
# mit-b0-CMP_semantic_seg_with_mps_v2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/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

#### Overall Dataset Metrics

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|
| 1.6807        | 1.0   | 189  | 1.3310          | 0.2226   | 0.3388        | 0.5893           |
| 1.1837        | 2.0   | 378  | 1.1731          | 0.2602   | 0.3876        | 0.6122           |
| 1.0241        | 3.0   | 567  | 1.0485          | 0.2915   | 0.3954        | 0.6393           |
| 0.9353        | 4.0   | 756  | 0.9943          | 0.3054   | 0.4021        | 0.6570           |
| 0.8717        | 5.0   | 945  | 1.0010          | 0.3299   | 0.4440        | 0.6530           |
| 0.8238        | 6.0   | 1134 | 0.9537          | 0.3546   | 0.4771        | 0.6701           |
| 0.7415        | 8.0   | 1512 | 0.9738          | 0.3554   | 0.4634        | 0.6733           |
| 0.7708        | 7.0   | 1323 | 0.9789          | 0.3550   | 0.4837        | 0.6683           |
| 0.7018        | 9.0   | 1701 | 0.9449          | 0.3667   | 0.4802        | 0.6826           |
| 0.682         | 10.0  | 1890 | 0.9422          | 0.3762   | 0.5047        | 0.6805           |
| 0.6503        | 11.0  | 2079 | 0.9889          | 0.3785   | 0.5082        | 0.6729           |
| 0.633         | 12.0  | 2268 | 0.9594          | 0.3901   | 0.5224        | 0.6797           |
| 0.6035        | 13.0  | 2457 | 0.9612          | 0.3939   | 0.5288        | 0.6834           |
| 0.5874        | 14.0  | 2646 | 0.9657          | 0.3939   | 0.5383        | 0.6844           |
| 0.5684        | 15.0  | 2835 | 0.9762          | 0.3950   | 0.5446        | 0.6855           |
| 0.5485        | 16.0  | 3024 | 1.0645          | 0.3794   | 0.5095        | 0.6704           |
| 0.5402        | 17.0  | 3213 | 0.9747          | 0.4044   | 0.5600        | 0.6839           |
| 0.5275        | 18.0  | 3402 | 1.0054          | 0.3944   | 0.5411        | 0.6790           |
| 0.5032        | 19.0  | 3591 | 1.0014          | 0.3973   | 0.5256        | 0.6875           |
| 0.4985        | 20.0  | 3780 | 0.9893          | 0.3990   | 0.5468        | 0.6883           |
| 0.4925        | 21.0  | 3969 | 1.0416          | 0.3955   | 0.5339        | 0.6806           |
| 0.4772        | 22.0  | 4158 | 1.0142          | 0.3969   | 0.5476        | 0.6838           |
| 0.4707        | 23.0  | 4347 | 0.9896          | 0.4077   | 0.5458        | 0.6966           |
| 0.4601        | 24.0  | 4536 | 1.0040          | 0.4104   | 0.5551        | 0.6948           |
| 0.4544        | 25.0  | 4725 | 1.0093          | 0.4093   | 0.5652        | 0.6899           |
| 0.4421        | 26.0  | 4914 | 1.0434          | 0.4064   | 0.5448        | 0.6938           |
| 0.4293        | 27.0  | 5103 | 1.0391          | 0.4076   | 0.5571        | 0.6908           |
| 0.4312        | 28.0  | 5292 | 1.0037          | 0.4100   | 0.5534        | 0.6958           |
| 0.4309        | 29.0  | 5481 | 1.0288          | 0.4101   | 0.5493        | 0.6968           |
| 0.4146        | 30.0  | 5670 | 1.0602          | 0.4062   | 0.5445        | 0.6928           |
| 0.4106        | 31.0  | 5859 | 1.0573          | 0.4113   | 0.5520        | 0.6937           |
| 0.4102        | 32.0  | 6048 | 1.0616          | 0.4043   | 0.5444        | 0.6904           |
| 0.394         | 33.0  | 6237 | 1.0244          | 0.4104   | 0.5587        | 0.6957           |
| 0.3865        | 34.0  | 6426 | 1.0618          | 0.4086   | 0.5468        | 0.6922           |
| 0.3816        | 35.0  | 6615 | 1.0515          | 0.4109   | 0.5587        | 0.6937           |
| 0.3803        | 36.0  | 6804 | 1.0709          | 0.4118   | 0.5507        | 0.6982           |
| 0.3841        | 37.0  | 6993 | 1.0646          | 0.4102   | 0.5423        | 0.7000           |
| 0.383         | 38.0  | 7182 | 1.0769          | 0.4076   | 0.5463        | 0.6981           |
| 0.3831        | 39.0  | 7371 | 1.0821          | 0.4081   | 0.5438        | 0.6949           |
| 0.3701        | 40.0  | 7560 | 1.0971          | 0.4094   | 0.5503        | 0.6939           |
| 0.3728        | 41.0  | 7749 | 1.0850          | 0.4073   | 0.5426        | 0.6955           |
| 0.3693        | 42.0  | 7938 | 1.0969          | 0.4065   | 0.5503        | 0.6922           |
| 0.3627        | 43.0  | 8127 | 1.0932          | 0.4087   | 0.5497        | 0.6948           |
| 0.3707        | 44.0  | 8316 | 1.1095          | 0.4071   | 0.5449        | 0.6950           |
| 0.3715        | 45.0  | 8505 | 1.0884          | 0.4110   | 0.5481        | 0.6962           |
| 0.3637        | 46.0  | 8694 | 1.0893          | 0.4116   | 0.5565        | 0.6948           |
| 0.3581        | 47.0  | 8883 | 1.1164          | 0.4080   | 0.5443        | 0.6938           |
| 0.3595        | 48.0  | 9072 | 1.1264          | 0.4056   | 0.5374        | 0.6942           |
| 0.3604        | 49.0  | 9261 | 1.0948          | 0.4104   | 0.5508        | 0.6953           |
| 0.3541        | 50.0  | 9450 | 1.0863          | 0.4097   | 0.5538        | 0.6951           |

#### Per Category IoU For Each Segment

| Epoch | Segment 0 | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 | Segment 6 | Segment 7 | Segment 8 | Segment 9 | Segment 10 | Segment 11 |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 1.0   | 0.4635 | 0.4905 | 0.4698 | 0.0 | 0.2307 | 0.1515 | 0.2789 | 0.0002 | 0.0250 | 0.3527 | 0.0 | 0.2087 |
| 2.0   | 0.4240 | 0.5249 | 0.5152 | 0.0057 | 0.2636 | 0.2756 | 0.3312 | 0.0575 | 0.0539 | 0.3860 | 0.0 | 0.2854 |
| 3.0   | 0.5442 | 0.5037 | 0.5329 | 0.0412 | 0.3062 | 0.2714 | 0.3820 | 0.1430 | 0.0796 | 0.4007 | 0.0002 | 0.2929 |
| 4.0   | 0.5776 | 0.5289 | 0.5391 | 0.1171 | 0.3137 | 0.2600 | 0.3664 | 0.1527 | 0.1074 | 0.3935 | 0.0002 | 0.3078 |
| 5.0   | 0.4790 | 0.5506 | 0.5472 | 0.1547 | 0.3372 | 0.3297 | 0.4151 | 0.2339 | 0.1709 | 0.4081 | 0.0008 | 0.3314 |
| 6.0   | 0.5572 | 0.5525 | 0.5611 | 0.2076 | 0.3434 | 0.3163 | 0.4103 | 0.3279 | 0.2107 | 0.4191 | 0.0067 | 0.3418 |
| 7.0   | 0.5310 | 0.5634 | 0.5594 | 0.2299 | 0.3424 | 0.3375 | 0.4050 | 0.2883 | 0.2197 | 0.4142 | 0.0316 | 0.3373 |
| 8.0   | 0.5366 | 0.5659 | 0.5550 | 0.2331 | 0.3497 | 0.3334 | 0.4301 | 0.3401 | 0.1989 | 0.4181 | 0.0358 | 0.2680 |
| 9.0   | 0.5798 | 0.5657 | 0.5624 | 0.2368 | 0.3648 | 0.3271 | 0.4250 | 0.3207 | 0.2096 | 0.4236 | 0.0504 | 0.3346 |
| 10.0  | 0.5802 | 0.5622 | 0.5585 | 0.2340 | 0.3793 | 0.3407 | 0.4277 | 0.3801 | 0.2301 | 0.4216 | 0.0640 | 0.3367 |
| 11.0  | 0.5193 | 0.5649 | 0.5605 | 0.2698 | 0.3772 | 0.3526 | 0.4342 | 0.3433 | 0.2415 | 0.4336 | 0.0889 | 0.3562 |
| 12.0  | 0.5539 | 0.5641 | 0.5679 | 0.2658 | 0.3757 | 0.3510 | 0.4257 | 0.3993 | 0.2354 | 0.4338 | 0.1800 | 0.3287 |
| 13.0  | 0.5663 | 0.5666 | 0.5679 | 0.2631 | 0.3726 | 0.3609 | 0.4351 | 0.3759 | 0.2511 | 0.4256 | 0.1737 | 0.3681 |
| 14.0  | 0.5807 | 0.5670 | 0.5679 | 0.2670 | 0.3594 | 0.3605 | 0.4393 | 0.3863 | 0.2406 | 0.4228 | 0.1705 | 0.3652 |
| 15.0  | 0.5800 | 0.5711 | 0.5671 | 0.2825 | 0.3664 | 0.3587 | 0.4408 | 0.4021 | 0.2540 | 0.4246 | 0.1376 | 0.3548 |
| 16.0  | 0.4855 | 0.5683 | 0.5685 | 0.2612 | 0.3832 | 0.3628 | 0.4378 | 0.4056 | 0.2525 | 0.4206 | 0.1242 | 0.2825 |
| 17.0  | 0.5697 | 0.5674 | 0.5687 | 0.2971 | 0.3767 | 0.3741 | 0.4486 | 0.4126 | 0.2489 | 0.4260 | 0.1874 | 0.3757 |
| 18.0  | 0.5341 | 0.5728 | 0.5616 | 0.2827 | 0.3823 | 0.3782 | 0.4298 | 0.4070 | 0.2578 | 0.4195 | 0.1448 | 0.3632 |
| 19.0  | 0.5696 | 0.5739 | 0.5699 | 0.2918 | 0.3717 | 0.3635 | 0.4444 | 0.4122 | 0.2531 | 0.4142 | 0.1659 | 0.3369 |
| 20.0  | 0.5937 | 0.5702 | 0.5630 | 0.2892 | 0.3790 | 0.3757 | 0.4383 | 0.4110 | 0.2592 | 0.4147 | 0.1291 | 0.3653 |
| 21.0  | 0.5336 | 0.5723 | 0.5732 | 0.2843 | 0.3748 | 0.3738 | 0.4383 | 0.3876 | 0.2598 | 0.4170 | 0.1693 | 0.3624 |
| 22.0  | 0.5634 | 0.5752 | 0.5595 | 0.2783 | 0.3833 | 0.3540 | 0.4448 | 0.4054 | 0.2586 | 0.4145 | 0.1597 | 0.3660 |
| 23.0  | 0.6013 | 0.5801 | 0.5794 | 0.2988 | 0.3816 | 0.3736 | 0.4464 | 0.4241 | 0.2633 | 0.4162 | 0.1747 | 0.3530 |
| 24.0  | 0.6061 | 0.5756 | 0.5721 | 0.3086 | 0.3771 | 0.3707 | 0.4459 | 0.4242 | 0.2665 | 0.4104 | 0.1942 | 0.3732 |
| 25.0  | 0.5826 | 0.5745 | 0.5742 | 0.3109 | 0.3765 | 0.3784 | 0.4441 | 0.4184 | 0.2609 | 0.4219 | 0.1930 | 0.3765 |
| 26.0  | 0.5783 | 0.5821 | 0.5770 | 0.2985 | 0.3885 | 0.3582 | 0.4458 | 0.4220 | 0.2717 | 0.4260 | 0.1690 | 0.3600 |
| 27.0  | 0.5764 | 0.5777 | 0.5749 | 0.2868 | 0.3824 | 0.3857 | 0.4450 | 0.4170 | 0.2644 | 0.4295 | 0.1922 | - |
| 28.0  | 0.6023 | 0.5776 | 0.5769 | 0.2964 | 0.3759 | 0.3758 | 0.4464 | 0.4245 | 0.2712 | 0.4083 | 0.1967 | 0.3680 |
| 29.0  | 0.6043 | 0.5814 | 0.5728 | 0.2882 | 0.3867 | 0.3841 | 0.4369 | 0.4254 | 0.2659 | 0.4252 | 0.2106 | 0.3391 |
| 30.0  | 0.5840 | 0.5792 | 0.5750 | 0.2859 | 0.3839 | 0.3786 | 0.4479 | 0.4259 | 0.2664 | 0.3947 | 0.1753 | 0.3780 |
| 31.0  | 0.5819 | 0.5787 | 0.5775 | 0.2882 | 0.3861 | 0.3888 | 0.4522 | 0.4207 | 0.2722 | 0.4277 | 0.2050 | 0.3566 |
| 32.0  | 0.5769 | 0.5774 | 0.5737 | 0.2844 | 0.3762 | 0.3768 | 0.4424 | 0.4331 | 0.2649 | 0.3959 | 0.1748 | 0.3744 |
| 33.0  | 0.6076 | 0.5755 | 0.5774 | 0.2887 | 0.3833 | 0.3803 | 0.4483 | 0.4329 | 0.2687 | 0.4194 | 0.1884 | 0.3547 |
| 34.0  | 0.5729 | 0.5787 | 0.5789 | 0.2853 | 0.3854 | 0.3735 | 0.4469 | 0.4279 | 0.2694 | 0.4240 | 0.1986 | 0.3613 |
| 35.0  | 0.5942 | 0.5769 | 0.5777 | 0.2873 | 0.3867 | 0.3811 | 0.4448 | 0.4281 | 0.2669 | 0.4147 | 0.1956 | 0.3774 |
| 36.0  | 0.6024 | 0.5819 | 0.5782 | 0.2870 | 0.3850 | 0.3781 | 0.4469 | 0.4259 | 0.2696 | 0.4177 | 0.1885 | 0.3802 |
| 37.0  | 0.6099 | 0.5822 | 0.5787 | 0.2920 | 0.3827 | 0.3739 | 0.4416 | 0.4271 | 0.2646 | 0.4200 | 0.1864 | 0.3637 |
| 38.0  | 0.6028 | 0.5823 | 0.5799 | 0.2887 | 0.3828 | 0.3770 | 0.4470 | 0.4238 | 0.2639 | 0.4197 | 0.1617 | 0.3610 |
| 39.0  | 0.5856 | 0.5809 | 0.5772 | 0.2889 | 0.3772 | 0.3683 | 0.4493 | 0.4296 | 0.2665 | 0.4112 | 0.1902 | 0.3723 |
| 40.0  | 0.5830 | 0.5808 | 0.5785 | 0.2947 | 0.3803 | 0.3832 | 0.4496 | 0.4284 | 0.2675 | 0.4111 | 0.1913 | 0.3644 |
| 41.0  | 0.5853 | 0.5827 | 0.5786 | 0.2921 | 0.3809 | 0.3712 | 0.4464 | 0.4330 | 0.2670 | 0.4180 | 0.1631 | 0.3694 |
| 42.0  | 0.5756 | 0.5804 | 0.5766 | 0.2872 | 0.3775 | 0.3786 | 0.4480 | 0.4396 | 0.2669 | 0.4132 | 0.1619 | 0.3729 |
| 43.0  | 0.5872 | 0.5821 | 0.5762 | 0.2896 | 0.3820 | 0.3742 | 0.4499 | 0.4346 | 0.2685 | 0.4164 | 0.1848 | 0.3597 |
| 44.0  | 0.5894 | 0.5823 | 0.5774 | 0.2917 | 0.3801 | 0.3754 | 0.4476 | 0.4287 | 0.2635 | 0.4096 | 0.1911 | 0.3478 |
| 45.0  | 0.5912 | 0.5809 | 0.5791 | 0.2980 | 0.3817 | 0.3750 | 0.4483 | 0.4349 | 0.2677 | 0.4155 | 0.1909 | 0.3686 |
| 46.0  | 0.5922 | 0.5794 | 0.5788 | 0.2952 | 0.3804 | 0.3754 | 0.4487 | 0.4356 | 0.2641 | 0.4159 | 0.2068 | 0.3666 |
| 47.0  | 0.5748 | 0.5822 | 0.5779 | 0.2909 | 0.3849 | 0.3751 | 0.4487 | 0.4350 | 0.2687 | 0.4150 | 0.1785 | 0.3643 |
| 48.0  | 0.5787 | 0.5823 | 0.5789 | 0.2896 | 0.3819 | 0.3750 | 0.4479 | 0.4224 | 0.2665 | 0.4140 | 0.1723 | 0.3580 |
| 49.0  | 0.5878 | 0.5812 | 0.5782 | 0.2930 | 0.3807 | 0.3796 | 0.4482 | 0.4364 | 0.2659 | 0.4139 | 0.1915 | 0.3678 |
| 50.0  | 0.5922 | 0.5796 | 0.5785 | 0.2917 | 0.3793 | 0.3797 | 0.4481 | 0.4354 | 0.2647 | 0.4174 | 0.1817 | 0.3681 |

#### Per Category Accuracy For Each Segment

| Epoch | Segment 0 | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 | Segment 6 | Segment 7 | Segment 8 | Segment 9 | Segment 10 | Segment 11 |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 1.0   | 0.6133 | 0.6847 | 0.7408 | 0.0 | 0.4973 | 0.1720 | 0.4073 | 0.0002 | 0.0255 | 0.6371 | 0.0 | 0.2874 |
| 2.0   | 0.4782 | 0.7844 | 0.6966 | 0.0057 | 0.5735 | 0.3684 | 0.6226 | 0.0577 | 0.0563 | 0.5907 | 0.0 | 0.4168 |
| 3.0   | 0.8126 | 0.6852 | 0.6683 | 0.0420 | 0.4972 | 0.3418 | 0.5121 | 0.1453 | 0.0849 | 0.5882 | 0.0002 | 0.3672 |
| 4.0   | 0.8079 | 0.7362 | 0.6803 | 0.1231 | 0.5129 | 0.3324 | 0.4212 | 0.1554 | 0.1223 | 0.5587 | 0.0002 | 0.3751 |
| 5.0   | 0.5408 | 0.8111 | 0.7439 | 0.1647 | 0.5336 | 0.4720 | 0.5650 | 0.2459 | 0.2127 | 0.6032 | 0.0008 | 0.4343 |
| 6.0   | 0.6870 | 0.7532 | 0.7389 | 0.2428 | 0.5081 | 0.4173 | 0.5923 | 0.3710 | 0.3117 | 0.6181 | 0.0068 | 0.4785 |
| 7.0   | 0.6050 | 0.7961 | 0.7434 | 0.2876 | 0.5835 | 0.4949 | 0.5608 | 0.3103 | 0.3672 | 0.6185 | 0.0345 | 0.4022 |
| 8.0   | 0.6081 | 0.8461 | 0.6598 | 0.3035 | 0.5720 | 0.4540 | 0.5735 | 0.3849 | 0.2642 | 0.5608 | 0.0379 | 0.2962 |
| 9.0   | 0.7241 | 0.7684 | 0.7677 | 0.2958 | 0.5321 | 0.4212 | 0.5547 | 0.3513 | 0.2813 | 0.5645 | 0.0544 | 0.4465 |
| 10.0  | 0.7124 | 0.7649 | 0.7024 | 0.2879 | 0.5535 | 0.4413 | 0.6310 | 0.4960 | 0.3982 | 0.5592 | 0.0724 | 0.4370 |
| 11.0  | 0.5876 | 0.8060 | 0.7296 | 0.3838 | 0.5267 | 0.4983 | 0.5902 | 0.3838 | 0.4151 | 0.5987 | 0.1030 | 0.4756 |
| 12.0  | 0.6497 | 0.7807 | 0.7448 | 0.4018 | 0.5381 | 0.4615 | 0.5849 | 0.4883 | 0.3248 | 0.6063 | 0.2918 | 0.3958 |
| 13.0  | 0.6650 | 0.7792 | 0.7595 | 0.4049 | 0.5501 | 0.4940 | 0.5831 | 0.4375 | 0.3843 | 0.5591 | 0.2578 | 0.4711 |
| 14.0  | 0.6881 | 0.7715 | 0.7076 | 0.4518 | 0.6011 | 0.4900 | 0.6235 | 0.4466 | 0.3627 | 0.5934 | 0.2537 | 0.4702 |
| 15.0  | 0.6690 | 0.7721 | 0.7253 | 0.4607 | 0.6286 | 0.4900 | 0.5936 | 0.4951 | 0.4337 | 0.6295 | 0.1749 | 0.4630 |
| 16.0  | 0.5250 | 0.8335 | 0.7460 | 0.3742 | 0.6114 | 0.4823 | 0.5880 | 0.5021 | 0.4084 | 0.5757 | 0.1498 | 0.3171 |
| 17.0  | 0.6652 | 0.7673 | 0.7058 | 0.4318 | 0.5995 | 0.5137 | 0.6112 | 0.5596 | 0.4548 | 0.5819 | 0.2821 | 0.5465 |
| 18.0  | 0.6012 | 0.8091 | 0.6765 | 0.4561 | 0.5707 | 0.5393 | 0.6255 | 0.5679 | 0.4347 | 0.5567 | 0.1806 | 0.4751 |
| 19.0  | 0.6634 | 0.8079 | 0.6986 | 0.4389 | 0.5274 | 0.4876 | 0.6232 | 0.5022 | 0.3717 | 0.5244 | 0.2232 | 0.4388 |
| 20.0  | 0.7110 | 0.7679 | 0.6952 | 0.4875 | 0.5261 | 0.5549 | 0.6444 | 0.5301 | 0.4512 | 0.5441 | 0.1603 | 0.4888 |
| 21.0  | 0.5945 | 0.8130 | 0.7299 | 0.4511 | 0.5922 | 0.5324 | 0.5643 | 0.4341 | 0.4067 | 0.5834 | 0.2272 | 0.4781 |
| 22.0  | 0.6478 | 0.7921 | 0.6887 | 0.4826 | 0.5784 | 0.4599 | 0.6029 | 0.5938 | 0.4905 | 0.5605 | 0.2094 | 0.4644 |
| 23.0  | 0.7110 | 0.7878 | 0.7192 | 0.4629 | 0.5670 | 0.5061 | 0.5891 | 0.5354 | 0.4442 | 0.5585 | 0.2280 | 0.4401 |
| 24.0  | 0.7277 | 0.7718 | 0.7095 | 0.4789 | 0.5401 | 0.5080 | 0.6040 | 0.5314 | 0.4573 | 0.5414 | 0.2853 | 0.5062 |
| 25.0  | 0.6781 | 0.7703 | 0.7305 | 0.5102 | 0.5954 | 0.5311 | 0.5960 | 0.5286 | 0.4647 | 0.5861 | 0.2676 | 0.5242 |
| 26.0  | 0.6603 | 0.7989 | 0.7349 | 0.4689 | 0.5677 | 0.4620 | 0.6111 | 0.5258 | 0.4556 | 0.5889 | 0.2110 | 0.4530 |
| 27.0  | - | - | - | - | - | - | - | - | - | - | - | - |
| 28.0  | 0.7218 | 0.7735 | 0.7273 | 0.4297 | 0.6001 | 0.5321 | - | - | - | - | - | - |
| 29.0  | 0.7054 | 0.7948 | 0.7009 | 0.4552 | 0.5413 | 0.5357 | 0.5421 | 0.5250 | 0.4701 | 0.5949 | 0.3048 | 0.4213 |
| 30.0  | 0.6744 | 0.8004 | 0.7289 | 0.4421 | 0.5410 | 0.5409 | 0.5822 | 0.5334 | 0.4790 | 0.5028 | 0.2177 | 0.4910 |
| 31.0  | 0.6622 | 0.7858 | 0.7534 | 0.3855 | 0.5707 | 0.5889 | 0.5902 | 0.4979 | 0.4268 | 0.6260 | 0.2735 | 0.4630 |
| 32.0  | 0.6629 | 0.7960 | 0.7345 | 0.4132 | 0.5703 | 0.5450 | 0.5855 | 0.5469 | 0.4371 | 0.5087 | 0.2178 | 0.5147 |
| 33.0  | 0.7279 | 0.7642 | 0.7250 | 0.4999 | 0.5330 | 0.5418 | 0.6148 | 0.5491 | 0.4678 | 0.5808 | 0.2548 | 0.4455 |
| 34.0  | 0.6571 | 0.8002 | 0.7190 | 0.4516 | 0.5621 | 0.5183 | 0.5822 | 0.5444 | 0.3994 | 0.5931 | 0.2752 | 0.4588 |
| 35.0  | 0.6946 | 0.7771 | 0.7289 | 0.4481 | 0.5478 | 0.5396 | 0.5834 | 0.5407 | 0.4980 | 0.5652 | 0.2696 | 0.5116 |
| 36.0  | 0.7040 | 0.7881 | 0.7314 | 0.4432 | 0.5429 | 0.5308 | 0.5705 | 0.5124 | 0.4619 | 0.5667 | 0.2465 | 0.5101 |
| 37.0  | 0.7277 | 0.7884 | 0.7298 | 0.4325 | 0.5471 | 0.5196 | 0.5523 | 0.5073 | 0.4390 | 0.5614 | 0.2453 | 0.4575 |
| 38.0  | 0.7092 | 0.7907 | 0.7297 | 0.4713 | 0.5626 | 0.5483 | 0.5667 | 0.5067 | 0.4552 | 0.5608 | 0.2002 | 0.4545 |
| 39.0  | 0.6763 | 0.8000 | 0.7345 | 0.4678 | 0.5544 | 0.5005 | 0.5818 | 0.5236 | 0.4071 | 0.5436 | 0.2496 | 0.4865 |
| 40.0  | 0.6681 | 0.8020 | 0.7232 | 0.4519 | 0.5724 | 0.5465 | 0.5828 | 0.5132 | 0.4686 | 0.5479 | 0.2589 | 0.4678 |
| 41.0  | 0.6698 | 0.8022 | 0.7318 | 0.4297 | 0.5493 | 0.5160 | 0.5727 | 0.5289 | 0.4574 | 0.5711 | 0.1978 | 0.4842 |
| 42.0  | 0.6542 | 0.7977 | 0.7309 | 0.4450 | 0.5653 | 0.5389 | 0.5874 | 0.5625 | 0.4662 | 0.5561 | 0.1969 | 0.5024 |
| 43.0  | 0.6732 | 0.7995 | 0.7126 | 0.4343 | 0.5636 | 0.5217 | 0.5952 | 0.5608 | 0.4679 | 0.5672 | 0.2449 | 0.4559 |
| 44.0  | 0.6797 | 0.8035 | 0.7234 | 0.4571 | 0.5651 | 0.5352 | 0.5728 | 0.5156 | 0.4591 | 0.5458 | 0.2506 | 0.4307 |
| 45.0  | 0.6866 | 0.7923 | 0.7332 | 0.4349 | 0.5523 | 0.5312 | 0.5855 | 0.5314 | 0.4323 | 0.5653 | 0.2488 | 0.4833 |
| 46.0  | 0.6868 | 0.7856 | 0.7297 | 0.4426 | 0.5763 | 0.5288 | 0.5846 | 0.5331 | 0.4573 | 0.5724 | 0.2999 | 0.4811 |
| 47.0  | 0.6506 | 0.8100 | 0.7248 | 0.4534 | 0.5506 | 0.5230 | 0.5954 | 0.5515 | 0.4251 | 0.5546 | 0.2245 | 0.4677 |
| 48.0  | 0.6590 | 0.8106 | 0.7334 | 0.4353 | 0.5542 | 0.5254 | 0.5813 | 0.4869 | 0.4373 | 0.5611 | 0.2135 | 0.4503 |
| 49.0  | 0.6790 | 0.7967 | 0.7227 | 0.4477 | 0.5612 | 0.5523 | 0.5861 | 0.5460 | 0.4310 | 0.5518 | 0.2535 | 0.4817 |
| 50.0  | 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 charts are rounded to nearest ten-thousandth.
 
### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "other", "tags": ["computer-vision", "generated_from_trainer"], "datasets": ["Xpitfire/cmp_facade"], "metrics": ["mean_iou"], "pipeline_tag": "image-segmentation", "model-index": [{"name": "mit-b0-CMP_semantic_seg_with_mps_v2", "results": []}]}
transformers pytorch tensorboard segformer computer-vision generated_from_trainer image-segmentation en dataset:Xpitfire/cmp_facade license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #computer-vision #generated_from_trainer #image-segmentation #en #dataset-Xpitfire/cmp_facade #license-other #endpoints_compatible #region-us 

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: URL


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: URL


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


#### Overall Dataset Metrics



#### Per Category IoU For Each Segment



#### Per Category Accuracy For Each Segment



* All values in the above charts are rounded to nearest ten-thousandth.


### Framework versions


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



### 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 #### Overall Dataset Metrics #### Per Category IoU For Each Segment #### Per Category Accuracy For Each Segment



* All values in the above charts are rounded to nearest ten-thousandth. ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #segformer #computer-vision #generated_from_trainer #image-segmentation #en #dataset-Xpitfire/cmp_facade #license-other #endpoints_compatible #region-us 
 ### 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 #### Overall Dataset Metrics #### Per Category IoU For Each Segment #### Per Category Accuracy For Each Segment



* All values in the above charts are rounded to nearest ten-thousandth. ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #segformer #computer-vision #generated_from_trainer #image-segmentation #en #dataset-Xpitfire/cmp_facade #license-other #endpoints_compatible #region-us 
### 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#### Overall Dataset Metrics#### Per Category IoU For Each Segment#### Per Category Accuracy For Each Segment



* All values in the above charts are rounded to nearest ten-thousandth.### Framework versions


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