results

This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0368

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss
3.4991	0.3137	100	3.6244
3.1728	0.3451	110	3.4319
2.7857	0.3765	120	3.2574
3.0606	0.4078	130	3.1484
2.6704	0.4392	140	3.0390
2.7332	0.4706	150	2.9956
2.8436	0.5020	160	2.9110
2.8464	0.5333	170	2.8551
2.1192	0.5647	180	2.8163
2.6557	0.5961	190	2.8145
2.3224	0.6275	200	2.7858
2.6007	0.6588	210	2.7064
2.6117	0.6902	220	2.6602
2.4549	0.7216	230	2.6368
2.5487	0.7529	240	2.6029
2.6048	0.7843	250	2.5573
2.0348	0.8157	260	2.5203
2.4741	0.8471	270	2.4935
2.5855	0.8784	280	2.4731
2.1076	0.9098	290	2.4283
2.3073	0.9412	300	2.3896
2.214	0.9725	310	2.3919
2.2078	1.0039	320	2.3343
2.2391	1.0353	330	2.2970
2.3607	1.0667	340	2.2921
2.0244	1.0980	350	2.2751
2.251	1.1294	360	2.2713
2.1133	1.1608	370	2.2701
2.124	1.1922	380	2.2618
2.1989	1.2235	390	2.2429
2.2315	1.2549	400	2.2463
2.2398	1.2863	410	2.2386
2.261	1.3176	420	2.2360
2.2144	1.3490	430	2.2427
2.3344	1.3804	440	2.2452
2.0412	1.4118	450	2.2092
2.0854	1.4431	460	2.2197
2.1636	1.4745	470	2.1830
1.7776	1.5059	480	2.1904
2.1118	1.5373	490	2.2194
2.1203	1.5686	500	2.1978
2.2468	1.6	510	2.1968
2.2992	1.6314	520	2.1963
2.2596	1.6627	530	2.1816
2.1836	1.6941	540	2.1800
2.2672	1.7255	550	2.1679
2.0702	1.7569	560	2.1607
2.5606	1.7882	570	2.1568
2.1392	1.8196	580	2.1578
1.9255	1.8510	590	2.1799
2.0995	1.8824	600	2.1995
2.1153	1.9137	610	2.1741
2.2068	1.9451	620	2.1638
1.8698	1.9765	630	2.1819
1.8849	2.0078	640	2.1807
2.0291	2.0392	650	2.1636
2.2092	2.0706	660	2.1356
2.1117	2.1020	670	2.1682
1.8318	2.1333	680	2.1719
1.9884	2.1647	690	2.2114
2.1933	2.1961	700	2.1526
2.2953	2.2275	710	2.1525
2.2841	2.2588	720	2.1417
1.9865	2.2902	730	2.1399
1.9193	2.3216	740	2.1313
1.8882	2.3529	750	2.1362
1.8967	2.3843	760	2.1454
1.9424	2.4157	770	2.1356
1.8531	2.4471	780	2.1340
1.9435	2.4784	790	2.1413
2.0455	2.5098	800	2.1558
1.9384	2.5412	810	2.1519
2.0826	2.5725	820	2.1381
2.0008	2.6039	830	2.1136
1.922	2.6353	840	2.1160
1.9567	2.6667	850	2.0991
2.2798	2.6980	860	2.0998
2.4014	2.7294	870	2.0922
2.3427	2.7608	880	2.0976
2.2701	2.7922	890	2.0823
2.1405	2.8235	900	2.1009
1.9259	2.8549	910	2.1075
2.0055	2.8863	920	2.1041
1.9902	2.9176	930	2.0854
1.9821	2.9490	940	2.1107
2.0292	2.9804	950	2.0901
1.9811	3.0118	960	2.1227
2.2674	3.0431	970	2.0934
2.0632	3.0745	980	2.0935
2.1232	3.1059	990	2.0843
2.0056	3.1373	1000	2.0891
2.0188	3.1686	1010	2.0811
2.0898	3.2	1020	2.0848
2.1809	3.2314	1030	2.0883
2.1636	3.2627	1040	2.0931
1.9941	3.2941	1050	2.0894
1.9761	3.3255	1060	2.0957
1.9908	3.3569	1070	2.0715
2.0806	3.3882	1080	2.0774
1.9419	3.4196	1090	2.0713
1.8643	3.4510	1100	2.0654
1.969	3.4824	1110	2.0636
2.0104	3.5137	1120	2.0710
1.6745	3.5451	1130	2.0551
2.047	3.5765	1140	2.0598
2.1289	3.6078	1150	2.0426
2.1158	3.6392	1160	2.0525
1.8543	3.6706	1170	2.0515
2.0206	3.7020	1180	2.0508
2.1992	3.7333	1190	2.0485
1.6875	3.7647	1200	2.0558
1.8452	3.7961	1210	2.0543
2.2061	3.8275	1220	2.0594
2.0418	3.8588	1230	2.0652
2.0411	3.8902	1240	2.0679
2.0835	3.9216	1250	2.0731
1.9003	3.9529	1260	2.0574
1.7881	3.9843	1270	2.0777
2.1354	4.0157	1280	2.0630
1.8935	4.0471	1290	2.0607
2.1067	4.0784	1300	2.0576
1.8225	4.1098	1310	2.0767
1.8132	4.1412	1320	2.0507
1.985	4.1725	1330	2.0669
2.112	4.2039	1340	2.0836
1.7993	4.2353	1350	2.0718
1.9784	4.2667	1360	2.0676
2.1628	4.2980	1370	2.0525
1.876	4.3294	1380	2.0615
2.0081	4.3608	1390	2.0736
1.8642	4.3922	1400	2.0565
1.9308	4.4235	1410	2.0608
2.2296	4.4549	1420	2.0553
2.0166	4.4863	1430	2.0575
2.0422	4.5176	1440	2.0543
1.8729	4.5490	1450	2.0552
2.0323	4.5804	1460	2.0656
1.9935	4.6118	1470	2.0794
1.8534	4.6431	1480	2.0685
1.8363	4.6745	1490	2.0581
1.9679	4.7059	1500	2.0353
1.8585	4.7373	1510	2.0334
1.9772	4.7686	1520	2.0420
1.8753	4.8	1530	2.0427
1.8911	4.8314	1540	2.0499
2.0614	4.8627	1550	2.0481
2.1184	4.8941	1560	2.0481
1.9504	4.9255	1570	2.0541
2.1337	4.9569	1580	2.0480
2.4391	4.9882	1590	2.0416
1.72	5.0196	1600	2.0412
2.0808	5.0510	1610	2.0458
1.8639	5.0824	1620	2.0438
1.9462	5.1137	1630	2.0428
2.0055	5.1451	1640	2.0366
2.0345	5.1765	1650	2.0644
1.9321	5.2078	1660	2.0454
1.8705	5.2392	1670	2.0394
2.0345	5.2706	1680	2.0475
1.9992	5.3020	1690	2.0567
2.2208	5.3333	1700	2.0558
1.8253	5.3647	1710	2.0413
2.0765	5.3961	1720	2.0319
2.2315	5.4275	1730	2.0360
2.2432	5.4588	1740	2.0436
2.0666	5.4902	1750	2.0451
2.0603	5.5216	1760	2.0296
1.6625	5.5529	1770	2.0513
2.0946	5.5843	1780	2.0306
1.9464	5.6157	1790	2.0315
2.0183	5.6471	1800	2.0276
2.0794	5.6784	1810	2.0512
2.0289	5.7098	1820	2.0369
2.1014	5.7412	1830	2.0520
1.9159	5.7725	1840	2.0491
2.2446	5.8039	1850	2.0508
1.9383	5.8353	1860	2.0327
2.0132	5.8667	1870	2.0161
2.2234	5.8980	1880	2.0406
2.2556	5.9294	1890	2.0365
2.2061	5.9608	1900	2.0314
1.7465	5.9922	1910	2.0543
1.9388	6.0235	1920	2.0525
1.9223	6.0549	1930	2.0325
1.9386	6.0863	1940	2.0282
1.9171	6.1176	1950	2.0462
1.9319	6.1490	1960	2.0369
1.7689	6.1804	1970	2.0364
2.0063	6.2118	1980	2.0388
2.1053	6.2431	1990	2.0346
2.1074	6.2745	2000	2.0368

Framework versions

Transformers 4.41.2
Pytorch 2.3.1
Datasets 2.19.2
Tokenizers 0.19.1

amaye15/results

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种