SegFormer_mit-b5_Clean-Set3_Augmented_Medium

Hasano20/SegFormer_mit-b5_Clean-Set3_Augmented_Medium

SegFormer_mit-b5_Clean-Set3_Augmented_Medium is a machine learning model from Hasano20. 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: Jul 18, 2024
Updated: Jul 18, 2024
SHA: 30236eea

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_mit-b5_Clean-Set3_Augmented_Medium

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

Loss: 0.0205
Mean Iou: 0.9709
Mean Accuracy: 0.9837
Overall Accuracy: 0.9928
Accuracy Background: 0.9979
Accuracy Melt: 0.9593
Accuracy Substrate: 0.9938
Iou Background: 0.9927
Iou Melt: 0.9315
Iou Substrate: 0.9885

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

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
0.1481	0.3968	50	0.1951	0.7748	0.8972	0.9261	0.9808	0.8114	0.8996	0.9583	0.4859	0.8802
0.1364	0.7937	100	0.0745	0.8855	0.9155	0.9727	0.9933	0.7647	0.9884	0.9753	0.7206	0.9604
0.0776	1.1905	150	0.1238	0.8046	0.8351	0.9565	0.9922	0.5172	0.9960	0.9754	0.5059	0.9325
0.0851	1.5873	200	0.0878	0.8651	0.8901	0.9687	0.9923	0.6841	0.9940	0.9791	0.6665	0.9498
0.1893	1.9841	250	0.0602	0.9077	0.9628	0.9766	0.9958	0.9233	0.9693	0.9872	0.7755	0.9602
0.1236	2.3810	300	0.0643	0.9042	0.9455	0.9768	0.9971	0.8609	0.9783	0.9758	0.7669	0.9699
0.0809	2.7778	350	0.0387	0.9408	0.9699	0.9857	0.9964	0.9271	0.9862	0.9895	0.8562	0.9769
0.0357	3.1746	400	0.0364	0.9431	0.9701	0.9860	0.9948	0.9274	0.9880	0.9897	0.8629	0.9767
0.0408	3.5714	450	0.0424	0.9349	0.9815	0.9834	0.9936	0.9745	0.9765	0.9904	0.8434	0.9708
0.0973	3.9683	500	0.0541	0.9172	0.9798	0.9785	0.9941	0.9796	0.9655	0.9903	0.7997	0.9615
0.0274	4.3651	550	0.0256	0.9636	0.9830	0.9904	0.9967	0.9629	0.9894	0.9893	0.9170	0.9844
0.04	4.7619	600	0.0329	0.9482	0.9696	0.9877	0.9977	0.9210	0.9899	0.9888	0.8743	0.9815
0.0301	5.1587	650	0.0247	0.9609	0.9756	0.9905	0.9975	0.9361	0.9933	0.9917	0.9067	0.9844
0.0137	5.5556	700	0.0214	0.9667	0.9779	0.9919	0.9962	0.9414	0.9962	0.9924	0.9206	0.9870
0.019	5.9524	750	0.0243	0.9619	0.9817	0.9907	0.9959	0.9577	0.9915	0.9916	0.9090	0.9851
0.021	6.3492	800	0.0200	0.9678	0.9853	0.9920	0.9972	0.9668	0.9917	0.9926	0.9237	0.9871
0.0156	6.7460	850	0.0211	0.9689	0.9813	0.9924	0.9965	0.9520	0.9954	0.9926	0.9259	0.9880
0.0153	7.1429	900	0.0205	0.9685	0.9842	0.9923	0.9970	0.9626	0.9930	0.9930	0.9249	0.9876
0.0125	7.5397	950	0.0205	0.9709	0.9837	0.9928	0.9979	0.9593	0.9938	0.9927	0.9315	0.9885

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Library: transformers
Type: segformer
License: other
Created: Jul 18, 2024
Updated: Jul 18, 2024