SegFormer-b0 Open-Source Image Segmentation Model - Free Deployment for High-Precision Semantic Segmentation Tasks

Segformer B0 Finetuned Morphpadver1 Hgo Coord V2

Developed by NICOPOI-9

A lightweight image segmentation model based on the SegFormer architecture, fine-tuned on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset, specifically designed for high-precision semantic segmentation tasks.

Image Segmentation

Transformers

Open Source License:Other #High-precision image segmentation #Medical image processing #Fine-tuning optimization

Downloads 23

Release Time : 4/25/2025

Model Overview

This model is a fine-tuned version of SegFormer-B0, optimized for specific image segmentation tasks, demonstrating extremely high accuracy (mean IoU 0.9952) and stability on the evaluation set.

Model Features

High-precision segmentation

Achieves a mean IoU of 0.9952 on the evaluation set, with segmentation accuracy exceeding 99% across all angles.

Lightweight architecture

Based on the lightweight design of SegFormer-B0, suitable for deployment in resource-constrained environments.

Multi-angle adaptation

Excellent performance in segmenting targets at different angles (0° accuracy 99.93%, 90° accuracy 99.83%).

Model Capabilities

Image semantic segmentation

Multi-class pixel-level classification

High-precision edge detection

Use Cases

Medical image analysis

Tissue morphology analysis

Used for precise segmentation of specific tissue structures in pathological slides.

Achieves an average accuracy of 99.56% on the test set.

Industrial inspection

Mechanical component localization

Identifies and locates industrial parts at different angles.

Detection accuracy exceeds 99.5% across all angles.

🚀 segformer-b0-finetuned-morphpadver1-hgo-coord-v2

This model is a fine - tuned version of NICOPOI - 9/segformer - b0 - finetuned - morphpadver1 - hgo - coord - v1, designed for image segmentation tasks with high accuracy.

🚀 Quick Start

This model is a fine-tuned version of NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1 on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset. It achieves excellent results on the evaluation set:

Loss: 0.0408
Mean Iou: 0.9952
Mean Accuracy: 0.9976
Overall Accuracy: 0.9976
Accuracy 0 - 0: 0.9993
Accuracy 0 - 90: 0.9958
Accuracy 90 - 0: 0.9969
Accuracy 90 - 90: 0.9983
Iou 0 - 0: 0.9975
Iou 0 - 90: 0.9929
Iou 90 - 0: 0.9949
Iou 90 - 90: 0.9955

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy 0 - 0	Accuracy 0 - 90	Accuracy 90 - 0	Accuracy 90 - 90	Iou 0 - 0	Iou 0 - 90	Iou 90 - 0	Iou 90 - 90
0.0654	2.5445	4000	0.1134	0.9236	0.9604	0.9602	0.9729	0.9373	0.9642	0.9674	0.9265	0.9144	0.9233	0.9301
0.0552	5.0891	8000	0.1426	0.9161	0.9562	0.9561	0.9607	0.9538	0.9547	0.9555	0.9166	0.9146	0.9112	0.9218
0.0469	7.6336	12000	0.0633	0.9556	0.9774	0.9773	0.9811	0.9714	0.9744	0.9826	0.9588	0.9516	0.9545	0.9576
0.0378	10.1781	16000	0.0506	0.9650	0.9822	0.9822	0.9826	0.9773	0.9844	0.9844	0.9661	0.9601	0.9643	0.9696
0.0582	12.7226	20000	0.0402	0.9737	0.9867	0.9866	0.9925	0.9891	0.9791	0.9860	0.9774	0.9700	0.9699	0.9774
0.0322	15.2672	24000	0.0453	0.9707	0.9850	0.9851	0.9809	0.9843	0.9909	0.9840	0.9746	0.9715	0.9637	0.9728
0.0254	17.8117	28000	0.1030	0.9652	0.9823	0.9822	0.9895	0.9808	0.9748	0.9841	0.9761	0.9599	0.9583	0.9666
2.3028	20.3562	32000	0.0572	0.9745	0.9871	0.9870	0.9861	0.9839	0.9885	0.9896	0.9789	0.9717	0.9700	0.9773
0.0769	22.9008	36000	0.0225	0.9866	0.9932	0.9932	0.9960	0.9899	0.9939	0.9932	0.9893	0.9837	0.9849	0.9884
0.0512	25.4453	40000	0.0329	0.9850	0.9924	0.9924	0.9959	0.9867	0.9954	0.9917	0.9857	0.9820	0.9843	0.9878
0.3281	27.9898	44000	0.0301	0.9866	0.9933	0.9932	0.9958	0.9913	0.9907	0.9952	0.9899	0.9858	0.9843	0.9863
0.1536	30.5344	48000	0.0355	0.9889	0.9944	0.9944	0.9981	0.9927	0.9920	0.9949	0.9941	0.9855	0.9880	0.9880
0.0079	33.0789	52000	0.0256	0.9933	0.9966	0.9966	0.9979	0.9951	0.9961	0.9974	0.9956	0.9917	0.9934	0.9924
0.0074	35.6234	56000	0.0205	0.9938	0.9969	0.9969	0.9983	0.9970	0.9966	0.9956	0.9963	0.9923	0.9928	0.9939
0.0077	38.1679	60000	0.0255	0.9933	0.9967	0.9966	0.9985	0.9946	0.9964	0.9971	0.9954	0.9925	0.9919	0.9934
0.0061	40.7125	64000	0.0282	0.9945	0.9972	0.9972	0.9987	0.9958	0.9974	0.9969	0.9967	0.9916	0.9950	0.9945
0.0051	43.2570	68000	0.0262	0.9937	0.9969	0.9968	0.9987	0.9949	0.9959	0.9979	0.9968	0.9916	0.9934	0.9930
0.0047	45.8015	72000	0.0564	0.9912	0.9956	0.9956	0.9991	0.9950	0.9940	0.9943	0.9958	0.9882	0.9897	0.9912
0.0046	48.3461	76000	0.0492	0.9939	0.9969	0.9969	0.9992	0.9941	0.9974	0.9970	0.9969	0.9903	0.9938	0.9945
0.0552	50.8906	80000	0.0438	0.9948	0.9974	0.9974	0.9992	0.9966	0.9967	0.9972	0.9980	0.9924	0.9948	0.9941
0.0039	53.4351	84000	0.0361	0.9953	0.9976	0.9976	0.9991	0.9961	0.9973	0.9981	0.9975	0.9928	0.9952	0.9956
0.0034	55.9796	88000	0.0317	0.9958	0.9979	0.9979	0.9993	0.9964	0.9974	0.9985	0.9979	0.9937	0.9955	0.9963
0.0149	58.5242	92000	0.0408	0.9952	0.9976	0.9976	0.9993	0.9958	0.9969	0.9983	0.9975	0.9929	0.9949	0.9955

Framework versions

Transformers 4.48.3
Pytorch 2.1.0
Datasets 3.2.0
Tokenizers 0.21.0

📄 License

The license of this model is other.

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