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mit-b0-CMP_semantic_seg_with_mps_v2

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metadata
language:
  - en
license: other
tags:
  - computer-vision
  - generated_from_trainer
model-index:
  - name: mit-b0-CMP_semantic_seg_with_mps_v2
    results: []
datasets:
  - Xpitfire/cmp_facade
metrics:
  - mean_iou
pipeline_tag: image-segmentation

mit-b0-CMP_semantic_seg_with_mps_v2

This model is a fine-tuned version of nvidia/mit-b0.

It achieves the following results on the evaluation set:

  • Loss: 1.0863
  • Mean Iou: 0.4097
  • Mean Accuracy: 0.5538
  • Overall Accuracy: 0.6951
  • Per Category Iou:
    • Segment 0: 0.5921698801573617
    • Segment 1: 0.5795623712718901
    • Segment 2: 0.5784812820145221
    • Segment 3: 0.2917052691882505
    • Segment 4: 0.3792639848157326
    • Segment 5: 0.37973303153855376
    • Segment 6: 0.4481097636024487
    • Segment 7: 0.4354492668218124
    • Segment 8: 0.26472453634508664
    • Segment 9: 0.4173722023142026
    • Segment 10: 0.18166072949276144
    • Segment 11: 0.36809541729585366
  • Per Category Accuracy:
    • Segment 0: 0.6884460857323806
    • Segment 1: 0.7851625477616788
    • Segment 2: 0.7322992353412343
    • Segment 3: 0.45229387721112274
    • Segment 4: 0.5829333862769369
    • Segment 5: 0.5516333441001092
    • Segment 6: 0.5904157921999404
    • Segment 7: 0.5288772981353482
    • Segment 8: 0.4518224891972707
    • Segment 9: 0.571864661897264
    • Segment 10: 0.23178753217655862
    • Segment 11: 0.47833833709905393

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Trained%2C%20But%20to%20My%20Standard/Center%20for%20Machine%20Perception/Version%202/Center%20for%20Machine%20Perception%20-%20semantic_segmentation_v2.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to use it, but remember that it is at your own risk/peril.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/Xpitfire/cmp_facade

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 Segment 0: Per Category Iou Segment 1: Per Category Iou Segment 2: Per Category Iou Segment 3: Per Category Iou Segment 4: Per Category Iou Segment 5: Per Category Iou Segment 6: Per Category Iou Segment 7: Per Category Iou Segment 8: Per Category Iou Segment 9: Per Category Iou Segment 10: Per Category Iou Segment 11: Per Category Iou Segment 0: Per Category Accuracy Segment 1: Per Category Accuracy Segment 2: Per Category Accuracy Segment 3: Per Category Accuracy Segment 4: Per Category Accuracy Segment 5: Per Category Accuracy Segment 6: Per Category Accuracy Segment 7: Per Category Accuracy Segment 8: Per Category Accuracy Segment 9: Per Category Accuracy Segment 10: Per Category Accuracy Segment 11: Per Category Accuracy
1.6807 1.0 189 1.3310 0.2226 0.3388 0.5893 0.4635 0.4905 0.4698 0.0 0.2307 0.1515 0.2789 0.0002 0.0250 0.3527 0.0 0.2087 0.6133 0.6847 0.7408 0.0 0.4973 0.1720 0.4073 0.0002 0.0255 0.6371 0.0 0.2874
1.1837 2.0 378 1.1731 0.2602 0.3876 0.6122 0.4240 0.5249 0.5152 0.0057 0.2636 0.2756 0.3312 0.0575 0.0539 0.3860 0.0 0.2854 0.4782 0.7844 0.6966 0.0057 0.5735 0.3684 0.6226 0.0577 0.0563 0.5907 0.0 0.4168
1.0241 3.0 567 1.0485 0.2915 0.3954 0.6393 0.5442 0.5037 0.5329 0.0412 0.3062 0.2714 0.3820 0.1430 0.0796 0.4007 0.0002 0.2929 0.8126 0.6852 0.6683 0.0420 0.4972 0.3418 0.5121 0.1453 0.0849 0.5882 0.0002 0.3672
0.9353 4.0 756 0.9943 0.3054 0.4021 0.6570 0.5776 0.5289 0.5391 0.1171 0.3137 0.2600 0.3664 0.1527 0.1074 0.3935 0.0002 0.3078 0.8079 0.7362 0.6803 0.1231 0.5129 0.3324 0.4212 0.1554 0.1223 0.5587 0.0002 0.3751
0.8717 5.0 945 1.0010 0.3299 0.4440 0.6530 0.4790 0.5506 0.5472 0.1547 0.3372 0.3297 0.4151 0.2339 0.1709 0.4081 0.00082 0.3314 0.5408 0.8111 0.7439 0.1647 0.5336 0.4720 0.5650 0.2459 0.2127 0.6032 0.0008 0.4343
0.8238 6.0 1134 0.9537 0.3546 0.4771 0.6701 0.5572 0.5525 0.5611 0.2076 0.3434 0.3163 0.4103 0.3279 0.2107 0.4191 0.0067 0.3418 0.6870 0.7532 0.7389 0.2428 0.5081 0.4173 0.5923 0.3710 0.3117 0.6181 0.0068 0.4785
0.7415 8.0 1512 0.9738 0.3554 0.4634 0.6733 0.5366 0.5659 0.5550 0.2331 0.3497 0.3334 0.4301 0.3401 0.1989 0.4181 0.0358 0.2680 0.6081 0.8461 0.6598 0.3035 0.5720 0.4540 0.5735 0.3849 0.2642 0.5608 0.0379 0.2962
0.7708 7.0 1323 0.9789 0.3550 0.4837 0.6683 0.5310 0.5634 0.5594 0.2299 0.3424 0.3375 0.4050 0.2883 0.2197 0.4142 0.0316 0.3373 0.6050 0.7961 0.7434 0.2876 0.5835 0.4949 0.5608 0.3103 0.3672 0.6185 0.0345 0.4022
0.7018 9.0 1701 0.9449 0.3667 0.4802 0.6826 0.5798 0.5657 0.5624 0.2368 0.3648 0.3271 0.4250 0.3207 0.2096 0.4236 0.0504 0.3346 0.7241 0.7684 0.7677 0.2958 0.5321 0.4212 0.5547 0.3513 0.2813 0.5645 0.0544 0.4465
0.682 10.0 1890 0.9422 0.3762 0.5047 0.6805 0.5802 0.5622 0.5585 0.2340 0.3793 0.3407 0.4277 0.3801 0.2301 0.4216 0.0640 0.3367 0.7124 0.7649 0.7024 0.2879 0.5535 0.4413 0.6310 0.4960 0.3982 0.5592 0.0724 0.4370
0.6503 11.0 2079 0.9889 0.3785 0.5082 0.6729 0.5193 0.5649 0.5605 0.2698 0.3772 0.3526 0.4342 0.3433 0.2415 0.4336 0.0889 0.3562 0.5876 0.8060 0.7296 0.3838 0.5267 0.4983 0.5902 0.3838 0.4151 0.5987 0.1030 0.4756
0.633 12.0 2268 0.9594 0.3901 0.5224 0.6797 0.5539 0.5641 0.5679 0.2658 0.3757 0.3510 0.4257 0.3993 0.2354 0.4338 0.1800 0.3287 0.6497 0.7807 0.7448 0.4018 0.5381 0.4615 0.5849 0.4883 0.3248 0.6063 0.2918 0.3958
0.6035 13.0 2457 0.9612 0.3939 0.5288 0.6834 0.5663 0.5666 0.5679 0.2631 0.3726 0.3609 0.4351 0.3759 0.2511 0.4256 0.1737 0.3681 0.6650 0.7792 0.7595 0.40489 0.5501 0.4940 0.5831 0.4375 0.3843 0.5591 0.2578 0.4711
0.5874 14.0 2646 0.9657 0.3939 0.5383 0.6844 0.5807 0.5670 0.5679 0.2670 0.3594 0.3605 0.4393 0.3863 0.2406 0.4228 0.1705 0.3652 0.6881 0.7715 0.7076 0.4518 0.6011 0.4900 0.6235 0.4466 0.3627 0.5934 0.2537 0.4702
0.5684 15.0 2835 0.9762 0.3950 0.5446 0.6855 0.5800 0.5711 0.5671 0.2825 0.3664 0.3587 0.4408 0.4021 0.2540 0.4246 0.1376 0.3548 0.6690 0.7721 0.7253 0.4607 0.6286 0.4900 0.5936 0.4951 0.4337 0.6295 0.1749 0.4630
0.5485 16.0 3024 1.0645 0.3794 0.5095 0.6704 0.4855 0.5683 0.5685 0.2612 0.3832 0.3628 0.4378 0.4056 0.2525 0.4206 0.1242 0.2825 0.5250 0.8335 0.7460 0.3742 0.6114 0.4823 0.5880 0.5021 0.4084 0.5757 0.1498 0.3171
0.5402 17.0 3213 0.9747 0.4044 0.5600 0.6839 0.5697 0.5674 0.5687 0.2971 0.3767 0.3741 0.4486 0.4126 0.2489 0.4260 0.1874 0.3757 0.6652 0.7673 0.7058 0.4318 0.5995 0.5137 0.6112 0.5596 0.4548 0.5819 0.2821 0.5465
0.5275 18.0 3402 1.0054 0.3944 0.5411 0.6790 0.5341 0.5728 0.5616 0.2827 0.3823 0.3782 0.4298 0.4070 0.2578 0.4195 0.1448 0.3632 0.6012 0.8091 0.6765 0.4561 0.5707 0.5393 0.6255 0.5679 0.4347 0.5567 0.1806 0.4751
0.5032 19.0 3591 1.0014 0.3973 0.5256 0.6875 0.5696 0.5739 0.5699 0.2918 0.3717 0.3635 0.4444 0.4122 0.2531 0.4142 0.1659 0.3369 0.6634 0.8079 0.6986 0.4389 0.5274 0.4876 0.6232 0.5022 0.3717 0.5244 0.2232 0.4388
0.4985 20.0 3780 0.9893 0.3990 0.5468 0.6883 0.5937 0.5702 0.5630 0.2892 0.3790 0.3757 0.4383 0.4110 0.2592 0.4147 0.1291 0.3653 0.7110 0.7679 0.6952 0.4875 0.5261 0.5549 0.6444 0.5301 0.4512 0.5441 0.1603 0.4888
0.4925 21.0 3969 1.0416 0.3955 0.5339 0.6806 0.5336 0.5723 0.5732 0.2843 0.3748 0.3738 0.4383 0.3876 0.2598 0.4170 0.1693 0.3624 0.5945 0.8130 0.7299 0.4511 0.5922 0.5324 0.5643 0.4341 0.4067 0.5834 0.2272 0.4781
0.4772 22.0 4158 1.0142 0.3969 0.5476 0.6838 0.5634 0.5752 0.5595 0.2783 0.3833 0.3540 0.4448 0.4054 0.2586 0.4145 0.1597 0.3660 0.6478 0.7921 0.6887 0.4826 0.5784 0.4599 0.6029 0.5938 0.4905 0.5605 0.2094 0.4644
0.4707 23.0 4347 0.9896 0.4077 0.5458 0.6966 0.6013 0.5801 0.5794 0.2988 0.3816 0.3736 0.4464 0.4241 0.2633 0.4162 0.1747 0.3530 0.7110 0.7878 0.7192 0.4629 0.5670 0.5061 0.5891 0.5354 0.4442 0.5585 0.2280 0.4401
0.4601 24.0 4536 1.0040 0.4104 0.5551 0.6948 0.6061 0.5756 0.5721 0.3086 0.3771 0.3707 0.4459 0.4242 0.2665 0.4104 0.1942 0.3732 0.7277 0.7718 0.7095 0.4789 0.5401 0.5080 0.6040 0.5314 0.4573 0.5414 0.2853 0.5062
0.4544 25.0 4725 1.0093 0.4093 0.5652 0.6899 0.5826 0.5745 0.5742 0.3109 0.3765 0.3784 0.4441 0.4184 0.2609 0.4219 0.1930 0.3765 0.6781 0.7703 0.7305 0.5102 0.5954 0.5311 0.5960 0.5286 0.4647 0.5861 0.2676 0.5242
0.4421 26.0 4914 1.0434 0.4064 0.5448 0.6938 0.5783 0.5821 0.5770 0.2985 0.3885 0.3582 0.4458 0.4220 0.2717 0.4260 0.1690 0.3600 0.6603 0.7989 0.7349 0.4689 0.5677 0.4620 0.6111 0.5258 0.4556 0.5889 0.2110 0.4530
0.4293 27.0 5103 1.0391 0.4076 0.5571 0.6908 0.5764 0.5777 0.5749 0.2868 0.3824 0.3857 0.4450 0.4170 0.2644 0.4295 0.1922
0.4312 28.0 5292 1.0037 0.4100 0.5534 0.6958 0.6023 0.5776 0.5769 0.2964 0.3759 0.3758 0.4464 0.4245 0.2712 0.4083 0.1967 0.3680 0.7218 0.7735 0.7273 0.4297 0.6001 0.5321
0.4309 29.0 5481 1.0288 0.4101 0.5493 0.6968 0.6043 0.5814 0.5728 0.2882 0.3867 0.3841 0.4369 0.4254 0.2659 0.4252 0.2106 0.3391 0.7054 0.7948 0.7009 0.4552 0.5413 0.5357 0.5421 0.5250 0.4701 0.5949 0.3048 0.4213
0.4146 30.0 5670 1.0602 0.4062 0.5445 0.6928 0.5840 0.5792 0.5750 0.2859 0.3839 0.3786 0.4479 0.4259 0.2664 0.3947 0.1753 0.3780 0.6744 0.8004 0.7289 0.4421 0.5410 0.5409 0.5822 0.5334 0.4790 0.5028 0.2177 0.4910
0.4106 31.0 5859 1.0573 0.4113 0.5520 0.6937 0.5819 0.5787 0.5775 0.2882 0.3861 0.3888 0.4522 0.4207 0.2722 0.4277 0.2050 0.3566 0.6622 0.7858 0.7534 0.3855 0.5707 0.5889 0.5902 0.4979 0.4268 0.6260 0.2735 0.4630
0.4102 32.0 6048 1.0616 0.4043 0.5444 0.6904 0.5769 0.5774 0.5737 0.2844 0.3762 0.3768 0.4424 0.4331 0.2649 0.3959 0.1748 0.3744 0.6629 0.7960 0.7345 0.4132 0.5703 0.5450 0.5855 0.5469 0.4371 0.5087 0.2178 0.5147
0.394 33.0 6237 1.0244 0.4104 0.5587 0.6957 0.6076 0.5755 0.5774 0.2887 0.3833 0.3803 0.4483 0.4329 0.2687 0.4194 0.1884 0.3547 0.7279 0.7642 0.7250 0.4999 0.5330 0.5418 0.6148 0.5491 0.4678 0.5808 0.2548 0.4455
0.3865 34.0 6426 1.0618 0.4086 0.5468 0.6922 0.5729 0.5787 0.5789 0.2853 0.3854 0.3735 0.4469 0.4279 0.2694 0.4240 0.1986 0.3613 0.6571 0.8002 0.7190 0.4516 0.5621 0.5183 0.5822 0.5444 0.3994 0.5931 0.2752 0.4588
0.3816 35.0 6615 1.0515 0.4109 0.5587 0.6937 0.5942 0.5769 0.5777 0.2873 0.3867 0.3811 0.4448 0.4281 0.2669 0.4147 0.1956 0.3774 0.6946 0.7771 0.7289 0.4481 0.5478 0.5396 0.5834 0.5407 0.4980 0.5652 0.2696 0.5116
0.3803 36.0 6804 1.0709 0.4118 0.5507 0.6982 0.6024 0.5819 0.5782 0.2870 0.3850 0.3781 0.4469 0.4259 0.2696 0.4177 0.1885 0.3802 0.7040 0.7881 0.7314 0.4432 0.5429 0.5308 0.5705 0.5124 0.4619 0.5667 0.2465 0.5101
0.3841 37.0 6993 1.0646 0.4102 0.5423 0.7000 0.6099 0.5822 0.5787 0.2920 0.3827 0.3739 0.4416 0.4271 0.2646 0.4200 0.1864 0.3637 0.7277 0.7884 0.7298 0.4325 0.5471 0.5196 0.5523 0.5073 0.4390 0.5614 0.2453 0.4575
0.383 38.0 7182 1.0769 0.4076 0.5463 0.6981 0.6028 0.5823 0.5799 0.2887 0.3828 0.3770 0.4470 0.4238 0.2639 0.4197 0.1617 0.3610 0.7092 0.7907 0.7297 0.4713 0.5626 0.5483 0.5667 0.5067 0.4552 0.5608 0.2002 0.4545
0.3831 39.0 7371 1.0821 0.4081 0.5438 0.6949 0.5856 0.5809 0.5772 0.2889 0.3772 0.3683 0.4493 0.4296 0.2665 0.4112 0.1902 0.3723 0.6763 0.8000 0.7345 0.4678 0.5544 0.5005 0.5818 0.5236 0.4071 0.5436 0.2496 0.4865
0.3701 40.0 7560 1.0971 0.4094 0.5503 0.6939 0.5830 0.5808 0.5785 0.2947 0.3803 0.3832 0.4496 0.4284 0.2675 0.4111 0.1913 0.3644 0.6681 0.8020 0.7232 0.4519 0.5724 0.5465 0.5828 0.5132 0.4686 0.5479 0.2589 0.4678
0.3728 41.0 7749 1.0850 0.4073 0.5426 0.6955 0.5853 0.5827 0.5786 0.2921 0.3809 0.3712 0.4464 0.4330 0.2670 0.4180 0.1631 0.3694 0.6698 0.8022 0.7318 0.4297 0.5493 0.5160 0.5727 0.5289 0.4574 0.5711 0.1978 0.4842
0.3693 42.0 7938 1.0969 0.4065 0.5503 0.6922 0.5756 0.5804 0.5766 0.2872 0.3775 0.3786 0.4480 0.4396 0.2669 0.4132 0.1619 0.3729 0.6542 0.7977 0.7309 0.4450 0.5653 0.5389 0.5874 0.5625 0.4662 0.5561 0.1969 0.5024
0.3627 43.0 8127 1.0932 0.4087 0.5497 0.6948 0.5872 0.5821 0.5762 0.2896 0.3820 0.3742 0.4499 0.4346 0.2685 0.4164 0.1848 0.3597 0.6732 0.7995 0.7126 0.4343 0.5636 0.5217 0.5952 0.5608 0.4679 0.5672 0.2449 0.4559
0.3707 44.0 8316 1.1095 0.4071 0.5449 0.6950 0.5894 0.5823 0.5774 0.2917 0.3801 0.3754 0.4476 0.4287 0.2635 0.4096 0.1911 0.3478 0.6797 0.8035 0.7234 0.4571 0.5651 0.5352 0.5728 0.5156 0.4591 0.5458 0.2506 0.4307
0.3715 45.0 8505 1.0884 0.4110 0.5481 0.6962 0.5912 0.5809 0.5791 0.2980 0.3817 0.3750 0.4483 0.4349 0.2677 0.4155 0.1909 0.3686 0.6866 0.7923 0.7332 0.4349 0.5523 0.5312 0.5855 0.5314 0.4323 0.5653 0.2488 0.4833
0.3637 46.0 8694 1.0893 0.4116 0.5565 0.6948 0.5922 0.5794 0.5788 0.2952 0.3804 0.3754 0.4487 0.4356 0.2641 0.4159 0.2068 0.3666 0.6868 0.7856 0.7297 0.4426 0.5763 0.5288 0.5846 0.5331 0.4573 0.5724 0.2999 0.4811
0.3581 47.0 8883 1.1164 0.4080 0.5443 0.6938 0.5748 0.5822 0.5779 0.2909 0.3849 0.3751 0.4487 0.4350 0.2687 0.4150 0.1785 0.3643 0.6506 0.8100 0.7248 0.4534 0.5506 0.5230 0.5954 0.5515 0.4251 0.5546 0.2245 0.4677
0.3595 48.0 9072 1.1264 0.4056 0.5374 0.6942 0.5787 0.5823 0.5789 0.2896 0.3819 0.3750 0.4479 0.4224 0.2665 0.4140 0.1723 0.3580 0.6590 0.8106 0.7334 0.4353 0.5542 0.5254 0.5813 0.4869 0.4373 0.5611 0.2135 0.4503
0.3604 49.0 9261 1.0948 0.4104 0.5508 0.6953 0.5878 0.5812 0.5782 0.2930 0.3807 0.3796 0.4482 0.4364 0.2659 0.4139 0.1915 0.3678 0.6790 0.7967 0.7227 0.4477 0.5612 0.5523 0.5861 0.5460 0.4310 0.5518 0.2535 0.4817
0.3541 50.0 9450 1.0863 0.4097 0.5538 0.6951 0.5922 0.5796 0.5785 0.2917 0.3793 0.3797 0.4481 0.4354 0.2647 0.4174 0.1817 0.3681 0.6884 0.7852 0.7323 0.4523 0.5829 0.5516 0.5904 0.5289 0.4518 0.5719 0.2318 0.4783
  • All values in the above chart are rounded to nearest ten-thousandth.

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.12.1
  • Datasets 2.9.0
  • Tokenizers 0.12.1