SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Soft

Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Soft

SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Soft 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 16, 2024
Updated: Jul 17, 2024
SHA: 2a9c1180

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_mit-b5_Clean-Set3-Grayscale_Soft-Augmented

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: 0.0143
Mean Iou: 0.9787
Mean Accuracy: 0.9898
Overall Accuracy: 0.9944
Accuracy Background: 0.9976
Accuracy Melt: 0.9776
Accuracy Substrate: 0.9943
Iou Background: 0.9927
Iou Melt: 0.9518
Iou Substrate: 0.9915

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
0.1578	0.5208	50	0.1042	0.8587	0.9293	0.9602	0.9866	0.8445	0.9568	0.9694	0.6723	0.9344
0.069	1.0417	100	0.0438	0.9400	0.9733	0.9845	0.9890	0.9435	0.9873	0.9817	0.8600	0.9783
0.1295	1.5625	150	0.0535	0.9282	0.9513	0.9807	0.9796	0.8764	0.9979	0.9774	0.8371	0.9700
0.0318	2.0833	200	0.0363	0.9495	0.9759	0.9867	0.9974	0.9462	0.9843	0.9851	0.8841	0.9792
0.041	2.6042	250	0.0290	0.9570	0.9763	0.9893	0.9918	0.9426	0.9946	0.9871	0.8987	0.9851
0.0281	3.125	300	0.0259	0.9628	0.9843	0.9898	0.9958	0.9691	0.9881	0.9859	0.9176	0.9849
0.0276	3.6458	350	0.0206	0.9669	0.9842	0.9917	0.9948	0.9643	0.9936	0.9907	0.9223	0.9878
0.0374	4.1667	400	0.0249	0.9628	0.9772	0.9907	0.9931	0.9421	0.9963	0.9903	0.9127	0.9854
0.056	4.6875	450	0.0197	0.9702	0.9847	0.9924	0.9943	0.9645	0.9952	0.9908	0.9309	0.9889
0.0092	5.2083	500	0.0171	0.9734	0.9889	0.9932	0.9961	0.9775	0.9932	0.9920	0.9384	0.9897
0.0217	5.7292	550	0.0183	0.9720	0.9880	0.9928	0.9968	0.9749	0.9923	0.9916	0.9355	0.9890
0.0185	6.25	600	0.0192	0.9720	0.9834	0.9929	0.9975	0.9580	0.9946	0.9920	0.9350	0.9891
0.0168	6.7708	650	0.0163	0.9748	0.9865	0.9935	0.9963	0.9680	0.9953	0.9920	0.9420	0.9903
0.014	7.2917	700	0.0160	0.9754	0.9850	0.9937	0.9960	0.9621	0.9968	0.9926	0.9431	0.9905
0.0133	7.8125	750	0.0253	0.9704	0.9842	0.9925	0.9963	0.9620	0.9942	0.9920	0.9308	0.9884
0.0103	8.3333	800	0.0168	0.9742	0.9852	0.9934	0.9977	0.9632	0.9946	0.9921	0.9405	0.9900
0.0142	8.8542	850	0.0139	0.9778	0.9868	0.9943	0.9974	0.9669	0.9959	0.9926	0.9494	0.9915
0.0112	9.375	900	0.0152	0.9761	0.9887	0.9938	0.9970	0.9748	0.9942	0.9927	0.9450	0.9905
0.0103	9.8958	950	0.0151	0.9769	0.9870	0.9940	0.9958	0.9687	0.9965	0.9928	0.9469	0.9909
0.0093	10.4167	1000	0.0133	0.9800	0.9898	0.9948	0.9967	0.9767	0.9960	0.9930	0.9546	0.9923
0.0108	10.9375	1050	0.0171	0.9763	0.9878	0.9931	0.9974	0.9733	0.9926	0.9898	0.9500	0.9890
0.0106	11.4583	1100	0.0141	0.9779	0.9880	0.9943	0.9972	0.9712	0.9956	0.9928	0.9494	0.9916
0.0118	11.9792	1150	0.0143	0.9787	0.9898	0.9944	0.9976	0.9776	0.9943	0.9927	0.9518	0.9915

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 16, 2024
Updated: Jul 17, 2024