new_ecc_segformer

rishitunu/new_ecc_segformer

new_ecc_segformer is a image segmentation model from rishitunu. 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: Nov 8, 2023
Updated: Nov 10, 2023
SHA: 00f074e4

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

new_ecc_segformer

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

Loss: 0.0663
Mean Iou: 0.1943
Mean Accuracy: 0.3915
Overall Accuracy: 0.3915
Accuracy Background: nan
Accuracy Crack: 0.3915
Iou Background: 0.0
Iou Crack: 0.3887

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: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crack	Iou Crack
0.0489	1.0	438	0.0634	0.1464	0.2933	0.2933	nan	0.2933	0.2929
0.0542	2.0	876	0.0439	0.1956	0.3917	0.3917	nan	0.3917	0.3912
0.0484	3.0	1314	0.0434	0.1719	0.3551	0.3551	nan	0.3551	0.3439
0.0539	4.0	1752	0.0447	0.1871	0.3820	0.3820	nan	0.3820	0.3741
0.0565	5.0	2190	0.0435	0.1888	0.3937	0.3937	nan	0.3937	0.3777
0.0544	6.0	2628	0.0442	0.1904	0.3930	0.3930	nan	0.3930	0.3808
0.0421	7.0	3066	0.0449	0.2256	0.4651	0.4651	nan	0.4651	0.4513
0.0352	8.0	3504	0.0587	0.1569	0.3165	0.3165	nan	0.3165	0.3138
0.0394	9.0	3942	0.0442	0.1842	0.3710	0.3710	nan	0.3710	0.3684
0.0445	10.0	4380	0.0609	0.1167	0.4173	0.4173	nan	0.4173	0.2334
0.0503	11.0	4818	0.0504	0.1702	0.3714	0.3714	nan	0.3714	0.3403
0.0379	12.0	5256	0.0460	0.1903	0.3869	0.3869	nan	0.3869	0.3807
0.0405	13.0	5694	0.0452	0.2017	0.4084	0.4084	nan	0.4084	0.4034
0.0367	14.0	6132	0.0477	0.1995	0.4060	0.4060	nan	0.4060	0.3990
0.0315	15.0	6570	0.0498	0.2073	0.4208	0.4208	nan	0.4208	0.4147
0.0244	16.0	7008	0.0486	0.1963	0.4029	0.4029	nan	0.4029	0.3926
0.031	17.0	7446	0.0568	0.1927	0.3892	0.3892	nan	0.3892	0.3855
0.0288	18.0	7884	0.0560	0.2033	0.4092	0.4092	nan	0.4092	0.4067
0.0354	19.0	8322	0.0613	0.2007	0.4056	0.4056	nan	0.4056	0.4013
0.0315	20.0	8760	0.0605	0.1865	0.3752	0.3752	nan	0.3752	0.3731
0.0343	21.0	9198	0.0653	0.1991	0.4019	0.4019	nan	0.4019	0.3981
0.0327	22.0	9636	0.0660	0.1945	0.3924	0.3924	nan	0.3924	0.3891
0.0252	22.83	10000	0.0663	0.1943	0.3915	0.3915	nan	0.3915	0.3887

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cpu
Datasets 2.14.6
Tokenizers 0.14.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Nov 8, 2023
Updated: Nov 10, 2023