan | 0.7470 | 0.0 | 0.7470 | | 0.0414 | 67.27 | 740 | 0.1017 | 0.3821 | 0.7643 | 0.7643 | nan | 0.7643 | 0.0 | 0.7643 | | 0.0336 | 69.09 | 760 | 0.1458 | 0.3685 | 0.7370 | 0.7370 | nan | 0.7370 | 0.0 | 0.7370 | | 0.0575 | 70.91 | 780 | 0.1392 | 0.3425 | 0.6851 | 0.6851 | nan | 0.6851 | 0.0 | 0.6851 | | 0.0324 | 72.73 | 800 | 0.1162 | 0.3689 | 0.7377 | 0.7377 | nan | 0.7377 | 0.0 | 0.7377 | | 0.0336 | 74.55 | 820 | 0.1366 | 0.4143 | 0.8287 | 0.8287 | nan | 0.8287 | 0.0 | 0.8287 | | 0.0889 | 76.36 | 840 | 0.1604 | 0.3726 | 0.7452 | 0.7452 | nan | 0.7452 | 0.0 | 0.7452 | | 0.0438 | 78.18 | 860 | 0.1528 | 0.3948 | 0.7895 | 0.7895 | nan | 0.7895 | 0.0 | 0.7895 | | 0.0194 | 80.0 | 880 | 0.1360 | 0.3335 | 0.6671 | 0.6671 | nan | 0.6671 | 0.0 | 0.6671 | | 0.0311 | 81.82 | 900 | 0.1832 | 0.4278 | 0.8555 | 0.8555 | nan | 0.8555 | 0.0 | 0.8555 | | 0.0478 | 83.64 | 920 | 0.0718 | 0.2656 | 0.5312 | 0.5312 | nan | 0.5312 | 0.0 | 0.5312 | | 0.0363 | 85.45 | 940 | 0.1281 | 0.3630 | 0.7259 | 0.7259 | nan | 0.7259 | 0.0 | 0.7259 | | 0.0354 | 87.27 | 960 | 0.0986 | 0.3891 | 0.7782 | 0.7782 | nan | 0.7782 | 0.0 | 0.7782 | | 0.0195 | 89.09 | 980 | 0.1582 | 0.4123 | 0.8247 | 0.8247 | nan | 0.8247 | 0.0 | 0.8247 | | 0.0432 | 90.91 | 1000 | 0.1086 | 0.3217 | 0.6433 | 0.6433 | nan | 0.6433 | 0.0 | 0.6433 | | 0.0288 | 92.73 | 1020 | 0.1669 | 0.4089 | 0.8179 | 0.8179 | nan | 0.8179 | 0.0 | 0.8179 | | 0.0326 | 94.55 | 1040 | 0.1335 | 0.3908 | 0.7816 | 0.7816 | nan | 0.7816 | 0.0 | 0.7816 | | 0.0384 | 96.36 | 1060 | 0.1415 | 0.3947 | 0.7895 | 0.7895 | nan | 0.7895 | 0.0 | 0.7895 | | 0.0399 | 98.18 | 1080 | 0.1784 | 0.4204 | 0.8408 | 0.8408 | nan | 0.8408 | 0.0 | 0.8408 | | 0.0214 | 100.0 | 1100 | 0.1451 | 0.3767 | 0.7533 | 0.7533 | nan | 0.7533 | 0.0 | 0.7533 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2