detr-resnet-50_finetuned_cppe5

This model is a fine-tuned version of facebook/detr-resnet-50 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1397

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: 4.941058844013093e-07
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • num_epochs: 1000

Training results

Training Loss Epoch Step Validation Loss
No log 1.0 10 7.0795
No log 2.0 20 5.7431
No log 3.0 30 6.7530
No log 4.0 40 5.3270
5.8012 5.0 50 6.1804
5.8012 6.0 60 6.0216
5.8012 7.0 70 5.2871
5.8012 8.0 80 5.1623
5.8012 9.0 90 6.9306
5.1916 10.0 100 5.7015
5.1916 11.0 110 7.5914
5.1916 12.0 120 6.0398
5.1916 13.0 130 5.3490
5.1916 14.0 140 5.8373
5.4281 15.0 150 4.1271
5.4281 16.0 160 6.2398
5.4281 17.0 170 4.2083
5.4281 18.0 180 4.7579
5.4281 19.0 190 5.2079
4.8531 20.0 200 3.9074
4.8531 21.0 210 5.1759
4.8531 22.0 220 3.8891
4.8531 23.0 230 3.8469
4.8531 24.0 240 4.0659
4.6505 25.0 250 4.7758
4.6505 26.0 260 4.2206
4.6505 27.0 270 4.8899
4.6505 28.0 280 4.6572
4.6505 29.0 290 4.1456
4.7175 30.0 300 4.3631
4.7175 31.0 310 3.9849
4.7175 32.0 320 5.4221
4.7175 33.0 330 3.7069
4.7175 34.0 340 5.2221
4.3464 35.0 350 4.0269
4.3464 36.0 360 3.8471
4.3464 37.0 370 4.0817
4.3464 38.0 380 4.0005
4.3464 39.0 390 3.7973
4.4884 40.0 400 4.1382
4.4884 41.0 410 3.9224
4.4884 42.0 420 4.3754
4.4884 43.0 430 3.9821
4.4884 44.0 440 3.9350
4.2526 45.0 450 4.0074
4.2526 46.0 460 3.6133
4.2526 47.0 470 3.8681
4.2526 48.0 480 3.9367
4.2526 49.0 490 3.8197
4.1344 50.0 500 3.5646
4.1344 51.0 510 3.7987
4.1344 52.0 520 3.9491
4.1344 53.0 530 4.1457
4.1344 54.0 540 3.6863
4.0492 55.0 550 3.8259
4.0492 56.0 560 3.8122
4.0492 57.0 570 4.0111
4.0492 58.0 580 3.7859
4.0492 59.0 590 3.7566
3.9168 60.0 600 3.6876
3.9168 61.0 610 3.7469
3.9168 62.0 620 4.0203
3.9168 63.0 630 4.7051
3.9168 64.0 640 3.6666
3.8979 65.0 650 3.5877
3.8979 66.0 660 3.5737
3.8979 67.0 670 3.6520
3.8979 68.0 680 3.4342
3.8979 69.0 690 3.9668
3.7801 70.0 700 4.0617
3.7801 71.0 710 3.8625
3.7801 72.0 720 3.3205
3.7801 73.0 730 4.0774
3.7801 74.0 740 4.0416
3.7303 75.0 750 3.4343
3.7303 76.0 760 3.5131
3.7303 77.0 770 3.5507
3.7303 78.0 780 3.9112
3.7303 79.0 790 3.3022
3.5806 80.0 800 3.8618
3.5806 81.0 810 3.6697
3.5806 82.0 820 3.5536
3.5806 83.0 830 3.3500
3.5806 84.0 840 3.8134
3.6183 85.0 850 3.4067
3.6183 86.0 860 3.4425
3.6183 87.0 870 3.2812
3.6183 88.0 880 3.3909
3.6183 89.0 890 3.6878
3.4767 90.0 900 3.5409
3.4767 91.0 910 3.5380
3.4767 92.0 920 3.8982
3.4767 93.0 930 3.4205
3.4767 94.0 940 4.0828
3.5444 95.0 950 3.2579
3.5444 96.0 960 3.3702
3.5444 97.0 970 4.2833
3.5444 98.0 980 3.4222
3.5444 99.0 990 3.4477
3.3821 100.0 1000 3.2484
3.3821 101.0 1010 3.3493
3.3821 102.0 1020 3.2192
3.3821 103.0 1030 3.2491
3.3821 104.0 1040 3.3853
3.3429 105.0 1050 3.4362
3.3429 106.0 1060 4.1587
3.3429 107.0 1070 3.9797
3.3429 108.0 1080 3.6257
3.3429 109.0 1090 3.4861
3.304 110.0 1100 3.3520
3.304 111.0 1110 3.0047
3.304 112.0 1120 3.4988
3.304 113.0 1130 3.4723
3.304 114.0 1140 3.4294
3.2826 115.0 1150 3.6923
3.2826 116.0 1160 3.2513
3.2826 117.0 1170 3.6769
3.2826 118.0 1180 3.5384
3.2826 119.0 1190 3.3773
3.1944 120.0 1200 3.2538
3.1944 121.0 1210 3.2896
3.1944 122.0 1220 3.4226
3.1944 123.0 1230 3.3085
3.1944 124.0 1240 3.1047
3.1978 125.0 1250 3.3142
3.1978 126.0 1260 3.4432
3.1978 127.0 1270 2.9309
3.1978 128.0 1280 3.3678
3.1978 129.0 1290 3.6156
3.3425 130.0 1300 3.3015
3.3425 131.0 1310 3.3181
3.3425 132.0 1320 3.2688
3.3425 133.0 1330 3.4590
3.3425 134.0 1340 3.0809
3.3654 135.0 1350 3.0907
3.3654 136.0 1360 3.2888
3.3654 137.0 1370 3.1504
3.3654 138.0 1380 3.4285
3.3654 139.0 1390 3.4080
3.1702 140.0 1400 3.1543
3.1702 141.0 1410 3.5154
3.1702 142.0 1420 3.1132
3.1702 143.0 1430 3.2503
3.1702 144.0 1440 3.6848
3.1929 145.0 1450 3.1961
3.1929 146.0 1460 3.4146
3.1929 147.0 1470 3.4162
3.1929 148.0 1480 3.2388
3.1929 149.0 1490 3.6281
3.0 150.0 1500 2.9830
3.0 151.0 1510 3.1817
3.0 152.0 1520 3.2862
3.0 153.0 1530 3.0465
3.0 154.0 1540 3.1208
2.9975 155.0 1550 3.3041
2.9975 156.0 1560 3.4944
2.9975 157.0 1570 3.5826
2.9975 158.0 1580 3.5453
2.9975 159.0 1590 4.0256
3.0312 160.0 1600 3.3678
3.0312 161.0 1610 2.9384
3.0312 162.0 1620 3.0596
3.0312 163.0 1630 3.3952
3.0312 164.0 1640 3.5299
2.9855 165.0 1650 2.9930
2.9855 166.0 1660 3.3869
2.9855 167.0 1670 3.1676
2.9855 168.0 1680 3.1330
2.9855 169.0 1690 3.2595
2.863 170.0 1700 3.1151
2.863 171.0 1710 3.1382
2.863 172.0 1720 3.7265
2.863 173.0 1730 2.8716
2.863 174.0 1740 3.0285
2.8942 175.0 1750 3.0285
2.8942 176.0 1760 3.7873
2.8942 177.0 1770 2.9266
2.8942 178.0 1780 2.9751
2.8942 179.0 1790 3.1875
2.7614 180.0 1800 2.6317
2.7614 181.0 1810 3.3780
2.7614 182.0 1820 3.1680
2.7614 183.0 1830 3.3270
2.7614 184.0 1840 3.2822
2.9 185.0 1850 3.0026
2.9 186.0 1860 3.0610
2.9 187.0 1870 3.2631
2.9 188.0 1880 2.8804
2.9 189.0 1890 3.2069
2.9176 190.0 1900 2.8339
2.9176 191.0 1910 2.9836
2.9176 192.0 1920 3.0211
2.9176 193.0 1930 2.8448
2.9176 194.0 1940 4.1654
2.8189 195.0 1950 3.0910
2.8189 196.0 1960 2.7972
2.8189 197.0 1970 3.5421
2.8189 198.0 1980 2.8334
2.8189 199.0 1990 3.0457
2.7236 200.0 2000 3.0531
2.7236 201.0 2010 3.0384
2.7236 202.0 2020 3.0183
2.7236 203.0 2030 3.1019
2.7236 204.0 2040 2.6909
2.6289 205.0 2050 2.8969
2.6289 206.0 2060 2.8063
2.6289 207.0 2070 3.3533
2.6289 208.0 2080 3.0578
2.6289 209.0 2090 3.0081
2.6592 210.0 2100 3.1674
2.6592 211.0 2110 3.0982
2.6592 212.0 2120 2.9070
2.6592 213.0 2130 2.8881
2.6592 214.0 2140 2.7869
2.6898 215.0 2150 2.9736
2.6898 216.0 2160 2.7309
2.6898 217.0 2170 3.2656
2.6898 218.0 2180 2.7734
2.6898 219.0 2190 2.6135
2.6117 220.0 2200 3.0652
2.6117 221.0 2210 3.0918
2.6117 222.0 2220 3.2191
2.6117 223.0 2230 2.8947
2.6117 224.0 2240 2.6307
2.6281 225.0 2250 2.6585
2.6281 226.0 2260 3.0801
2.6281 227.0 2270 2.9075
2.6281 228.0 2280 3.1795
2.6281 229.0 2290 2.8762
2.4503 230.0 2300 2.6883
2.4503 231.0 2310 3.0329
2.4503 232.0 2320 2.8990
2.4503 233.0 2330 2.7381
2.4503 234.0 2340 2.8102
2.5171 235.0 2350 3.0730
2.5171 236.0 2360 2.9376
2.5171 237.0 2370 2.5781
2.5171 238.0 2380 2.9466
2.5171 239.0 2390 2.6868
2.5004 240.0 2400 2.6414
2.5004 241.0 2410 3.0623
2.5004 242.0 2420 2.8071
2.5004 243.0 2430 2.4406
2.5004 244.0 2440 2.6247
2.5338 245.0 2450 2.7334
2.5338 246.0 2460 2.8576
2.5338 247.0 2470 2.6042
2.5338 248.0 2480 2.8519
2.5338 249.0 2490 3.0416
2.429 250.0 2500 2.7010
2.429 251.0 2510 4.0268
2.429 252.0 2520 2.9236
2.429 253.0 2530 2.5467
2.429 254.0 2540 2.7355
2.4368 255.0 2550 3.1205
2.4368 256.0 2560 2.8335
2.4368 257.0 2570 2.7752
2.4368 258.0 2580 2.7598
2.4368 259.0 2590 2.6409
2.3204 260.0 2600 2.7808
2.3204 261.0 2610 2.4784
2.3204 262.0 2620 2.9005
2.3204 263.0 2630 2.6729
2.3204 264.0 2640 2.6290
2.4044 265.0 2650 2.8760
2.4044 266.0 2660 2.5683
2.4044 267.0 2670 2.8607
2.4044 268.0 2680 2.5760
2.4044 269.0 2690 2.6616
2.3464 270.0 2700 2.6968
2.3464 271.0 2710 2.7200
2.3464 272.0 2720 2.7963
2.3464 273.0 2730 2.5230
2.3464 274.0 2740 2.7015
2.2999 275.0 2750 2.9836
2.2999 276.0 2760 2.6443
2.2999 277.0 2770 2.5045
2.2999 278.0 2780 3.2068
2.2999 279.0 2790 2.5038
2.3102 280.0 2800 2.7581
2.3102 281.0 2810 2.6092
2.3102 282.0 2820 2.4482
2.3102 283.0 2830 3.0941
2.3102 284.0 2840 2.3476
2.2134 285.0 2850 2.8535
2.2134 286.0 2860 2.6361
2.2134 287.0 2870 2.6033
2.2134 288.0 2880 2.4526
2.2134 289.0 2890 2.7966
2.3276 290.0 2900 2.6472
2.3276 291.0 2910 2.6410
2.3276 292.0 2920 2.5670
2.3276 293.0 2930 2.7832
2.3276 294.0 2940 2.5031
2.287 295.0 2950 2.5614
2.287 296.0 2960 3.0045
2.287 297.0 2970 2.5755
2.287 298.0 2980 2.5132
2.287 299.0 2990 2.6427
2.1723 300.0 3000 3.2675
2.1723 301.0 3010 2.5890
2.1723 302.0 3020 2.7935
2.1723 303.0 3030 2.5836
2.1723 304.0 3040 2.4359
2.237 305.0 3050 2.7048
2.237 306.0 3060 2.4640
2.237 307.0 3070 2.5528
2.237 308.0 3080 2.4919
2.237 309.0 3090 2.5067
2.1502 310.0 3100 2.6569
2.1502 311.0 3110 2.6649
2.1502 312.0 3120 2.7721
2.1502 313.0 3130 2.3934
2.1502 314.0 3140 2.4799
2.2248 315.0 3150 2.6882
2.2248 316.0 3160 2.8493
2.2248 317.0 3170 2.5919
2.2248 318.0 3180 2.4124
2.2248 319.0 3190 2.5997
2.2399 320.0 3200 2.3440
2.2399 321.0 3210 2.6292
2.2399 322.0 3220 3.2851
2.2399 323.0 3230 2.4422
2.2399 324.0 3240 2.3866
2.1759 325.0 3250 2.4307
2.1759 326.0 3260 2.2842
2.1759 327.0 3270 2.5418
2.1759 328.0 3280 2.5840
2.1759 329.0 3290 2.9884
2.2557 330.0 3300 2.5096
2.2557 331.0 3310 3.2382
2.2557 332.0 3320 2.5237
2.2557 333.0 3330 2.4346
2.2557 334.0 3340 2.4034
2.216 335.0 3350 2.4259
2.216 336.0 3360 2.4239
2.216 337.0 3370 2.5417
2.216 338.0 3380 2.7757
2.216 339.0 3390 2.6264
2.2112 340.0 3400 2.6611
2.2112 341.0 3410 2.6828
2.2112 342.0 3420 2.4541
2.2112 343.0 3430 2.4426
2.2112 344.0 3440 2.4566
2.1473 345.0 3450 2.8140
2.1473 346.0 3460 2.3079
2.1473 347.0 3470 2.4263
2.1473 348.0 3480 2.4176
2.1473 349.0 3490 2.5132
2.0273 350.0 3500 2.5695
2.0273 351.0 3510 2.3300
2.0273 352.0 3520 2.3673
2.0273 353.0 3530 2.4108
2.0273 354.0 3540 2.1937
1.9724 355.0 3550 2.4282
1.9724 356.0 3560 2.5854
1.9724 357.0 3570 2.3549
1.9724 358.0 3580 2.7288
1.9724 359.0 3590 2.3138
2.1214 360.0 3600 2.6228
2.1214 361.0 3610 2.5202
2.1214 362.0 3620 2.3395
2.1214 363.0 3630 2.7839
2.1214 364.0 3640 2.3686
2.0616 365.0 3650 2.1838
2.0616 366.0 3660 2.1441
2.0616 367.0 3670 2.3893
2.0616 368.0 3680 2.3090
2.0616 369.0 3690 2.5005
2.0561 370.0 3700 2.5149
2.0561 371.0 3710 2.4185
2.0561 372.0 3720 2.2988
2.0561 373.0 3730 2.5609
2.0561 374.0 3740 2.4859
1.9504 375.0 3750 2.6781
1.9504 376.0 3760 2.4028
1.9504 377.0 3770 2.2976
1.9504 378.0 3780 2.6518
1.9504 379.0 3790 2.4606
1.9662 380.0 3800 2.0894
1.9662 381.0 3810 2.7766
1.9662 382.0 3820 2.6676
1.9662 383.0 3830 2.3832
1.9662 384.0 3840 2.3459
2.007 385.0 3850 2.5191
2.007 386.0 3860 2.5370
2.007 387.0 3870 2.3437
2.007 388.0 3880 2.5367
2.007 389.0 3890 2.3221
1.9401 390.0 3900 2.2395
1.9401 391.0 3910 2.3589
1.9401 392.0 3920 2.3799
1.9401 393.0 3930 2.3295
1.9401 394.0 3940 2.6330
1.9375 395.0 3950 2.4340
1.9375 396.0 3960 2.5184
1.9375 397.0 3970 2.1730
1.9375 398.0 3980 2.2300
1.9375 399.0 3990 2.4796
1.9703 400.0 4000 2.2612
1.9703 401.0 4010 2.3175
1.9703 402.0 4020 2.5344
1.9703 403.0 4030 2.1123
1.9703 404.0 4040 2.2479
1.8652 405.0 4050 2.6316
1.8652 406.0 4060 2.1574
1.8652 407.0 4070 2.4231
1.8652 408.0 4080 2.1255
1.8652 409.0 4090 2.2994
1.9834 410.0 4100 2.3541
1.9834 411.0 4110 2.3113
1.9834 412.0 4120 2.3966
1.9834 413.0 4130 2.3865
1.9834 414.0 4140 3.0955
1.976 415.0 4150 2.6212
1.976 416.0 4160 2.3237
1.976 417.0 4170 3.3010
1.976 418.0 4180 2.7378
1.976 419.0 4190 2.4063
1.9165 420.0 4200 2.9853
1.9165 421.0 4210 2.0776
1.9165 422.0 4220 2.3036
1.9165 423.0 4230 2.1934
1.9165 424.0 4240 2.1535
1.9224 425.0 4250 2.3000
1.9224 426.0 4260 2.6858
1.9224 427.0 4270 2.4825
1.9224 428.0 4280 2.4776
1.9224 429.0 4290 2.2042
1.9091 430.0 4300 2.2847
1.9091 431.0 4310 2.0935
1.9091 432.0 4320 2.6040
1.9091 433.0 4330 2.2520
1.9091 434.0 4340 2.5126
1.9543 435.0 4350 2.3081
1.9543 436.0 4360 2.5018
1.9543 437.0 4370 2.4462
1.9543 438.0 4380 2.1927
1.9543 439.0 4390 2.1584
1.7975 440.0 4400 2.2996
1.7975 441.0 4410 2.2288
1.7975 442.0 4420 2.4102
1.7975 443.0 4430 2.3321
1.7975 444.0 4440 1.9341
1.9595 445.0 4450 2.1064
1.9595 446.0 4460 2.4024
1.9595 447.0 4470 2.1377
1.9595 448.0 4480 2.2580
1.9595 449.0 4490 2.2505
1.8746 450.0 4500 2.3562
1.8746 451.0 4510 2.2730
1.8746 452.0 4520 2.1447
1.8746 453.0 4530 2.2458
1.8746 454.0 4540 2.2136
2.0722 455.0 4550 2.1459
2.0722 456.0 4560 1.9991
2.0722 457.0 4570 2.1572
2.0722 458.0 4580 2.2700
2.0722 459.0 4590 2.3094
1.9179 460.0 4600 2.2721
1.9179 461.0 4610 2.2809
1.9179 462.0 4620 2.4517
1.9179 463.0 4630 2.2500
1.9179 464.0 4640 2.2107
2.0428 465.0 4650 2.1489
2.0428 466.0 4660 2.2571
2.0428 467.0 4670 2.2047
2.0428 468.0 4680 2.5041
2.0428 469.0 4690 2.2354
1.8738 470.0 4700 2.0811
1.8738 471.0 4710 2.1300
1.8738 472.0 4720 2.3041
1.8738 473.0 4730 2.1780
1.8738 474.0 4740 2.0481
1.8625 475.0 4750 2.2354
1.8625 476.0 4760 2.1670
1.8625 477.0 4770 2.1575
1.8625 478.0 4780 2.0797
1.8625 479.0 4790 2.2353
1.7743 480.0 4800 2.2478
1.7743 481.0 4810 2.1120
1.7743 482.0 4820 2.1790
1.7743 483.0 4830 3.1939
1.7743 484.0 4840 2.0575
1.7955 485.0 4850 2.3685
1.7955 486.0 4860 2.1021
1.7955 487.0 4870 2.3043
1.7955 488.0 4880 2.1155
1.7955 489.0 4890 2.0982
1.7685 490.0 4900 2.2740
1.7685 491.0 4910 2.1216
1.7685 492.0 4920 2.0764
1.7685 493.0 4930 2.1182
1.7685 494.0 4940 2.0343
1.8806 495.0 4950 2.0229
1.8806 496.0 4960 2.9971
1.8806 497.0 4970 2.1848
1.8806 498.0 4980 2.6586
1.8806 499.0 4990 2.3622
1.8554 500.0 5000 2.5255
1.8554 501.0 5010 2.1792
1.8554 502.0 5020 2.2098
1.8554 503.0 5030 3.0466
1.8554 504.0 5040 2.2054
1.8123 505.0 5050 2.0846
1.8123 506.0 5060 2.4480
1.8123 507.0 5070 2.1692
1.8123 508.0 5080 2.1262
1.8123 509.0 5090 2.0610
1.8189 510.0 5100 2.1438
1.8189 511.0 5110 1.9691
1.8189 512.0 5120 1.9818
1.8189 513.0 5130 2.1824
1.8189 514.0 5140 2.3053
1.7296 515.0 5150 2.0095
1.7296 516.0 5160 2.3895
1.7296 517.0 5170 2.4203
1.7296 518.0 5180 2.8143
1.7296 519.0 5190 1.9249
1.9353 520.0 5200 1.9745
1.9353 521.0 5210 2.3712
1.9353 522.0 5220 2.2221
1.9353 523.0 5230 2.3223
1.9353 524.0 5240 2.0649
1.8203 525.0 5250 2.4524
1.8203 526.0 5260 2.1729
1.8203 527.0 5270 2.3503
1.8203 528.0 5280 1.8859
1.8203 529.0 5290 2.5795
1.9042 530.0 5300 2.2665
1.9042 531.0 5310 1.9231
1.9042 532.0 5320 2.1896
1.9042 533.0 5330 2.1866
1.9042 534.0 5340 2.1273
1.797 535.0 5350 2.1864
1.797 536.0 5360 2.1360
1.797 537.0 5370 2.1195
1.797 538.0 5380 1.9885
1.797 539.0 5390 1.9990
1.8289 540.0 5400 2.0208
1.8289 541.0 5410 1.9337
1.8289 542.0 5420 2.0515
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1.6751 1000.0 10000 2.1397

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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