segformer-b0_DsApollo_ILT

ironchanchellor/segformer-b0_DsApollo_ILT

segformer-b0_DsApollo_ILT is a machine learning model from ironchanchellor. Use it for the machine learning 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

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Dec 4, 2023
Updated: Dec 5, 2023
SHA: 32bec75c

Base models

ironchanchellor/segformer-b0_DsA

Model card

From the Hugging Face model README

segformer-b0_DsApollo_ILT

This model is a fine-tuned version of ironchanchellor/segformer-b0_DsA on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0116
Mean Iou: 0.7963
Mean Accuracy: 0.9731
Overall Accuracy: 0.9942
Accuracy Background: nan
Accuracy Haz: 0.9965
Accuracy Matrix: 0.9883
Accuracy Porosity: 0.9077
Accuracy Carbides: 0.9757
Accuracy Substrate: 0.9970
Iou Background: 0.0
Iou Haz: 0.9932
Iou Matrix: 0.9767
Iou Porosity: 0.8590
Iou Carbides: 0.9547
Iou Substrate: 0.9942

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Haz	Accuracy Matrix	Accuracy Porosity	Accuracy Carbides	Accuracy Substrate	Iou Haz	Iou Matrix	Iou Porosity	Iou Carbides	Iou Substrate
0.2652	1.0	591	0.0181	0.7893	0.9731	0.9914	nan	0.9952	0.9836	0.9148	0.9794	0.9926	0.9872	0.9741	0.8340	0.9516	0.9886
0.0167	2.0	1182	0.0196	0.7899	0.9662	0.9920	nan	0.9919	0.9849	0.8780	0.9793	0.9967	0.9884	0.9746	0.8346	0.9522	0.9899
0.0272	3.0	1773	0.0152	0.7895	0.9652	0.9923	nan	0.9970	0.9906	0.8787	0.9672	0.9926	0.9890	0.9753	0.8311	0.9512	0.9903
0.0117	4.0	2364	0.0131	0.7924	0.9663	0.9933	nan	0.9951	0.9902	0.8801	0.9698	0.9966	0.9912	0.9759	0.8425	0.9524	0.9924
0.0176	5.0	2955	0.0160	0.7919	0.9658	0.9930	nan	0.9939	0.9871	0.8731	0.9780	0.9968	0.9903	0.9760	0.8396	0.9536	0.9916
0.0772	6.0	3546	0.0125	0.7928	0.9671	0.9937	nan	0.9949	0.9858	0.8767	0.9804	0.9976	0.9922	0.9760	0.8419	0.9535	0.9933
0.0568	7.0	4137	0.0126	0.7950	0.9697	0.9937	nan	0.9978	0.9895	0.8933	0.9735	0.9946	0.9920	0.9766	0.8540	0.9540	0.9931
0.0228	8.0	4728	0.0158	0.7956	0.9766	0.9934	nan	0.9933	0.9882	0.9283	0.9749	0.9980	0.9911	0.9766	0.8591	0.9543	0.9923
0.0058	9.0	5319	0.0120	0.7960	0.9731	0.9940	nan	0.9958	0.9873	0.9068	0.9782	0.9972	0.9927	0.9766	0.8580	0.9547	0.9938
0.0038	10.0	5910	0.0116	0.7963	0.9731	0.9942	nan	0.9965	0.9883	0.9077	0.9757	0.9970	0.9932	0.9767	0.8590	0.9547	0.9942

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Library: transformers
Type: segformer
License: other
Created: Dec 4, 2023
Updated: Dec 5, 2023