ecc_segformer_main Open-source Image Segmentation Model - Free Deployment, Accurately Detect Cracking Problems

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Ecc Segformer Main

Developed by rishitunu

An image segmentation model fine-tuned from nvidia/mit-b5 for crack detection tasks

Image Segmentation

Transformers

Open Source License:Other #Crack detection #Building structure analysis #High-precision segmentation

Downloads 15

Release Time : 8/28/2023

Model Overview

This model is an image segmentation model based on the SegFormer architecture, specifically designed for engineering crack detection. It demonstrates good crack recognition capabilities on evaluation datasets.

Model Features

Crack detection capability

Optimized specifically for engineering crack detection tasks, achieving an Intersection over Union (IoU) of 0.4658 on evaluation datasets

SegFormer architecture

Based on the efficient SegFormer architecture, using mit-b5 as the backbone

Model Capabilities

Image segmentation

Crack detection

Engineering structural health monitoring

Use Cases

Infrastructure inspection

Concrete structure crack detection

Used to detect cracks in concrete structures such as buildings and bridges

Crack detection accuracy of 0.4658

🚀 ecc_segformer_main

This model is a fine - tuned version of nvidia/mit-b5 on the rishitunu/ecc_crackdetector_dataset_main dataset. It's designed for image segmentation tasks, offering valuable results in crack detection within the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of nvidia/mit-b5 on the rishitunu/ecc_crackdetector_dataset_main dataset. It achieves the following results on the evaluation set:

Loss: 0.1918
Mean Iou: 0.2329
Mean Accuracy: 0.4658
Overall Accuracy: 0.4658
Accuracy Background: nan
Accuracy Crack: 0.4658
Iou Background: 0.0
Iou Crack: 0.4658

✨ Features

Fine - Tuned Model: Based on nvidia/mit-b5, fine - tuned on a specific dataset for better performance in image segmentation.
Comprehensive Evaluation Metrics: Provides multiple evaluation metrics such as loss, mean Iou, and various accuracy measures.

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e - 05
train_batch_size: 2
eval_batch_size: 2
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: polynomial
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crack	Iou Crack
0.1069	1.0	172	0.1376	0.1660	0.3320	0.3320	nan	0.3320	0.3320
0.0682	2.0	344	0.1327	0.2298	0.4596	0.4596	nan	0.4596	0.4596
0.0666	3.0	516	0.2478	0.1200	0.2401	0.2401	nan	0.2401	0.2401
0.0639	4.0	688	0.1732	0.1538	0.3076	0.3076	nan	0.3076	0.3076
0.0624	5.0	860	0.1027	0.2334	0.4668	0.4668	nan	0.4668	0.4668
0.0557	6.0	1032	0.1003	0.1851	0.3703	0.3703	nan	0.3703	0.3703
0.0563	7.0	1204	0.1512	0.2007	0.4014	0.4014	nan	0.4014	0.4014
0.054	8.0	1376	0.1000	0.2401	0.4802	0.4802	nan	0.4802	0.4802
0.0546	9.0	1548	0.0933	0.2238	0.4475	0.4475	nan	0.4475	0.4475
0.0498	10.0	1720	0.0964	0.2303	0.4606	0.4606	nan	0.4606	0.4606
0.0515	11.0	1892	0.1107	0.2258	0.4516	0.4516	nan	0.4516	0.4516
0.0453	12.0	2064	0.0961	0.2557	0.5115	0.5115	nan	0.5115	0.5115
0.0431	13.0	2236	0.1027	0.2396	0.4792	0.4792	nan	0.4792	0.4792
0.0418	14.0	2408	0.1027	0.2521	0.5042	0.5042	nan	0.5042	0.5042
0.0426	15.0	2580	0.1059	0.2561	0.5123	0.5123	nan	0.5123	0.5123
0.0377	16.0	2752	0.1193	0.2281	0.4561	0.4561	nan	0.4561	0.4561
0.0369	17.0	2924	0.1161	0.2486	0.4972	0.4972	nan	0.4972	0.4972
0.036	18.0	3096	0.1058	0.2515	0.5029	0.5029	nan	0.5029	0.5029
0.034	19.0	3268	0.1176	0.2434	0.4868	0.4868	nan	0.4868	0.4868
0.0337	20.0	3440	0.1162	0.2254	0.4509	0.4509	nan	0.4509	0.4509
0.0281	21.0	3612	0.1203	0.2213	0.4426	0.4426	nan	0.4426	0.4426
0.0354	22.0	3784	0.1266	0.2384	0.4768	0.4768	nan	0.4768	0.4768
0.0323	23.0	3956	0.1223	0.2409	0.4818	0.4818	nan	0.4818	0.4818
0.0299	24.0	4128	0.1356	0.2195	0.4390	0.4390	nan	0.4390	0.4390
0.0294	25.0	4300	0.1285	0.2318	0.4636	0.4636	nan	0.4636	0.4636
0.0295	26.0	4472	0.1274	0.2559	0.5119	0.5119	nan	0.5119	0.5119
0.0252	27.0	4644	0.1387	0.2413	0.4827	0.4827	nan	0.4827	0.4827
0.029	28.0	4816	0.1468	0.2236	0.4472	0.4472	nan	0.4472	0.4472
0.0218	29.0	4988	0.1448	0.2433	0.4866	0.4866	nan	0.4866	0.4866
0.0275	30.0	5160	0.1478	0.2318	0.4635	0.4635	nan	0.4635	0.4635
0.0233	31.0	5332	0.1377	0.2502	0.5005	0.5005	nan	0.5005	0.5005
0.0252	32.0	5504	0.1458	0.2399	0.4797	0.4797	nan	0.4797	0.4797
0.0245	33.0	5676	0.1431	0.2480	0.4960	0.4960	nan	0.4960	0.4960
0.0225	34.0	5848	0.1562	0.2439	0.4879	0.4879	nan	0.4879	0.4879
0.0242	35.0	6020	0.1633	0.2323	0.4646	0.4646	nan	0.4646	0.4646
0.0213	36.0	6192	0.1666	0.2274	0.4549	0.4549	nan	0.4549	0.4549
0.0256	37.0	6364	0.1665	0.2340	0.4680	0.4680	nan	0.4680	0.4680
0.0237	38.0	6536	0.1658	0.2410	0.4819	0.4819	nan	0.4819	0.4819
0.0192	39.0	6708	0.1705	0.2286	0.4572	0.4572	nan	0.4572	0.4572
0.0198	40.0	6880	0.1688	0.2322	0.4644	0.4644	nan	0.4644	0.4644
0.0214	41.0	7052	0.1717	0.2315	0.4630	0.4630	nan	0.4630	0.4630
0.0197	42.0	7224	0.1764	0.2338	0.4677	0.4677	nan	0.4677	0.4677
0.0187	43.0	7396	0.1764	0.2437	0.4874	0.4874	nan	0.4874	0.4874
0.0212	44.0	7568	0.1874	0.2259	0.4519	0.4519	nan	0.4519	0.4519
0.0188	45.0	7740	0.1854	0.2362	0.4725	0.4725	nan	0.4725	0.4725
0.0188	46.0	7912	0.1772	0.2320	0.4641	0.4641	nan	0.4641	0.4641
0.0228	47.0	8084	0.1783	0.2385	0.4770	0.4770	nan	0.4770	0.4770
0.0199	48.0	8256	0.1850	0.2317	0.4634	0.4634	nan	0.4634	0.4634
0.0202	49.0	8428	0.1872	0.2336	0.4672	0.4672	nan	0.4672	0.4672
0.0181	50.0	8600	0.1803	0.2405	0.4810	0.4810	nan	0.4810	0.4810
0.0157	51.0	8772	0.1874	0.2349	0.4697	0.4697	nan	0.4697	0.4697
0.0162	52.0	8944	0.1889	0.2332	0.4665	0.4665	nan	0.4665	0.4665
0.0178	53.0	9116	0.1948	0.2357	0.4715	0.4715	nan	0.4715	0.4715
0.0166	54.0	9288	0.1911	0.2333	0.4666	0.4666	nan	0.4666	0.4666
0.0193	55.0	9460	0.1959	0.2306	0.4611	0.4611	nan	0.4611	0.4611
0.0199	56.0	9632	0.1999	0.2330	0.4659	0.4659	nan	0.4659	0.4659
0.0177	57.0	9804	0.1943	0.2319	0.4639	0.4639	nan	0.4639	0.4639
0.019	58.0	9976	0.1926	0.2327	0.4653	0.4653	nan	0.4653	0.4653
0.0187	58.14	10000	0.1918	0.2329	0.4658	0.4658	nan	0.4658	0.4658

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cpu
Datasets 2.14.4
Tokenizers 0.13.3

📄 License

other

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