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Roadsense High Definition Street Segmentation

Developed by iammartian0

A lightweight image segmentation model based on SegFormer architecture, specifically fine-tuned for sidewalk scenarios

Image Segmentation

Transformers

Open Source License:Other #Sidewalk Segmentation #Lightweight Model #Urban Street View

Downloads 63

Release Time : 7/7/2023

Model Overview

This model is based on the SegFormer MIT-B0 architecture, fine-tuned on the segments/sidewalk-semantic dataset for semantic segmentation tasks in sidewalk scenarios.

Model Features

Lightweight Design

Based on SegFormer-B0 architecture, suitable for deployment in resource-constrained environments

Sidewalk Scenario Optimization

Specifically fine-tuned for sidewalk-related scenarios to improve recognition accuracy of relevant categories

Multi-category Recognition

Can recognize 40+ categories including roads, sidewalks, buildings, vehicles, etc.

Model Capabilities

Semantic Segmentation

Scene Understanding

Road Element Recognition

Urban Landscape Analysis

Use Cases

Smart City

Sidewalk Maintenance Monitoring

Automatically detects damaged or abnormal areas on sidewalks

Flat sidewalk recognition accuracy reaches 96.11%

Traffic Infrastructure Analysis

Identifies road signs, traffic lights, and other infrastructure

Autonomous Driving

Road Scene Understanding

Provides environmental perception capabilities for autonomous driving systems

Vehicle recognition accuracy reaches 93.32%

license: other tags:

generated_from_trainer
image_segmentation model-index:
name: segformer-b0-finetuned-segments-sidewalk results: [] datasets:
segments/sidewalk-semantic library_name: transformers pipeline_tag: image-segmentation

segformer-b0-finetuned-segments-sidewalk

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5449
Mean Iou: 0.3292
Mean Accuracy: 0.3907
Overall Accuracy: 0.8555
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.8585
Accuracy Flat-sidewalk: 0.9611
Accuracy Flat-crosswalk: 0.7673
Accuracy Flat-cyclinglane: 0.8223
Accuracy Flat-parkingdriveway: 0.5127
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.4937
Accuracy Human-person: 0.7164
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.9332
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: nan
Accuracy Vehicle-tramtrain: nan
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.3858
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.9040
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.5848
Accuracy Construction-fenceguardrail: 0.4417
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: nan
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.3156
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.9413
Accuracy Nature-terrain: 0.8456
Accuracy Sky: 0.9600
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.0
Accuracy Void-static: 0.2780
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.7447
Iou Flat-sidewalk: 0.8755
Iou Flat-crosswalk: 0.6244
Iou Flat-cyclinglane: 0.7325
Iou Flat-parkingdriveway: 0.3997
Iou Flat-railtrack: nan
Iou Flat-curb: 0.3974
Iou Human-person: 0.4985
Iou Human-rider: 0.0
Iou Vehicle-car: 0.7798
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: nan
Iou Vehicle-tramtrain: nan
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.2904
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.7233
Iou Construction-door: 0.0
Iou Construction-wall: 0.4555
Iou Construction-fenceguardrail: 0.3734
Iou Construction-bridge: 0.0
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.0
Iou Object-pole: 0.2484
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.8451
Iou Nature-terrain: 0.7346
Iou Sky: 0.9161
Iou Void-ground: 0.0
Iou Void-dynamic: 0.0
Iou Void-static: 0.2359
Iou Void-unclear: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-crosswalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Vehicle-car	Accuracy Vehicle-bus	Accuracy Vehicle-tramtrain	Accuracy Vehicle-bicycle	Accuracy Construction-building	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Construction-tunnel	Accuracy Object-pole	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-crosswalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bus	Iou Vehicle-tramtrain	Iou Vehicle-bicycle	Iou Construction-building	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-tunnel	Iou Object-pole	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-static
1.4172	1.87	200	1.2183	0.1696	0.2214	0.7509	nan	0.8882	0.9199	0.0	0.4200	0.0164	nan	0.0	0.0	0.8778	nan	nan	0.0	0.8448	0.0	0.0	nan	0.0	0.9430	0.8044	0.9274	0.0	nan	0.5435	0.8135	0.0	0.3743	0.0160	nan	0.0	0.0	0.6044	nan	nan	0.0	0.5373	0.0	0.0	nan	0.0	0.7516	0.6550	0.7928	0.0
1.1152	3.74	400	0.8946	0.1947	0.2441	0.7852	nan	0.8535	0.9471	0.0	0.7379	0.2453	nan	0.0398	0.0	0.8882	nan	nan	0.0	0.8746	0.0061	0.0	nan	0.0014	0.9526	0.8285	0.9448	0.0019	nan	0.6355	0.8321	0.0	0.5529	0.1940	nan	0.0392	0.0	0.6807	nan	nan	0.0	0.5913	0.0061	0.0	nan	0.0014	0.7701	0.6777	0.8567	0.0019
0.6637	5.61	600	0.7447	0.2349	0.2841	0.8104	nan	0.8589	0.9451	0.4455	0.8008	0.3753	nan	0.3267	0.0380	0.8920	nan	nan	0.0	0.9227	0.0938	0.0	nan	0.0167	0.9291	0.8677	0.9557	0.0562	nan	0.6768	0.8543	0.4064	0.6414	0.2914	nan	0.2749	0.0376	0.7268	nan	nan	0.0	0.6078	0.0879	0.0	nan	0.0164	0.8005	0.6817	0.8918	0.0525
0.673	7.48	800	0.6631	0.2691	0.3202	0.8278	nan	0.8387	0.9575	0.6176	0.7938	0.4208	nan	0.3575	0.3977	0.9264	nan	nan	0.0	0.9068	0.4035	0.0	nan	0.1137	0.9495	0.8165	0.9453	0.1599	nan	0.7042	0.8567	0.5239	0.6600	0.3246	nan	0.3003	0.3212	0.7246	nan	nan	0.0	0.6749	0.3113	0.0	nan	0.1038	0.8147	0.7070	0.9008	0.1445
0.502	9.35	1000	0.6249	0.2818	0.3371	0.8345	nan	0.8332	0.9538	0.7158	0.8344	0.4079	nan	0.4420	0.4941	0.9275	nan	nan	0.0172	0.9102	0.4787	0.0253	nan	0.1454	0.9460	0.8350	0.9588	0.1887	nan	0.7176	0.8635	0.6035	0.6519	0.3246	nan	0.3545	0.3720	0.7524	nan	nan	0.0172	0.6861	0.3286	0.0250	nan	0.1309	0.8335	0.7300	0.9037	0.1584
0.9687	11.21	1200	0.5786	0.3093	0.3675	0.8471	nan	0.8703	0.9504	0.7382	0.7705	0.5297	nan	0.4804	0.6250	0.9168	nan	nan	0.1397	0.9228	0.5710	0.3183	nan	0.2252	0.9314	0.8840	0.9536	0.1981	nan	0.7380	0.8743	0.5825	0.7093	0.3829	nan	0.3743	0.4600	0.7727	nan	nan	0.1372	0.7008	0.4315	0.2847	nan	0.1930	0.8397	0.7121	0.9109	0.1761
0.4681	13.08	1400	0.5759	0.3106	0.3665	0.8462	nan	0.8586	0.9572	0.5158	0.8121	0.5195	nan	0.4539	0.6944	0.9308	nan	nan	0.2759	0.9126	0.4927	0.3145	nan	0.2566	0.9396	0.8736	0.9644	0.2226	nan	0.7134	0.8742	0.5009	0.7146	0.4018	nan	0.3726	0.4661	0.7674	nan	nan	0.2501	0.6997	0.3933	0.2827	nan	0.2137	0.8377	0.7212	0.9109	0.1964
0.5374	14.95	1600	0.5534	0.3232	0.3823	0.8518	nan	0.8607	0.9545	0.7138	0.8398	0.5129	nan	0.4823	0.7055	0.9225	nan	nan	0.3058	0.8999	0.5436	0.3798	nan	0.2878	0.9485	0.8388	0.9598	0.3145	nan	0.7336	0.8788	0.6094	0.7062	0.3966	nan	0.3854	0.4897	0.7823	nan	nan	0.2782	0.7148	0.4182	0.3304	nan	0.2324	0.8415	0.7356	0.9130	0.2491
0.6115	16.82	1800	0.5528	0.3266	0.3849	0.8539	nan	0.8521	0.9611	0.6840	0.8291	0.5057	nan	0.5070	0.7165	0.9267	nan	nan	0.3659	0.9007	0.5844	0.3961	nan	0.2827	0.9517	0.8371	0.9602	0.2848	nan	0.7414	0.8721	0.6312	0.7245	0.3979	nan	0.3987	0.4932	0.7799	nan	nan	0.2788	0.7242	0.4542	0.3464	nan	0.2326	0.8384	0.7318	0.9141	0.2386
0.4766	18.69	2000	0.5449	0.3292	0.3907	0.8555	nan	0.8585	0.9611	0.7673	0.8223	0.5127	nan	0.4937	0.7164	0.9332	nan	nan	0.3858	0.9040	0.5848	0.4417	nan	0.3156	0.9413	0.8456	0.9600	0.2780	nan	0.7447	0.8755	0.6244	0.7325	0.3997	nan	0.3974	0.4985	0.7798	nan	nan	0.2904	0.7233	0.4555	0.3734	nan	0.2484	0.8451	0.7346	0.9161	0.2359

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

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