Open-source ecc_segformerv2 Image Segmentation Model - Accurately Complete Crack Detection Tasks

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Ecc Segformerv2

Developed by rishitunu

An image segmentation model fine-tuned based on nvidia/mit-b5, specialized in crack detection tasks

Image Segmentation

Transformers

Open Source License:Other #Crack Detection #Building Structure Analysis #Image Segmentation

Downloads 18

Release Time : 8/13/2023

Model Overview

This model is an image segmentation model fine-tuned on the rishitunu/ecc_crackdetector_dataset using the nvidia/mit-b5 architecture, primarily used for detecting crack structures in images.

Model Features

Specialized for Crack Detection

Optimized specifically for crack detection tasks, capable of identifying crack structures in images

Based on SegFormer Architecture

Utilizes the efficient SegFormer image segmentation architecture, balancing accuracy and computational efficiency

Model Capabilities

Image Segmentation

Crack Detection

Structural Defect Identification

Use Cases

Infrastructure Inspection

Building Crack Detection

Used to detect crack defects on building surfaces

Achieved an IoU of 0.1723 on the evaluation set for cracks

Road Crack Detection

Identifies cracks and damages on road surfaces

🚀 ecc_segformerv2

This model, ecc_segformerv2, is a fine - tuned version of nvidia/mit-b5 on the rishitunu/ecc_crackdetector_dataset dataset. It offers valuable insights for image segmentation tasks, especially in the field of vision.

🚀 Quick Start

This model is ready for use in image segmentation tasks. You can fine - tune it further on your own dataset or directly use it for inference.

📚 Documentation

Model Information

Property	Details
Model Type	Fine - tuned version of `nvidia/mit-b5`
Training Data	rishitunu/ecc_crackdetector_dataset

Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.3478
Mean Iou: 0.0862
Mean Accuracy: 0.1924
Overall Accuracy: 0.1924
Accuracy Background: nan
Accuracy Crack: 0.1924
Iou Background: 0.0
Iou Crack: 0.1723

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e - 05
train_batch_size: 4
eval_batch_size: 4
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
0.1019	1.0	251	0.5116	0.1490	0.3280	0.3280	nan	0.3280	0.2979
0.0938	2.0	502	0.4725	0.1144	0.2400	0.2400	nan	0.2400	0.2287
0.098	3.0	753	0.5117	0.1276	0.2748	0.2748	nan	0.2748	0.2552
0.1018	4.0	1004	0.3870	0.1053	0.2254	0.2254	nan	0.2254	0.2106
0.0928	5.0	1255	0.2907	0.0772	0.1630	0.1630	nan	0.1630	0.1544
0.0936	6.0	1506	0.5220	0.1193	0.2544	0.2544	nan	0.2544	0.2385
0.077	7.0	1757	0.1608	0.0617	0.1308	0.1308	nan	0.1308	0.1235
0.0963	8.0	2008	0.1756	0.0456	0.0923	0.0923	nan	0.0923	0.0912
0.0958	9.0	2259	0.2027	0.0862	0.1813	0.1813	nan	0.1813	0.1725
0.0755	10.0	2510	0.2327	0.0888	0.1832	0.1832	nan	0.1832	0.1776
0.0632	11.0	2761	0.2169	0.0846	0.1863	0.1863	nan	0.1863	0.1693
0.0638	12.0	3012	0.2309	0.0852	0.1957	0.1957	nan	0.1957	0.1704
0.0509	13.0	3263	0.3209	0.1236	0.2910	0.2910	nan	0.2910	0.2472
0.0497	14.0	3514	0.3274	0.1045	0.2354	0.2354	nan	0.2354	0.2089
0.0396	15.0	3765	0.3415	0.1005	0.2257	0.2257	nan	0.2257	0.2010
0.0373	16.0	4016	0.3530	0.1122	0.2486	0.2486	nan	0.2486	0.2244
0.0388	17.0	4267	0.3312	0.0889	0.1974	0.1974	nan	0.1974	0.1778
0.0346	18.0	4518	0.3061	0.0903	0.2125	0.2125	nan	0.2125	0.1807
0.0296	19.0	4769	0.3223	0.1000	0.2315	0.2315	nan	0.2315	0.2000
0.0311	20.0	5020	0.3458	0.0943	0.2237	0.2237	nan	0.2237	0.1887
0.0303	21.0	5271	0.3283	0.0975	0.2255	0.2255	nan	0.2255	0.1951
0.0249	22.0	5522	0.3387	0.0998	0.2327	0.2327	nan	0.2327	0.1996
0.0298	23.0	5773	0.3332	0.0973	0.2242	0.2242	nan	0.2242	0.1946
0.0239	24.0	6024	0.3778	0.1146	0.2634	0.2634	nan	0.2634	0.2292
0.0238	25.0	6275	0.3250	0.0909	0.2081	0.2081	nan	0.2081	0.1818
0.0242	26.0	6526	0.3826	0.1002	0.2285	0.2285	nan	0.2285	0.2004
0.017	27.0	6777	0.3543	0.1058	0.2367	0.2367	nan	0.2367	0.2115
0.0241	28.0	7028	0.3491	0.0915	0.2069	0.2069	nan	0.2069	0.1830
0.0203	29.0	7279	0.3354	0.0899	0.2056	0.2056	nan	0.2056	0.1798
0.0206	30.0	7530	0.3592	0.0944	0.2165	0.2165	nan	0.2165	0.1888
0.0211	31.0	7781	0.3200	0.0943	0.2100	0.2100	nan	0.2100	0.1886
0.0209	32.0	8032	0.3401	0.0850	0.1941	0.1941	nan	0.1941	0.1701
0.0172	33.0	8283	0.3326	0.0879	0.1986	0.1986	nan	0.1986	0.1759
0.0187	34.0	8534	0.3343	0.0869	0.1960	0.1960	nan	0.1960	0.1739
0.0181	35.0	8785	0.3223	0.0824	0.1835	0.1835	nan	0.1835	0.1648
0.0168	36.0	9036	0.3461	0.0864	0.1933	0.1933	nan	0.1933	0.1727
0.0169	37.0	9287	0.3438	0.0848	0.1888	0.1888	nan	0.1888	0.1695
0.0182	38.0	9538	0.3506	0.0865	0.1933	0.1933	nan	0.1933	0.1730
0.0167	39.0	9789	0.3535	0.0869	0.1946	0.1946	nan	0.1946	0.1739
0.0174	39.84	10000	0.3478	0.0862	0.1924	0.1924	nan	0.1924	0.1723

Framework Versions

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

📄 License

This model is under the other license.

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