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