SegFormer-B5 Open-Source Image Segmentation Model - Free Deployment to Assist in Precise Segmentation and Processing of Foot Images

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Segformer B5 Finetuned Segments Instryde Foot Test

Developed by plant

An image segmentation model fine-tuned on the inStryde/inStrydeSegmentationFoot dataset, based on nvidia/mit-b5

Image Segmentation

Transformers

Open Source License:Other #Foot Image Segmentation #High-Precision Segmentation #Medical Image Analysis

Downloads 19

Release Time : 8/4/2022

Model Overview

This model is a B5 variant of the SegFormer architecture, specifically fine-tuned for foot image segmentation tasks. Suitable for medical imaging or foot-related visual analysis scenarios.

Model Features

High-Precision Segmentation

Achieves an overall accuracy of 0.9344 on foot image segmentation tasks

Domain-Specific Optimization

Specifically fine-tuned for medical/foot imaging data

Efficient Architecture

Based on SegFormer's hybrid Transformer architecture, balancing performance and efficiency

Model Capabilities

Foot Image Segmentation

Medical Image Analysis

Pixel-Level Classification

Use Cases

Medical Imaging

Foot Lesion Area Segmentation

Identify and segment specific areas in foot medical images

Average IoU reaches 0.4672

Biometrics

Foot Feature Extraction

Foot contour analysis for biometric recognition systems

🚀 segformer-b5-finetuned-segments-instryde-foot-test

This is a fine - tuned model based on nvidia/mit-b5 for image segmentation tasks, achieving excellent results on the inStryde/inStrydeSegmentationFoot dataset.

🚀 Quick Start

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

Loss: 0.0496
Mean Iou: 0.4672
Mean Accuracy: 0.9344
Overall Accuracy: 0.9344
Per Category Iou: [0.0, 0.9343870058298716]
Per Category Accuracy: [nan, 0.9343870058298716]

📚 Documentation

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

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.1392	0.23	20	0.2371	0.4064	0.8128	0.8128	[0.0, 0.8127920708469037]	[nan, 0.8127920708469037]
0.2273	0.45	40	0.0993	0.4449	0.8898	0.8898	[0.0, 0.889800913515142]	[nan, 0.889800913515142]
0.0287	0.68	60	0.0607	0.4190	0.8379	0.8379	[0.0, 0.8379005425233161]	[nan, 0.8379005425233161]
0.03	0.91	80	0.0572	0.4072	0.8144	0.8144	[0.0, 0.8144304164916533]	[nan, 0.8144304164916533]
0.0239	1.14	100	0.0577	0.3973	0.7946	0.7946	[0.0, 0.7946284254068925]	[nan, 0.7946284254068925]
0.0196	1.36	120	0.0425	0.4227	0.8455	0.8455	[0.0, 0.8454754171184029]	[nan, 0.8454754171184029]
0.0295	1.59	140	0.0368	0.4479	0.8958	0.8958	[0.0, 0.895802316554768]	[nan, 0.895802316554768]
0.0297	1.82	160	0.0441	0.4561	0.9121	0.9121	[0.0, 0.9121241975954804]	[nan, 0.9121241975954804]
0.0276	2.05	180	0.0332	0.4629	0.9258	0.9258	[0.0, 0.925774145806165]	[nan, 0.925774145806165]
0.0148	2.27	200	0.0395	0.4310	0.8621	0.8621	[0.0, 0.8620666905637888]	[nan, 0.8620666905637888]
0.012	2.5	220	0.0372	0.4381	0.8761	0.8761	[0.0, 0.8761025846276997]	[nan, 0.8761025846276997]
0.0117	2.73	240	0.0339	0.4471	0.8941	0.8941	[0.0, 0.8941320836457919]	[nan, 0.8941320836457919]
0.0198	2.95	260	0.0297	0.4485	0.8969	0.8969	[0.0, 0.8969491585060927]	[nan, 0.8969491585060927]
0.0247	3.18	280	0.0303	0.4565	0.9130	0.9130	[0.0, 0.9130423308930413]	[nan, 0.9130423308930413]
0.0115	3.41	300	0.0307	0.4533	0.9066	0.9066	[0.0, 0.9065626188900153]	[nan, 0.9065626188900153]
0.0164	3.64	320	0.0330	0.4549	0.9097	0.9097	[0.0, 0.9097436483868343]	[nan, 0.9097436483868343]
0.0114	3.86	340	0.0362	0.4425	0.8850	0.8850	[0.0, 0.8849727418868903]	[nan, 0.8849727418868903]
0.012	4.09	360	0.0321	0.4582	0.9164	0.9164	[0.0, 0.9164498699219532]	[nan, 0.9164498699219532]
0.0153	4.32	380	0.0321	0.4572	0.9144	0.9144	[0.0, 0.9144310762281544]	[nan, 0.9144310762281544]
0.0115	4.55	400	0.0307	0.4573	0.9145	0.9145	[0.0, 0.9145300367033407]	[nan, 0.9145300367033407]
0.0139	4.77	420	0.0330	0.4678	0.9357	0.9357	[0.0, 0.935664695520609]	[nan, 0.935664695520609]
0.014	5.0	440	0.0317	0.4635	0.9271	0.9271	[0.0, 0.9270562337402442]	[nan, 0.9270562337402442]
0.0197	5.23	460	0.0320	0.4678	0.9356	0.9356	[0.0, 0.9355745315321061]	[nan, 0.9355745315321061]
0.0086	5.45	480	0.0337	0.4607	0.9214	0.9214	[0.0, 0.9213528116870122]	[nan, 0.9213528116870122]
0.3103	5.68	500	0.0338	0.4548	0.9096	0.9096	[0.0, 0.9095853116265363]	[nan, 0.9095853116265363]
0.0088	5.91	520	0.0305	0.4635	0.9270	0.9270	[0.0, 0.9270243464760175]	[nan, 0.9270243464760175]
0.0119	6.14	540	0.0299	0.4680	0.9359	0.9359	[0.0, 0.9359494817769782]	[nan, 0.9359494817769782]
0.0114	6.36	560	0.0314	0.4574	0.9148	0.9148	[0.0, 0.914796130425508]	[nan, 0.914796130425508]
0.0122	6.59	580	0.0289	0.4613	0.9227	0.9227	[0.0, 0.9226920767845322]	[nan, 0.9226920767845322]
0.0164	6.82	600	0.0312	0.4620	0.9240	0.9240	[0.0, 0.9239807620836238]	[nan, 0.9239807620836238]
0.0062	7.05	620	0.0335	0.4605	0.9210	0.9210	[0.0, 0.9209954544155065]	[nan, 0.9209954544155065]
0.0089	7.27	640	0.0309	0.4659	0.9317	0.9317	[0.0, 0.9317029778306545]	[nan, 0.9317029778306545]
0.0251	7.5	660	0.0291	0.4734	0.9468	0.9468	[0.0, 0.9467878529315391]	[nan, 0.9467878529315391]
0.0065	7.73	680	0.0326	0.4598	0.9195	0.9195	[0.0, 0.9195297398219151]	[nan, 0.9195297398219151]
0.0056	7.95	700	0.0310	0.4606	0.9213	0.9213	[0.0, 0.9212714441851925]	[nan, 0.9212714441851925]
0.0099	8.18	720	0.0345	0.4503	0.9006	0.9006	[0.0, 0.9006183930138303]	[nan, 0.9006183930138303]
0.0103	8.41	740	0.0335	0.4539	0.9078	0.9078	[0.0, 0.9077512441530853]	[nan, 0.9077512441530853]
0.0065	8.64	760	0.0334	0.4544	0.9088	0.9088	[0.0, 0.9087936278250467]	[nan, 0.9087936278250467]
0.0047	8.86	780	0.0341	0.4557	0.9114	0.9114	[0.0, 0.9114215782216583]	[nan, 0.9114215782216583]
0.0105	9.09	800	0.0315	0.4597	0.9195	0.9195	[0.0, 0.9194703635368034]	[nan, 0.9194703635368034]
0.0087	9.32	820	0.0329	0.4583	0.9166	0.9166	[0.0, 0.9165708216138474]	[nan, 0.9165708216138474]
0.0122	9.55	840	0.0357	0.4537	0.9073	0.9073	[0.0, 0.9073004242105703]	[nan, 0.9073004242105703]
0.0057	9.77	860	0.0319	0.4621	0.9241	0.9241	[0.0, 0.9241050124580242]	[nan, 0.9241050124580242]
0.0068	10.0	880	0.0342	0.4539	0.9078	0.9078	[0.0, 0.907799624829843]	[nan, 0.907799624829843]
0.0095	10.23	900	0.0340	0.4578	0.9156	0.9156	[0.0, 0.9155933120311748]	[nan, 0.9155933120311748]
0.0043	10.45	920	0.0319	0.4636	0.9272	0.9272	[0.0, 0.9271771854321385]	[nan, 0.9271771854321385]
0.0049	10.68	940	0.0308	0.4659	0.9319	0.9319	[0.0, 0.9318525181042692]	[nan, 0.9318525181042692]
0.005	10.91	960	0.0319	0.4640	0.9281	0.9281	[0.0, 0.9280612323438019]	[nan, 0.9280612323438019]
0.0043	11.14	980	0.0313	0.4653	0.9306	0.9306	[0.0, 0.930638602941985]	[nan, 0.930638602941985]
0.0084	11.36	1000	0.0321	0.4632	0.9264	0.9264	[0.0, 0.9264294840640648]	[nan, 0.9264294840640648]
0.0044	11.59	1020	0.0320	0.4643	0.9285	0.9285	[0.0, 0.9285241474555063]	[nan, 0.9285241474555063]
0.0044	11.82	1040	0.0321	0.4661	0.9321	0.9321	[0.0, 0.9321098153397533]	[nan, 0.9321098153397533]
0.0057	12.05	1060	0.0338	0.4626	0.9253	0.9253	[0.0, 0.9252518544093489]	[nan, 0.9252518544093489]
0.0064	12.27	1080	0.0348	0.4616	0.9231	0.9231	[0.0, 0.9231450958487181]	[nan, 0.9231450958487181]
0.0075	12.5	1100	0.0331	0.4618	0.9237	0.9237	[0.0, 0.9236706859280404]	[nan, 0.9236706859280404]
0.0103	12.73	1120	0.0317	0.4704	0.9408	0.9408	[0.0, 0.9408425274945187]	[nan, 0.9408425274945187]
0.0053	12.95	1140	0.0320	0.4704	0.9407	0.9407	[0.0, 0.9407292727284723]	[nan, 0.9407292727284723]
0.0073	13.18	1160	0.0331	0.4652	0.9305	0.9305	[0.0, 0.9304681710124976]	[nan, 0.9304681710124976]
0.0052	13.41	1180	0.0342	0.4664	0.9328	0.9328	[0.0, 0.9328047377877275]	[nan, 0.9328047377877275]
0.0089	13.64	1200	0.0322	0.4676	0.9353	0.9353	[0.0, 0.9352996413232555]	[nan, 0.9352996413232555]
0.0054	13.86	1220	0.0332	0.4655	0.9311	0.9311	[0.0, 0.9310509382552609]	[nan, 0.9310509382552609]
0.0057	14.09	1240	0.0333	0.4661	0.9321	0.9321	[0.0, 0.9321439017256508]	[nan, 0.9321439017256508]
0.0047	14.32	1260	0.0346	0.4639	0.9278	0.9278	[0.0, 0.9277522557490538]	[nan, 0.9277522557490538]
0.0092	14.55	1280	0.0380	0.4583	0.9166	0.9166	[0.0, 0.9166290983381238]	[nan, 0.9166290983381238]
0.0066	14.77	1300	0.0338	0.4638	0.9277	0.9277	[0.0, 0.927687381659765]	[nan, 0.927687381659765]
0.0076	15.0	1320	0.0347	0.4640	0.9280	0.9280	[0.0, 0.9279897608895007]	[nan, 0.9279897608895007]
0.0054	15.23	1340	0.0345	0.4647	0.9295	0.9295	[0.0, 0.9294664710914461]	[nan, 0.9294664710914461]
0.0036	15.45	1360	0.0349	0.4666	0.9332	0.9332	[0.0, 0.9331950818842955]	[nan, 0.9331950818842955]
0.004	15.68	1380	0.0352	0.4617	0.9234	0.9234	[0.0, 0.9234408777134413]	[nan, 0.9234408777134413]
0.0042	15.91	1400	0.0357	0.4622	0.9244	0.9244	[0.0, 0.9244282833436326]	[nan, 0.9244282833436326]
0.0048	16.14	1420	0.0370	0.4586	0.9172	0.9172	[0.0, 0.9171546884174461]	[nan, 0.9171546884174461]
0.0043	16.36	1440	0.0345	0.4647	0.9294	0.9294	[0.0, 0.9294411811922318]	[nan, 0.9294411811922318]
0.0027	16.59	1460	0.0354	0.4667	0.9334	0.9334	[0.0, 0.9333754098613014]	[nan, 0.9333754098613014]
0.0057	16.82	1480	0.0364	0.4689	0.9379	0.9379	[0.0, 0.9378913062122988]	[nan, 0.9378913062122988]
0.0035	17.05	1500	0.0363	0.4662	0.9325	0.9325	[0.0, 0.9324988144273747]	[nan, 0.9324988144273747]

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