SegFormer Open-Source Image Segmentation Model - Free Deployment for High-Precision Specific Image Segmentation

Segformer B0 Finetuned Morphpadver1 Hgo Coord V1

Developed by NICOPOI-9

SegFormer image segmentation model based on NVIDIA MIT-B1 architecture, fine-tuned on specific datasets, excels in high-precision image segmentation tasks

Image Segmentation

Transformers

Open Source License:Other #High-precision image segmentation #Medical image processing #Coordinate-enhanced segmentation

Downloads 29

Release Time : 4/25/2025

Model Overview

This model is a SegFormer-b0 variant based on the nvidia/mit-b1 architecture, fine-tuned on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset, specifically designed for image segmentation tasks. It demonstrated outstanding performance in evaluations, achieving a mean Intersection over Union (mIoU) of 0.9579

Model Features

High-precision segmentation

Achieved an average Intersection over Union (mIoU) of 0.9579 and an average accuracy of 0.9785 on the evaluation set, demonstrating excellent performance

Lightweight architecture

Based on the SegFormer-b0 architecture, balancing performance and computational efficiency

Multi-angle adaptability

Maintains stable segmentation performance across different angles (0-0, 0-90, 90-0, 90-90)

Model Capabilities

Image segmentation

Pixel-level classification

Semantic segmentation

Use Cases

Medical image analysis

Tissue segmentation

Used for precise segmentation of specific tissues in medical images

Industrial inspection

Defect detection

Identifies and segments defective areas on the surface of industrial products

🚀 segformer-b0-finetuned-morphpadver1-hgo-coord-v1

This model is a fine - tuned version of nvidia/mit-b1 on the NICOPOI - 9/morphpad_coord_hgo_512_4class dataset. It offers high - performance image segmentation capabilities, achieving excellent results in evaluation metrics.

🚀 Quick Start

This section provides an overview of the model's evaluation results and training details.

Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.0644
Mean Iou: 0.9579
Mean Accuracy: 0.9785
Overall Accuracy: 0.9785
Accuracy 0 - 0: 0.9792
Accuracy 0 - 90: 0.9782
Accuracy 90 - 0: 0.9762
Accuracy 90 - 90: 0.9804
Iou 0 - 0: 0.9634
Iou 0 - 90: 0.9512
Iou 90 - 0: 0.9543
Iou 90 - 90: 0.9627

📚 Documentation

Model description

This model is a fine - tuned version of nvidia/mit-b1 on the NICOPOI - 9/morphpad_coord_hgo_512_4class dataset.

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: 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: 40

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
1.1962	2.5445	4000	1.2063	0.2464	0.3994	0.4010	0.2991	0.2803	0.5096	0.5084	0.2367	0.2111	0.2658	0.2721
1.051	5.0891	8000	1.0734	0.3118	0.4765	0.4765	0.3856	0.5520	0.3937	0.5745	0.3123	0.3148	0.2968	0.3233
0.9309	7.6336	12000	0.9672	0.3612	0.5314	0.5323	0.4806	0.5216	0.7075	0.4158	0.3362	0.3706	0.3778	0.3603
0.8041	10.1781	16000	0.8444	0.4475	0.6180	0.6178	0.6131	0.6672	0.6120	0.5798	0.4403	0.4360	0.4543	0.4593
0.6617	12.7226	20000	0.7405	0.5039	0.6697	0.6700	0.6310	0.6588	0.6714	0.7177	0.5097	0.4912	0.5114	0.5033
0.54	15.2672	24000	0.6090	0.5828	0.7360	0.7362	0.6931	0.7532	0.7427	0.7550	0.5911	0.5709	0.5876	0.5819
0.7378	17.8117	28000	0.3740	0.7401	0.8507	0.8505	0.8789	0.8270	0.8186	0.8783	0.7712	0.7324	0.7203	0.7366
0.58	20.3562	32000	0.1892	0.8644	0.9272	0.9272	0.9329	0.9188	0.9142	0.9430	0.8810	0.8523	0.8539	0.8704
0.1305	22.9008	36000	0.1473	0.8945	0.9443	0.9443	0.9563	0.9245	0.9421	0.9542	0.9021	0.8783	0.8925	0.9049
0.1775	25.4453	40000	0.1133	0.9178	0.9571	0.9571	0.9578	0.9536	0.9583	0.9586	0.9264	0.9068	0.9130	0.9249
0.4792	27.9898	44000	0.0961	0.9306	0.9640	0.9640	0.9662	0.9633	0.9617	0.9650	0.9374	0.9194	0.9268	0.9387
0.1084	30.5344	48000	0.0886	0.9364	0.9671	0.9672	0.9684	0.9600	0.9689	0.9712	0.9429	0.9257	0.9335	0.9437
0.0471	33.0789	52000	0.0721	0.9485	0.9735	0.9735	0.9772	0.9674	0.9729	0.9767	0.9528	0.9402	0.9467	0.9542
0.0722	35.6234	56000	0.0646	0.9554	0.9772	0.9772	0.9809	0.9728	0.9757	0.9794	0.9576	0.9488	0.9522	0.9629
0.0406	38.1679	60000	0.0644	0.9579	0.9785	0.9785	0.9792	0.9782	0.9762	0.9804	0.9634	0.9512	0.9543	0.9627

Framework versions

Transformers 4.48.3
Pytorch 2.1.0
Datasets 3.2.0
Tokenizers 0.21.0

📄 License

This model is under the "other" license.

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