SegFormer-B0 Image Segmentation Model Open Source - Free Deployment, Highly Practical for High-Precision Image Segmentation

Segformer B0 Finetuned Morphpadver1 Hgo Coord V3

Developed by NICOPOI-9

An image segmentation model fine-tuned based on nvidia/mit-b3, trained on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset, excelling in high-precision image segmentation tasks.

Image Segmentation

Transformers

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

Downloads 22

Release Time : 4/25/2025

Model Overview

This model is a variant of the SegFormer architecture, specifically designed for image segmentation tasks. Through fine-tuning on a specific dataset, it achieves extremely high segmentation accuracy (mIoU reaches 0.9952).

Model Features

High-precision segmentation

Achieves an average IoU (mIoU) of 0.9952 on the evaluation set, demonstrating excellent performance.

Multi-angle adaptation

Supports image segmentation at 0-degree and 90-degree angles, with accuracy exceeding 99% for each angle.

Efficient fine-tuning

Based on a pre-trained model, fine-tuning achieves high performance in just 60 training epochs.

Model Capabilities

Image segmentation

Pixel-level classification

Multi-angle image processing

Use Cases

Medical imaging

Tissue segmentation

Used for segmenting specific tissue regions in medical images

Accuracy can reach over 99%

Industrial inspection

Defect detection

Identifies defect areas on product surfaces

🚀 segformer-b0-finetuned-morphpadver1-hgo-coord-v3

This model is a fine - tuned version of nvidia/mit-b3 on the NICOPOI - 9/morphpad_coord_hgo_512_4class dataset. It offers high - performance image segmentation capabilities, achieving excellent results in accuracy and loss metrics on the evaluation set.

🚀 Quick Start

This model is ready for use after fine - tuning. You can load it using the transformers library and start making predictions on relevant image segmentation tasks.

📚 Documentation

Model Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.0298
Mean Iou: 0.9952
Mean Accuracy: 0.9976
Overall Accuracy: 0.9976
Accuracy 0 - 0: 0.9983
Accuracy 0 - 90: 0.9969
Accuracy 90 - 0: 0.9980
Accuracy 90 - 90: 0.9971
Iou 0 - 0: 0.9959
Iou 0 - 90: 0.9950
Iou 90 - 0: 0.9945
Iou 90 - 90: 0.9954

Training Procedure

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
0.9708	2.6525	4000	0.9827	0.3521	0.5133	0.5141	0.6031	0.4019	0.6068	0.4413	0.4004	0.3106	0.2982	0.3990
1.8598	5.3050	8000	0.6389	0.5316	0.6914	0.6913	0.6875	0.7105	0.5770	0.7908	0.6004	0.4655	0.5143	0.5463
0.4633	7.9576	12000	0.5041	0.6369	0.7767	0.7768	0.7883	0.7582	0.7869	0.7735	0.6734	0.6126	0.5897	0.6720
0.5576	10.6101	16000	0.4221	0.6949	0.8191	0.8194	0.8477	0.7814	0.8269	0.8206	0.7084	0.7075	0.6533	0.7106
0.3003	13.2626	20000	0.3963	0.7293	0.8413	0.8414	0.8524	0.8809	0.8047	0.8273	0.7565	0.6617	0.7326	0.7666
0.3174	15.9151	24000	0.4310	0.7511	0.8573	0.8572	0.8538	0.8774	0.8402	0.8577	0.7779	0.7125	0.7412	0.7729
0.2307	18.5676	28000	0.3326	0.8024	0.8901	0.8902	0.9029	0.8852	0.8832	0.8893	0.8196	0.7874	0.7805	0.8221
0.1946	21.2202	32000	0.2625	0.8409	0.9134	0.9134	0.9136	0.9197	0.8974	0.9230	0.8533	0.8114	0.8410	0.8581
0.1325	23.8727	36000	0.1298	0.9185	0.9575	0.9576	0.9647	0.9488	0.9691	0.9474	0.9319	0.9114	0.9158	0.9150
0.1007	26.5252	40000	0.0752	0.9596	0.9794	0.9794	0.9824	0.9811	0.9748	0.9792	0.9671	0.9557	0.9532	0.9624
0.0336	29.1777	44000	0.2640	0.9435	0.9709	0.9709	0.9750	0.9668	0.9713	0.9706	0.9473	0.9389	0.9382	0.9497
0.0182	31.8302	48000	0.1066	0.9680	0.9837	0.9837	0.9882	0.9801	0.9845	0.9821	0.9712	0.9677	0.9627	0.9703
0.0141	34.4828	52000	0.0716	0.9806	0.9902	0.9902	0.9880	0.9898	0.9893	0.9937	0.9822	0.9789	0.9792	0.9821
0.0117	37.1353	56000	0.0705	0.9850	0.9925	0.9925	0.9914	0.9920	0.9929	0.9935	0.9861	0.9838	0.9842	0.9859
0.0129	39.7878	60000	0.0932	0.9833	0.9916	0.9916	0.9897	0.9930	0.9920	0.9916	0.9834	0.9823	0.9827	0.9846
0.0101	42.4403	64000	0.0302	0.9924	0.9962	0.9962	0.9964	0.9966	0.9972	0.9946	0.9938	0.9917	0.9921	0.9921
0.0071	45.0928	68000	0.0294	0.9933	0.9966	0.9966	0.9971	0.9961	0.9958	0.9976	0.9946	0.9931	0.9925	0.9930
0.0066	47.7454	72000	0.0637	0.9912	0.9956	0.9956	0.9959	0.9957	0.9962	0.9946	0.9927	0.9917	0.9895	0.9910
0.0083	50.3979	76000	0.0494	0.9901	0.9950	0.9950	0.9956	0.9951	0.9946	0.9948	0.9901	0.9899	0.9887	0.9917
0.0049	53.0504	80000	0.0286	0.9944	0.9972	0.9972	0.9982	0.9967	0.9976	0.9963	0.9956	0.9935	0.9939	0.9946
0.0104	55.7029	84000	0.0244	0.9953	0.9976	0.9976	0.9984	0.9971	0.9978	0.9971	0.9957	0.9953	0.9942	0.9959
0.0046	58.3554	88000	0.0298	0.9952	0.9976	0.9976	0.9983	0.9969	0.9980	0.9971	0.9959	0.9950	0.9945	0.9954

Framework Versions

Transformers 4.48.3
Pytorch 2.1.0
Datasets 3.2.0
Tokenizers 0.21.0

📄 License

This model uses the "other" license.

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