segformer-b2-cloth-parse-9

yolo12138/segformer-b2-cloth-parse-9

segformer-b2-cloth-parse-9 is a image segmentation model from yolo12138. 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 mit.

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

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At a glance

Task: Image Segmentation
Library: transformers
License: mit
Model type: segformer
Access: Public
Created: Dec 15, 2023
Updated: Dec 16, 2023
SHA: baa437ef

Base models

mattmdjaga/segformer_b2_clothes

Datasets

cloth_parsing_mix

Spaces

gdfgdgfd/yolo12138-segformer-b2-cloth-parse-9

Model card

From the Hugging Face model README

segformer-b2-cloth-parse-9

This model is a fine-tuned version of mattmdjaga/segformer_b2_clothes on the cloth_parsing_mix dataset. It achieves the following results on the evaluation set:

Loss: 0.0433
Mean Iou: 0.8611
Mean Accuracy: 0.9107
Overall Accuracy: 0.9846
Accuracy Background: 0.9964
Accuracy Upper Torso: 0.9857
Accuracy Left Pants: 0.9654
Accuracy Right Patns: 0.9664
Accuracy Skirts: 0.9065
Accuracy Left Sleeve: 0.9591
Accuracy Right Sleeve: 0.9662
Accuracy Outer Collar: 0.6491
Accuracy Inner Collar: 0.8015
Iou Background: 0.9923
Iou Upper Torso: 0.9655
Iou Left Pants: 0.9017
Iou Right Patns: 0.9085
Iou Skirts: 0.8749
Iou Left Sleeve: 0.9223
Iou Right Sleeve: 0.9289
Iou Outer Collar: 0.5394
Iou Inner Collar: 0.7160

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: 1e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Upper Torso	Accuracy Left Pants	Accuracy Right Patns	Accuracy Skirts	Accuracy Left Sleeve	Accuracy Right Sleeve	Accuracy Outer Collar	Accuracy Inner Collar	Iou Background	Iou Upper Torso	Iou Left Pants	Iou Right Patns	Iou Skirts	Iou Left Sleeve	Iou Right Sleeve	Iou Outer Collar	Iou Inner Collar
0.1054	0.11	500	0.1180	0.7305	0.7971	0.9670	0.9902	0.9720	0.9654	0.9756	0.8036	0.9226	0.9289	0.0716	0.5444	0.9830	0.9234	0.8752	0.8765	0.7370	0.8236	0.8232	0.0703	0.4628
0.1033	0.22	1000	0.0851	0.7862	0.8418	0.9746	0.9924	0.9829	0.9665	0.9653	0.8491	0.9145	0.9226	0.3219	0.6608	0.9866	0.9424	0.8858	0.8875	0.8105	0.8538	0.8614	0.2833	0.5642
0.0944	0.32	1500	0.0713	0.8077	0.8595	0.9773	0.9941	0.9833	0.9566	0.9625	0.8924	0.9094	0.9181	0.4414	0.6774	0.9880	0.9481	0.8937	0.8950	0.8437	0.8668	0.8751	0.3629	0.5958
0.0746	0.43	2000	0.0683	0.8190	0.8770	0.9783	0.9941	0.9796	0.9652	0.9722	0.8656	0.9480	0.9562	0.4882	0.7236	0.9888	0.9497	0.9070	0.9127	0.8306	0.8790	0.8870	0.3945	0.6218
0.0548	0.54	2500	0.0666	0.8187	0.8713	0.9787	0.9951	0.9831	0.9580	0.9606	0.8651	0.9215	0.9453	0.4839	0.7293	0.9893	0.9514	0.8939	0.9006	0.8245	0.8812	0.8964	0.4010	0.6298
0.0728	0.65	3000	0.0591	0.8271	0.8806	0.9804	0.9945	0.9839	0.9624	0.9659	0.8982	0.9399	0.9430	0.4884	0.7493	0.9900	0.9551	0.8940	0.8966	0.8583	0.8930	0.9011	0.4100	0.6458
0.0505	0.75	3500	0.0648	0.8218	0.8745	0.9797	0.9947	0.9847	0.9858	0.9905	0.8402	0.9500	0.9587	0.4480	0.7178	0.9900	0.9534	0.9022	0.9037	0.8223	0.8944	0.9017	0.3881	0.6402
0.0601	0.86	4000	0.0568	0.8415	0.8951	0.9817	0.9952	0.9817	0.9632	0.9640	0.9170	0.9521	0.9541	0.5781	0.7508	0.9903	0.9576	0.9138	0.9199	0.8716	0.9010	0.9106	0.4562	0.6529
0.0438	0.97	4500	0.0569	0.8431	0.8925	0.9815	0.9947	0.9844	0.9764	0.9838	0.8870	0.9492	0.9595	0.5561	0.7416	0.9903	0.9560	0.9287	0.9370	0.8585	0.9000	0.9089	0.4524	0.6559
0.0617	1.08	5000	0.0529	0.8417	0.8933	0.9816	0.9952	0.9841	0.9602	0.9631	0.8922	0.9475	0.9533	0.5797	0.7642	0.9907	0.9571	0.9097	0.9126	0.8488	0.9044	0.9158	0.4687	0.6678
0.0452	1.19	5500	0.0557	0.8351	0.8935	0.9812	0.9949	0.9842	0.9644	0.9667	0.8781	0.9494	0.9604	0.5961	0.7471	0.9906	0.9588	0.8803	0.8885	0.8349	0.9069	0.9169	0.4743	0.6645
0.0571	1.29	6000	0.0551	0.8351	0.8934	0.9810	0.9957	0.9831	0.9652	0.9693	0.8562	0.9593	0.9569	0.5959	0.7586	0.9910	0.9579	0.8842	0.8879	0.8188	0.9084	0.9155	0.4774	0.6749
0.0778	1.4	6500	0.0537	0.8430	0.8994	0.9818	0.9948	0.9839	0.9872	0.9921	0.8702	0.9587	0.9635	0.5790	0.7656	0.9911	0.9579	0.9044	0.9093	0.8458	0.9060	0.9157	0.4760	0.6808
0.0392	1.51	7000	0.0491	0.8503	0.9069	0.9830	0.9954	0.9823	0.9645	0.9666	0.9205	0.9534	0.9599	0.6214	0.7984	0.9916	0.9607	0.9123	0.9139	0.8755	0.9072	0.9180	0.4907	0.6830
0.0376	1.62	7500	0.0514	0.8442	0.9010	0.9819	0.9954	0.9832	0.9652	0.9660	0.8850	0.9525	0.9598	0.6257	0.7762	0.9914	0.9586	0.8944	0.9053	0.8355	0.9104	0.9215	0.4965	0.6838
0.0391	1.73	8000	0.0492	0.8422	0.8993	0.9819	0.9958	0.9836	0.9641	0.9671	0.8692	0.9561	0.9661	0.6159	0.7756	0.9916	0.9596	0.8882	0.8930	0.8338	0.9103	0.9189	0.4982	0.6860
0.0446	1.83	8500	0.0491	0.8515	0.9079	0.9829	0.9960	0.9836	0.9890	0.9913	0.8770	0.9505	0.9631	0.6458	0.7751	0.9916	0.9603	0.9114	0.9161	0.8559	0.9100	0.9217	0.5096	0.6867
0.041	1.94	9000	0.0482	0.8464	0.8978	0.9825	0.9958	0.9848	0.9619	0.9668	0.8822	0.9569	0.9659	0.5961	0.7703	0.9916	0.9602	0.8958	0.9018	0.8438	0.9148	0.9231	0.4966	0.6899
0.0744	2.05	9500	0.0474	0.8523	0.9018	0.9834	0.9961	0.9840	0.9598	0.9633	0.9195	0.9471	0.9644	0.6055	0.7766	0.9919	0.9619	0.9095	0.9125	0.8697	0.9113	0.9238	0.5010	0.6889
0.0433	2.16	10000	0.0471	0.8581	0.9103	0.9842	0.9951	0.9843	0.9617	0.9646	0.9416	0.9549	0.9718	0.6305	0.7879	0.9915	0.9644	0.9100	0.9155	0.8976	0.9145	0.9245	0.5127	0.6920
0.0412	2.26	10500	0.0468	0.8574	0.9042	0.9835	0.9956	0.9848	0.9628	0.9669	0.9023	0.9615	0.9677	0.6115	0.7847	0.9918	0.9601	0.9248	0.9286	0.8656	0.9177	0.9245	0.5073	0.6964
0.0489	2.37	11000	0.0496	0.8511	0.9029	0.9832	0.9956	0.9858	0.9905	0.9948	0.8694	0.9574	0.9654	0.5748	0.7926	0.9921	0.9604	0.9066	0.9086	0.8615	0.9167	0.9228	0.4913	0.7004
0.0388	2.48	11500	0.0450	0.8594	0.9036	0.9849	0.9957	0.9857	0.9621	0.9648	0.9620	0.9493	0.9604	0.5733	0.7793	0.9922	0.9649	0.9155	0.9205	0.9076	0.9138	0.9257	0.4941	0.7002
0.0409	2.59	12000	0.0493	0.8579	0.9124	0.9844	0.9955	0.9853	0.9928	0.9929	0.9083	0.9573	0.9671	0.6288	0.7832	0.9921	0.9651	0.9046	0.9086	0.8842	0.9196	0.9267	0.5175	0.7026
0.0477	2.7	12500	0.0436	0.8610	0.9051	0.9848	0.9957	0.9868	0.9639	0.9675	0.9478	0.9445	0.9590	0.5972	0.7831	0.9919	0.9654	0.9187	0.9251	0.9029	0.9126	0.9253	0.5035	0.7034
0.0488	2.8	13000	0.0450	0.8577	0.9076	0.9842	0.9963	0.9848	0.9712	0.9695	0.9132	0.9493	0.9621	0.6188	0.8026	0.9924	0.9635	0.9095	0.9124	0.8742	0.9172	0.9276	0.5157	0.7065
0.0879	2.91	13500	0.0516	0.8453	0.8949	0.9819	0.9960	0.9867	0.9631	0.9665	0.8325	0.9618	0.9678	0.6033	0.7763	0.9919	0.9574	0.8955	0.9007	0.8088	0.9206	0.9245	0.5069	0.7013
0.0525	3.02	14000	0.0474	0.8521	0.9053	0.9830	0.9959	0.9849	0.9850	0.9925	0.8703	0.9481	0.9597	0.6076	0.8038	0.9923	0.9600	0.9050	0.9099	0.8420	0.9143	0.9263	0.5148	0.7044
0.0455	3.13	14500	0.0435	0.8579	0.9111	0.9842	0.9953	0.9852	0.9646	0.9672	0.9255	0.9569	0.9654	0.6514	0.7888	0.9923	0.9642	0.8971	0.9055	0.8780	0.9182	0.9284	0.5327	0.7046
0.0454	3.24	15000	0.0451	0.8599	0.9161	0.9844	0.9953	0.9858	0.9895	0.9907	0.8944	0.9635	0.9692	0.6643	0.7925	0.9924	0.9645	0.9061	0.9107	0.8803	0.9202	0.9236	0.5356	0.7058
0.0687	3.34	15500	0.0496	0.8482	0.9017	0.9827	0.9959	0.9869	0.9715	0.9676	0.8483	0.9616	0.9672	0.6235	0.7932	0.9922	0.9614	0.8904	0.8909	0.8269	0.9187	0.9218	0.5249	0.7069
0.0555	3.45	16000	0.0445	0.8568	0.9081	0.9838	0.9964	0.9858	0.9649	0.9681	0.8880	0.9585	0.9610	0.6510	0.7995	0.9922	0.9635	0.8996	0.9073	0.8582	0.9230	0.9257	0.5328	0.7093
0.0528	3.56	16500	0.0477	0.8549	0.9053	0.9833	0.9958	0.9875	0.9668	0.9677	0.8740	0.9512	0.9631	0.6512	0.7902	0.9920	0.9618	0.9021	0.9036	0.8486	0.9185	0.9254	0.5348	0.7070
0.043	3.67	17000	0.0439	0.8633	0.9173	0.9849	0.9960	0.9851	0.9860	0.9893	0.9114	0.9555	0.9656	0.6623	0.8046	0.9921	0.9666	0.9083	0.9158	0.8910	0.9197	0.9262	0.5391	0.7111
0.0372	3.77	17500	0.0474	0.8555	0.9039	0.9836	0.9959	0.9876	0.9626	0.9647	0.8818	0.9556	0.9623	0.6393	0.7858	0.9921	0.9623	0.8999	0.9065	0.8526	0.9218	0.9264	0.5299	0.7082
0.0614	3.88	18000	0.0463	0.8564	0.9088	0.9839	0.9959	0.9853	0.9644	0.9662	0.9035	0.9569	0.9638	0.6413	0.8025	0.9921	0.9643	0.8967	0.9020	0.8607	0.9202	0.9276	0.5330	0.7111
0.0413	3.99	18500	0.0453	0.8579	0.9123	0.9841	0.9963	0.9848	0.9794	0.9828	0.8865	0.9613	0.9695	0.6526	0.7977	0.9922	0.9648	0.8991	0.9047	0.8629	0.9221	0.9274	0.5369	0.7112
0.0386	4.1	19000	0.0438	0.8578	0.9109	0.9842	0.9959	0.9844	0.9649	0.9667	0.9154	0.9580	0.9662	0.6408	0.8062	0.9924	0.9644	0.8973	0.9025	0.8683	0.9196	0.9279	0.5340	0.7134
0.0541	4.21	19500	0.0443	0.8577	0.9118	0.9840	0.9957	0.9847	0.9829	0.9872	0.8935	0.9594	0.9686	0.6265	0.8077	0.9921	0.9641	0.9017	0.9079	0.8621	0.9203	0.9277	0.5298	0.7133
0.0409	4.31	20000	0.0433	0.8560	0.9083	0.9840	0.9959	0.9860	0.9670	0.9687	0.9020	0.9578	0.9632	0.6421	0.7918	0.9922	0.9652	0.8921	0.8966	0.8633	0.9206	0.9278	0.5349	0.7117
0.0398	4.42	20500	0.0451	0.8581	0.9102	0.9840	0.9960	0.9859	0.9687	0.9685	0.8885	0.9597	0.9684	0.6554	0.8004	0.9922	0.9638	0.9000	0.9042	0.8595	0.9232	0.9266	0.5395	0.7144
0.038	4.53	21000	0.0464	0.8608	0.9123	0.9843	0.9959	0.9866	0.9885	0.9907	0.8739	0.9616	0.9678	0.6398	0.8056	0.9921	0.9639	0.9088	0.9160	0.8657	0.9238	0.9273	0.5347	0.7150
0.0295	4.64	21500	0.0433	0.8596	0.9094	0.9840	0.9960	0.9864	0.9641	0.9664	0.8985	0.9535	0.9582	0.6581	0.8033	0.9922	0.9633	0.9056	0.9102	0.8619	0.9195	0.9276	0.5408	0.7151
0.0318	4.75	22000	0.0439	0.8600	0.9127	0.9842	0.9964	0.9848	0.9665	0.9676	0.8929	0.9627	0.9689	0.6656	0.8089	0.9923	0.9643	0.9007	0.9080	0.8645	0.9223	0.9283	0.5444	0.7156
0.0377	4.85	22500	0.0429	0.8619	0.9125	0.9846	0.9963	0.9849	0.9633	0.9666	0.9115	0.9609	0.9689	0.6527	0.8069	0.9923	0.9654	0.9052	0.9104	0.8762	0.9217	0.9288	0.5407	0.7166
0.0419	4.96	23000	0.0433	0.8611	0.9107	0.9846	0.9964	0.9857	0.9654	0.9664	0.9065	0.9591	0.9662	0.6491	0.8015	0.9923	0.9655	0.9017	0.9085	0.8749	0.9223	0.9289	0.5394	0.7160

Framework versions

Transformers 4.35.2
Pytorch 2.1.1
Datasets 2.15.0
Tokenizers 0.15.0

Task: Image Segmentation
Library: transformers
Type: segformer
License: mit
Created: Dec 15, 2023
Updated: Dec 16, 2023