SegFormer_mit-b5_Clean-Set3-Grayscale

Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale

SegFormer_mit-b5_Clean-Set3-Grayscale is a image segmentation model from Hasano20. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Jul 9, 2024
Updated: Jul 9, 2024
SHA: 967bff6f

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_mit-b5_Clean-Set3-Grayscale

This model is a fine-tuned version of nvidia/mit-b5 on _Clean-Set3-Grayscale. It achieves the following results on the evaluation set:

Train-Loss: 0.0053
Loss: 0.0156
Mean Iou: 0.9776
Mean Accuracy: 0.9882
Overall Accuracy: 0.9952
Accuracy Background: 0.9974
Accuracy Melt: 0.9708
Accuracy Substrate: 0.9963
Iou Background: 0.9942
Iou Melt: 0.9458
Iou Substrate: 0.9927

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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 200
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.1206	1.8519	50	0.0898	0.8826	0.9277	0.9727	0.9809	0.8182	0.9840	0.9697	0.7209	0.9571
0.0687	3.7037	100	0.0445	0.9291	0.9568	0.9845	0.9920	0.8888	0.9895	0.9833	0.8286	0.9754
0.0457	5.5556	150	0.0413	0.9284	0.9428	0.9859	0.9938	0.8381	0.9966	0.9877	0.8204	0.9770
0.0281	7.4074	200	0.0240	0.9592	0.9706	0.9914	0.9971	0.9198	0.9949	0.9900	0.9011	0.9865
0.0234	9.2593	250	0.0179	0.9672	0.9810	0.9932	0.9960	0.9513	0.9957	0.9926	0.9195	0.9893
0.0147	11.1111	300	0.0180	0.9672	0.9785	0.9932	0.9955	0.9429	0.9972	0.9925	0.9197	0.9893
0.012	12.9630	350	0.0139	0.9748	0.9864	0.9946	0.9967	0.9664	0.9962	0.9936	0.9390	0.9918
0.0104	14.8148	400	0.0138	0.9756	0.9890	0.9947	0.9972	0.9748	0.9949	0.9935	0.9413	0.9919
0.0094	16.6667	450	0.0136	0.9767	0.9862	0.9950	0.9965	0.9646	0.9974	0.9940	0.9436	0.9924
0.0101	18.5185	500	0.0135	0.9767	0.9867	0.9950	0.9974	0.9663	0.9964	0.9940	0.9438	0.9924
0.0087	20.3704	550	0.0144	0.9764	0.9887	0.9949	0.9954	0.9736	0.9970	0.9935	0.9435	0.9923
0.0078	22.2222	600	0.0145	0.9760	0.9885	0.9949	0.9967	0.9727	0.9960	0.9938	0.9417	0.9924
0.0095	24.0741	650	0.0145	0.9753	0.9855	0.9948	0.9971	0.9626	0.9967	0.9939	0.9398	0.9921
0.0073	25.9259	700	0.0145	0.9761	0.9892	0.9949	0.9965	0.9752	0.9960	0.9938	0.9419	0.9925
0.009	27.7778	750	0.0143	0.9772	0.9891	0.9951	0.9958	0.9745	0.9970	0.9938	0.9451	0.9929
0.0049	29.6296	800	0.0143	0.9782	0.9883	0.9953	0.9966	0.9713	0.9971	0.9942	0.9474	0.9929
0.0075	31.4815	850	0.0153	0.9767	0.9886	0.9951	0.9967	0.9727	0.9963	0.9941	0.9434	0.9925
0.008	33.3333	900	0.0155	0.9772	0.9876	0.9952	0.9970	0.9690	0.9968	0.9943	0.9447	0.9927
0.0061	35.1852	950	0.0150	0.9777	0.9877	0.9953	0.9973	0.9691	0.9967	0.9943	0.9461	0.9928
0.0053	37.0370	1000	0.0156	0.9776	0.9882	0.9952	0.9974	0.9708	0.9963	0.9942	0.9458	0.9927

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Jul 9, 2024
Updated: Jul 9, 2024