segformerSAAD2

This model is a fine-tuned version of nvidia/mit-b0 on the Saad287/SixGUN dataset. It achieves the following results on the evaluation set:

• Loss: 0.0658
• Mean Iou: 0.8885
• Mean Accuracy: 0.9302
• Overall Accuracy: 0.9934
• Accuracy Bkg: 0.9977
• Accuracy Knife: 0.8700
• Accuracy Gun: 0.9228
• Iou Bkg: 0.9940
• Iou Knife: 0.8345
• Iou Gun: 0.8370

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: 23
• eval_batch_size: 23
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Bkg	Accuracy Knife	Accuracy Gun	Iou Bkg	Iou Knife	Iou Gun
0.9085	10.0	10	1.0050	0.5155	0.9279	0.9160	0.9152	0.9126	0.9559	0.9144	0.3135	0.3185
0.7013	20.0	20	0.8010	0.6030	0.8080	0.9590	0.9692	0.6715	0.7832	0.9590	0.4442	0.4060
0.5991	30.0	30	0.6384	0.6286	0.7939	0.9648	0.9765	0.6946	0.7107	0.9647	0.5156	0.4054
0.5105	40.0	40	0.5393	0.6328	0.8246	0.9632	0.9727	0.7618	0.7393	0.9631	0.5383	0.3970
0.4642	50.0	50	0.4625	0.6478	0.8186	0.9668	0.9769	0.7866	0.6922	0.9666	0.5662	0.4108
0.4301	60.0	60	0.4175	0.6580	0.8092	0.9696	0.9805	0.7819	0.6650	0.9693	0.5757	0.4291
0.3623	70.0	70	0.3713	0.7008	0.8249	0.9758	0.9861	0.7827	0.7061	0.9757	0.6257	0.5010
0.3356	80.0	80	0.3247	0.7648	0.8740	0.9825	0.9899	0.8066	0.8254	0.9827	0.6695	0.6422
0.2975	90.0	90	0.2862	0.7936	0.8754	0.9857	0.9932	0.8001	0.8330	0.9860	0.6986	0.6962
0.2722	100.0	100	0.2656	0.8099	0.8962	0.9869	0.9931	0.8202	0.8753	0.9873	0.7126	0.7296
0.2465	110.0	110	0.2377	0.8192	0.8904	0.9880	0.9946	0.8170	0.8597	0.9884	0.7251	0.7440
0.2221	120.0	120	0.2163	0.8311	0.9018	0.9889	0.9949	0.8265	0.8840	0.9895	0.7391	0.7648
0.2082	130.0	130	0.2007	0.8341	0.8971	0.9892	0.9955	0.8297	0.8662	0.9896	0.7500	0.7628
0.1974	140.0	140	0.1928	0.8462	0.9026	0.9901	0.9961	0.8390	0.8729	0.9906	0.7685	0.7795
0.1823	150.0	150	0.1720	0.8464	0.8974	0.9903	0.9966	0.8324	0.8632	0.9907	0.7710	0.7775
0.1743	160.0	160	0.1598	0.8533	0.9014	0.9908	0.9969	0.8307	0.8766	0.9913	0.7799	0.7888
0.1577	170.0	170	0.1560	0.8592	0.9150	0.9911	0.9963	0.8514	0.8973	0.9917	0.7892	0.7967
0.1468	180.0	180	0.1432	0.8632	0.9079	0.9915	0.9972	0.8406	0.8858	0.9920	0.7964	0.8012
0.1397	190.0	190	0.1323	0.8641	0.9128	0.9915	0.9969	0.8503	0.8911	0.9920	0.7993	0.8009
0.1305	200.0	200	0.1264	0.8659	0.9102	0.9917	0.9972	0.8468	0.8867	0.9922	0.8024	0.8032
0.1228	210.0	210	0.1209	0.8730	0.9188	0.9922	0.9972	0.8552	0.9038	0.9927	0.8119	0.8144
0.1179	220.0	220	0.1123	0.8730	0.9172	0.9922	0.9973	0.8527	0.9016	0.9928	0.8128	0.8133
0.1126	230.0	230	0.1078	0.8742	0.9203	0.9923	0.9972	0.8568	0.9069	0.9929	0.8148	0.8149
0.1066	240.0	240	0.1029	0.8758	0.9216	0.9924	0.9972	0.8587	0.9090	0.9930	0.8176	0.8169
0.1026	250.0	250	0.0978	0.8786	0.9224	0.9926	0.9974	0.8648	0.9050	0.9931	0.8230	0.8197
0.1012	260.0	260	0.0963	0.8797	0.9271	0.9927	0.9972	0.8659	0.9182	0.9933	0.8242	0.8217
0.0952	270.0	270	0.0926	0.8800	0.9229	0.9927	0.9975	0.8607	0.9104	0.9933	0.8250	0.8218
0.0938	280.0	280	0.0895	0.8819	0.9238	0.9929	0.9975	0.8649	0.9090	0.9934	0.8284	0.8237
0.0893	290.0	290	0.0860	0.8832	0.9260	0.9930	0.9975	0.8686	0.9120	0.9935	0.8301	0.8261
0.0868	300.0	300	0.0835	0.8831	0.9243	0.9930	0.9976	0.8646	0.9106	0.9935	0.8301	0.8257
0.0853	310.0	310	0.0820	0.8834	0.9257	0.9930	0.9975	0.8664	0.9132	0.9936	0.8307	0.8260
0.0817	320.0	320	0.0786	0.8867	0.9291	0.9932	0.9976	0.8707	0.9190	0.9938	0.8343	0.8319
0.0823	330.0	330	0.0785	0.8851	0.9261	0.9931	0.9977	0.8677	0.9129	0.9937	0.8326	0.8291
0.0858	340.0	340	0.0768	0.8860	0.9272	0.9932	0.9976	0.8680	0.9159	0.9938	0.8330	0.8312
0.078	350.0	350	0.0744	0.8853	0.9253	0.9931	0.9977	0.8648	0.9133	0.9937	0.8315	0.8308
0.0755	360.0	360	0.0739	0.8872	0.9290	0.9933	0.9976	0.8694	0.9201	0.9939	0.8337	0.8342
0.0752	370.0	370	0.0727	0.8880	0.9307	0.9933	0.9976	0.8721	0.9224	0.9939	0.8351	0.8349
0.0727	380.0	380	0.0719	0.8858	0.9269	0.9932	0.9977	0.8665	0.9166	0.9938	0.8318	0.8318
0.0725	390.0	390	0.0709	0.8873	0.9298	0.9933	0.9976	0.8705	0.9215	0.9939	0.8347	0.8333
0.0725	400.0	400	0.0695	0.8870	0.9283	0.9933	0.9977	0.8686	0.9188	0.9939	0.8331	0.8340
0.0718	410.0	410	0.0689	0.8890	0.9309	0.9934	0.9976	0.8706	0.9244	0.9940	0.8351	0.8378
0.0695	420.0	420	0.0679	0.8872	0.9284	0.9933	0.9977	0.8689	0.9186	0.9939	0.8335	0.8341
0.0707	430.0	430	0.0676	0.8881	0.9307	0.9933	0.9976	0.8686	0.9258	0.9940	0.8334	0.8370
0.0684	440.0	440	0.0668	0.8883	0.9298	0.9934	0.9977	0.8693	0.9225	0.9940	0.8339	0.8372
0.0692	450.0	450	0.0668	0.8887	0.9310	0.9934	0.9976	0.8708	0.9245	0.9940	0.8350	0.8372
0.0681	460.0	460	0.0661	0.8889	0.9296	0.9934	0.9977	0.8696	0.9216	0.9940	0.8344	0.8382
0.0688	470.0	470	0.0661	0.8888	0.9305	0.9934	0.9977	0.8708	0.9230	0.9940	0.8352	0.8372
0.0675	480.0	480	0.0659	0.8888	0.9307	0.9934	0.9976	0.8717	0.9228	0.9940	0.8353	0.8370
0.0679	490.0	490	0.0659	0.8886	0.9300	0.9934	0.9977	0.8708	0.9214	0.9940	0.8352	0.8367
0.0668	500.0	500	0.0658	0.8885	0.9302	0.9934	0.9977	0.8700	0.9228	0.9940	0.8345	0.8370

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1