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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: dropoff-utcustom-train-SF-RGB-b0_6 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# dropoff-utcustom-train-SF-RGB-b0_6 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1833 |
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- Mean Iou: 0.6595 |
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- Mean Accuracy: 0.7018 |
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- Overall Accuracy: 0.9666 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.4113 |
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- Accuracy Undropoff: 0.9924 |
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- Iou Unlabeled: nan |
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- Iou Dropoff: 0.3531 |
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- Iou Undropoff: 0.9660 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 120 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| |
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| 1.1234 | 3.33 | 10 | 1.0973 | 0.1779 | 0.5629 | 0.3723 | nan | 0.7720 | 0.3538 | 0.0 | 0.1801 | 0.3536 | |
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| 0.975 | 6.67 | 20 | 1.0260 | 0.3499 | 0.8180 | 0.8109 | nan | 0.8259 | 0.8102 | 0.0 | 0.2428 | 0.8069 | |
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| 0.9464 | 10.0 | 30 | 0.8130 | 0.4297 | 0.7456 | 0.9502 | nan | 0.5212 | 0.9700 | 0.0 | 0.3385 | 0.9507 | |
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| 0.6167 | 13.33 | 40 | 0.6001 | 0.4451 | 0.7438 | 0.9617 | nan | 0.5048 | 0.9829 | 0.0 | 0.3743 | 0.9610 | |
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| 0.4818 | 16.67 | 50 | 0.4629 | 0.4491 | 0.7237 | 0.9666 | nan | 0.4573 | 0.9902 | 0.0 | 0.3815 | 0.9659 | |
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| 0.4733 | 20.0 | 60 | 0.4379 | 0.4335 | 0.7067 | 0.9630 | nan | 0.4256 | 0.9879 | 0.0 | 0.3383 | 0.9623 | |
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| 0.3843 | 23.33 | 70 | 0.4073 | 0.4310 | 0.6872 | 0.9652 | nan | 0.3821 | 0.9922 | 0.0 | 0.3283 | 0.9646 | |
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| 0.3579 | 26.67 | 80 | 0.3731 | 0.4354 | 0.6999 | 0.9651 | nan | 0.4090 | 0.9908 | 0.0 | 0.3418 | 0.9644 | |
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| 0.3212 | 30.0 | 90 | 0.3655 | 0.6589 | 0.7129 | 0.9647 | nan | 0.4366 | 0.9892 | nan | 0.3538 | 0.9640 | |
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| 0.3088 | 33.33 | 100 | 0.3306 | 0.6310 | 0.6689 | 0.9641 | nan | 0.3451 | 0.9928 | nan | 0.2985 | 0.9635 | |
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| 0.2825 | 36.67 | 110 | 0.3253 | 0.6633 | 0.7103 | 0.9663 | nan | 0.4293 | 0.9912 | nan | 0.3609 | 0.9657 | |
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| 0.3029 | 40.0 | 120 | 0.3130 | 0.6556 | 0.7079 | 0.9645 | nan | 0.4264 | 0.9895 | nan | 0.3474 | 0.9638 | |
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| 0.252 | 43.33 | 130 | 0.2898 | 0.6703 | 0.7310 | 0.9652 | nan | 0.4740 | 0.9880 | nan | 0.3762 | 0.9645 | |
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| 0.2395 | 46.67 | 140 | 0.2843 | 0.6587 | 0.7088 | 0.9653 | nan | 0.4275 | 0.9902 | nan | 0.3527 | 0.9646 | |
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| 0.2308 | 50.0 | 150 | 0.2744 | 0.6481 | 0.6870 | 0.9659 | nan | 0.3811 | 0.9930 | nan | 0.3309 | 0.9653 | |
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| 0.2125 | 53.33 | 160 | 0.2579 | 0.6555 | 0.7028 | 0.9653 | nan | 0.4147 | 0.9909 | nan | 0.3464 | 0.9647 | |
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| 0.1953 | 56.67 | 170 | 0.2551 | 0.6549 | 0.7054 | 0.9647 | nan | 0.4209 | 0.9899 | nan | 0.3458 | 0.9641 | |
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| 0.1743 | 60.0 | 180 | 0.2377 | 0.6393 | 0.6768 | 0.9651 | nan | 0.3605 | 0.9931 | nan | 0.3140 | 0.9646 | |
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| 0.17 | 63.33 | 190 | 0.2342 | 0.6564 | 0.7002 | 0.9660 | nan | 0.4086 | 0.9918 | nan | 0.3474 | 0.9654 | |
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| 0.173 | 66.67 | 200 | 0.2296 | 0.6629 | 0.7095 | 0.9664 | nan | 0.4277 | 0.9913 | nan | 0.3602 | 0.9657 | |
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| 0.1487 | 70.0 | 210 | 0.2152 | 0.6525 | 0.6861 | 0.9673 | nan | 0.3777 | 0.9946 | nan | 0.3383 | 0.9667 | |
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| 0.1501 | 73.33 | 220 | 0.2179 | 0.6593 | 0.7019 | 0.9665 | nan | 0.4116 | 0.9923 | nan | 0.3527 | 0.9659 | |
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| 0.1419 | 76.67 | 230 | 0.2055 | 0.6605 | 0.7057 | 0.9663 | nan | 0.4199 | 0.9916 | nan | 0.3553 | 0.9656 | |
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| 0.2049 | 80.0 | 240 | 0.2060 | 0.6563 | 0.7004 | 0.9659 | nan | 0.4091 | 0.9917 | nan | 0.3472 | 0.9653 | |
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| 0.1339 | 83.33 | 250 | 0.2006 | 0.6514 | 0.6921 | 0.9660 | nan | 0.3916 | 0.9926 | nan | 0.3375 | 0.9654 | |
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| 0.1262 | 86.67 | 260 | 0.1963 | 0.6559 | 0.7033 | 0.9654 | nan | 0.4158 | 0.9908 | nan | 0.3470 | 0.9647 | |
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| 0.179 | 90.0 | 270 | 0.1907 | 0.6549 | 0.6976 | 0.9660 | nan | 0.4032 | 0.9921 | nan | 0.3445 | 0.9654 | |
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| 0.1216 | 93.33 | 280 | 0.1901 | 0.6561 | 0.6994 | 0.9661 | nan | 0.4068 | 0.9920 | nan | 0.3468 | 0.9655 | |
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| 0.1144 | 96.67 | 290 | 0.1917 | 0.6565 | 0.7017 | 0.9658 | nan | 0.4119 | 0.9915 | nan | 0.3478 | 0.9652 | |
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| 0.1095 | 100.0 | 300 | 0.1900 | 0.6621 | 0.7108 | 0.9659 | nan | 0.4309 | 0.9907 | nan | 0.3590 | 0.9653 | |
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| 0.1144 | 103.33 | 310 | 0.1848 | 0.6595 | 0.6994 | 0.9670 | nan | 0.4058 | 0.9930 | nan | 0.3526 | 0.9664 | |
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| 0.1144 | 106.67 | 320 | 0.1849 | 0.6585 | 0.7011 | 0.9665 | nan | 0.4100 | 0.9922 | nan | 0.3512 | 0.9658 | |
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| 0.1574 | 110.0 | 330 | 0.1852 | 0.6592 | 0.7025 | 0.9664 | nan | 0.4128 | 0.9921 | nan | 0.3526 | 0.9658 | |
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| 0.1085 | 113.33 | 340 | 0.1819 | 0.6595 | 0.7016 | 0.9667 | nan | 0.4108 | 0.9924 | nan | 0.3530 | 0.9660 | |
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| 0.1099 | 116.67 | 350 | 0.1856 | 0.6602 | 0.7057 | 0.9662 | nan | 0.4198 | 0.9915 | nan | 0.3548 | 0.9656 | |
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| 0.1048 | 120.0 | 360 | 0.1833 | 0.6595 | 0.7018 | 0.9666 | nan | 0.4113 | 0.9924 | nan | 0.3531 | 0.9660 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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