mit-b0-CMP_semantic_seg_with_mps_v2 Open-source Model - Free Deployment for Building Facade Image Segmentation

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Mit B0 CMP Semantic Seg With Mps V2

Developed by DunnBC22

Semantic segmentation model fine-tuned from nvidia/mit-b0 for building facade image segmentation tasks

Image Segmentation

Transformers

EnglishOpen Source License:Other #Building facade segmentation #Multi-category recognition #SegFormer fine-tuning

Downloads 15

Release Time : 2/20/2023

Model Overview

This model is a semantic segmentation model fine-tuned based on the nvidia/mit-b0 architecture, specifically designed for building facade image segmentation tasks, capable of recognizing 12 different building element categories.

Model Features

Building facade element recognition

Accurately identifies 12 different element categories in building facades

Efficient segmentation performance

Achieves a mean Intersection over Union of 0.4097 and overall accuracy of 0.6951 on the evaluation set

Based on MIT-B0 architecture

Utilizes the lightweight yet efficient MIT-B0 architecture for image segmentation

Model Capabilities

Image semantic segmentation

Building element recognition

Computer vision analysis

Use Cases

Architectural digitization

Building facade analysis

Automatically identifies and segments various elements in building facades

Mean Intersection over Union 0.4097, overall accuracy 0.6951

Urban planning

Urban architectural style analysis

Analyzes urban architectural style distribution through building facade segmentation results

🚀 mit-b0-CMP_semantic_seg_with_mps_v2

This model is a fine - tuned version of nvidia/mit-b0, designed for image segmentation tasks. It offers a set of evaluation metrics that demonstrate its performance on the relevant dataset.

🚀 Quick Start

This section provides a high - level overview of the model. For more detailed usage, please refer to the relevant sections below.

✨ Features

Fine - tuned Model: Based on nvidia/mit-b0, it has been fine - tuned to better suit specific tasks.
Rich Evaluation Metrics: Provides multiple evaluation metrics such as loss, mean IoU, mean accuracy, and overall accuracy, as well as per - category IoU and accuracy.

📚 Documentation

Model description

For more information on how it was created, check out the following link: Creation Details

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: Xpitfire/cmp_facade

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Property	Details
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

Evaluation results

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

📄 License

This project uses an "other" license.

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