detr-resnet-50_finetuned_cppe5

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: 3.7727

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 400
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.0989	1.0	125	3.4853
2.6414	2.0	250	3.4603
2.4811	3.0	375	3.1249
2.2018	4.0	500	2.9870
2.1618	5.0	625	3.2940
2.0608	6.0	750	3.1065
1.9556	7.0	875	3.0672
1.9142	8.0	1000	3.1347
1.8984	9.0	1125	3.0545
1.7799	10.0	1250	3.1041
1.7396	11.0	1375	2.9855
1.6741	12.0	1500	2.9899
1.6367	13.0	1625	3.0423
1.5807	14.0	1750	2.9943
1.6542	15.0	1875	3.0440
1.6268	16.0	2000	3.0157
1.5376	17.0	2125	3.0821
1.5396	18.0	2250	3.0359
1.5513	19.0	2375	3.0542
1.5038	20.0	2500	3.0153
1.4642	21.0	2625	3.0700
1.4331	22.0	2750	2.9970
1.4549	23.0	2875	3.0449
1.3796	24.0	3000	2.9348
1.3486	25.0	3125	3.0530
1.3557	26.0	3250	2.9036
1.3322	27.0	3375	2.8978
1.2866	28.0	3500	2.9449
1.2197	29.0	3625	2.9240
1.2596	30.0	3750	2.9422
1.2618	31.0	3875	2.8963
1.2298	32.0	4000	2.9116
1.2755	33.0	4125	2.8461
1.2062	34.0	4250	2.8651
1.2536	35.0	4375	2.8515
1.2543	36.0	4500	2.8212
1.2047	37.0	4625	2.8645
1.1618	38.0	4750	2.8755
1.1341	39.0	4875	2.9445
1.1881	40.0	5000	2.8731
1.2004	41.0	5125	2.8917
1.105	42.0	5250	2.9692
1.1408	43.0	5375	2.8619
1.0987	44.0	5500	2.8797
1.0857	45.0	5625	2.9032
1.0983	46.0	5750	2.7954
1.1254	47.0	5875	2.8693
0.9895	48.0	6000	2.9085
1.0401	49.0	6125	2.9256
1.0427	50.0	6250	2.9414
1.0813	51.0	6375	2.9730
1.043	52.0	6500	2.9698
1.0406	53.0	6625	2.9039
1.0178	54.0	6750	2.8910
1.0342	55.0	6875	2.8973
0.9433	56.0	7000	2.9515
1.0011	57.0	7125	2.8979
0.9683	58.0	7250	2.9770
0.9852	59.0	7375	2.9760
0.8886	60.0	7500	2.9978
0.9192	61.0	7625	2.9287
1.0015	62.0	7750	3.0118
0.9786	63.0	7875	2.9599
0.9238	64.0	8000	2.9459
0.9055	65.0	8125	2.9782
0.8864	66.0	8250	2.9396
0.8986	67.0	8375	3.0249
0.9571	68.0	8500	3.0207
0.8921	69.0	8625	2.9716
0.847	70.0	8750	3.0404
0.881	71.0	8875	2.9912
0.8919	72.0	9000	2.8573
0.8949	73.0	9125	2.8769
0.8704	74.0	9250	2.8301
0.8159	75.0	9375	2.9823
0.8586	76.0	9500	2.9548
0.8398	77.0	9625	2.9726
0.8368	78.0	9750	2.9853
0.8597	79.0	9875	3.0549
0.8176	80.0	10000	3.0531
0.838	81.0	10125	2.9602
0.8059	82.0	10250	2.9129
0.8176	83.0	10375	2.9702
0.8318	84.0	10500	2.9154
0.7104	85.0	10625	2.9706
0.7804	86.0	10750	2.9344
0.7696	87.0	10875	3.0594
0.7478	88.0	11000	2.9454
0.7374	89.0	11125	2.9786
0.7977	90.0	11250	2.9514
0.7864	91.0	11375	2.9400
0.7665	92.0	11500	2.8717
0.7539	93.0	11625	3.0417
0.6882	94.0	11750	2.9567
0.7424	95.0	11875	2.9805
0.7238	96.0	12000	3.0428
0.7383	97.0	12125	2.9852
0.6602	98.0	12250	3.0132
0.6971	99.0	12375	2.9537
0.7379	100.0	12500	2.9592
0.7207	101.0	12625	2.9905
0.7012	102.0	12750	3.0638
0.6768	103.0	12875	3.0401
0.6777	104.0	13000	3.0396
0.6913	105.0	13125	2.9501
0.6654	106.0	13250	3.1079
0.6393	107.0	13375	3.0405
0.6465	108.0	13500	3.1579
0.6379	109.0	13625	3.1174
0.6662	110.0	13750	3.0548
0.657	111.0	13875	3.0542
0.6193	112.0	14000	3.0411
0.5961	113.0	14125	3.0915
0.6438	114.0	14250	3.0924
0.5815	115.0	14375	3.0309
0.6113	116.0	14500	3.0300
0.635	117.0	14625	2.9968
0.5875	118.0	14750	2.9821
0.6447	119.0	14875	3.0285
0.5632	120.0	15000	3.0684
0.6206	121.0	15125	3.0065
0.5929	122.0	15250	3.0490
0.5509	123.0	15375	3.0520
0.6068	124.0	15500	3.0957
0.5747	125.0	15625	2.9621
0.5844	126.0	15750	3.0194
0.5684	127.0	15875	3.1037
0.6356	128.0	16000	3.0752
0.5644	129.0	16125	3.0063
0.6303	130.0	16250	3.0204
0.5432	131.0	16375	3.0809
0.6153	132.0	16500	3.1015
0.5662	133.0	16625	3.0639
0.5704	134.0	16750	3.1974
0.603	135.0	16875	3.1371
0.526	136.0	17000	3.1381
0.5767	137.0	17125	3.1614
0.5591	138.0	17250	3.2744
0.5609	139.0	17375	3.0405
0.5229	140.0	17500	3.0773
0.5367	141.0	17625	3.1367
0.5719	142.0	17750	3.1770
0.5172	143.0	17875	3.0953
0.5592	144.0	18000	3.2524
0.5422	145.0	18125	3.1534
0.5007	146.0	18250	3.1571
0.5348	147.0	18375	3.0949
0.5123	148.0	18500	3.1381
0.4839	149.0	18625	3.1624
0.5207	150.0	18750	3.0585
0.5236	151.0	18875	3.0886
0.5144	152.0	19000	3.1348
0.4882	153.0	19125	3.1027
0.4618	154.0	19250	3.1335
0.4573	155.0	19375	3.1687
0.4956	156.0	19500	3.2155
0.5073	157.0	19625	3.2512
0.5334	158.0	19750	3.2262
0.5014	159.0	19875	3.2350
0.4519	160.0	20000	3.2416
0.5042	161.0	20125	3.1955
0.4624	162.0	20250	3.2036
0.4577	163.0	20375	3.2498
0.5032	164.0	20500	3.1687
0.4894	165.0	20625	3.1920
0.4621	166.0	20750	3.2275
0.4896	167.0	20875	3.1416
0.4998	168.0	21000	3.1483
0.4941	169.0	21125	3.1408
0.4307	170.0	21250	3.2056
0.4284	171.0	21375	3.2112
0.4431	172.0	21500	3.1926
0.4429	173.0	21625	3.0972
0.4832	174.0	21750	3.2309
0.4417	175.0	21875	3.2027
0.4022	176.0	22000	3.2380
0.4777	177.0	22125	3.3315
0.462	178.0	22250	3.1854
0.4209	179.0	22375	3.1563
0.4271	180.0	22500	3.3036
0.4359	181.0	22625	3.3058
0.4324	182.0	22750	3.3639
0.4252	183.0	22875	3.2810
0.4382	184.0	23000	3.4633
0.4344	185.0	23125	3.2875
0.4639	186.0	23250	3.2771
0.4104	187.0	23375	3.2768
0.437	188.0	23500	3.3128
0.4469	189.0	23625	3.2389
0.4084	190.0	23750	3.4082
0.4333	191.0	23875	3.3177
0.4337	192.0	24000	3.3474
0.4173	193.0	24125	3.3495
0.386	194.0	24250	3.3413
0.4279	195.0	24375	3.3327
0.4046	196.0	24500	3.3383
0.4163	197.0	24625	3.3235
0.4032	198.0	24750	3.3549
0.381	199.0	24875	3.2899
0.3858	200.0	25000	3.3752
0.4085	201.0	25125	3.2569
0.3643	202.0	25250	3.3265
0.3621	203.0	25375	3.3730
0.4749	204.0	25500	3.3738
0.3969	205.0	25625	3.3619
0.3677	206.0	25750	3.4378
0.3838	207.0	25875	3.3412
0.4063	208.0	26000	3.3268
0.3719	209.0	26125	3.4574
0.3803	210.0	26250	3.3598
0.4093	211.0	26375	3.3738
0.38	212.0	26500	3.2644
0.3757	213.0	26625	3.3872
0.4116	214.0	26750	3.4318
0.3741	215.0	26875	3.2945
0.3809	216.0	27000	3.4419
0.3625	217.0	27125	3.4126
0.3772	218.0	27250	3.3693
0.3494	219.0	27375	3.3014
0.3521	220.0	27500	3.4202
0.3498	221.0	27625	3.2887
0.3716	222.0	27750	3.5634
0.346	223.0	27875	3.3463
0.388	224.0	28000	3.4088
0.3708	225.0	28125	3.3841
0.3964	226.0	28250	3.3839
0.3897	227.0	28375	3.4874
0.3272	228.0	28500	3.5225
0.3582	229.0	28625	3.4964
0.3656	230.0	28750	3.3781
0.3497	231.0	28875	3.4067
0.3318	232.0	29000	3.4918
0.3565	233.0	29125	3.5039
0.3865	234.0	29250	3.5416
0.3583	235.0	29375	3.4231
0.3464	236.0	29500	3.4524
0.3465	237.0	29625	3.4779
0.3428	238.0	29750	3.4889
0.3847	239.0	29875	3.5142
0.3505	240.0	30000	3.5132
0.344	241.0	30125	3.5439
0.3741	242.0	30250	3.4861
0.3045	243.0	30375	3.4534
0.3443	244.0	30500	3.4675
0.3719	245.0	30625	3.4354
0.3534	246.0	30750	3.4817
0.3644	247.0	30875	3.5027
0.3157	248.0	31000	3.5055
0.3393	249.0	31125	3.3962
0.3054	250.0	31250	3.4470
0.3434	251.0	31375	3.5036
0.3141	252.0	31500	3.5428
0.3227	253.0	31625	3.5025
0.3199	254.0	31750	3.5110
0.3667	255.0	31875	3.5168
0.3442	256.0	32000	3.5739
0.366	257.0	32125	3.5094
0.3053	258.0	32250	3.4360
0.3595	259.0	32375	3.5895
0.3329	260.0	32500	3.5869
0.3139	261.0	32625	3.5317
0.299	262.0	32750	3.6398
0.3131	263.0	32875	3.5696
0.3218	264.0	33000	3.5519
0.3677	265.0	33125	3.6712
0.3447	266.0	33250	3.5278
0.3094	267.0	33375	3.5613
0.3031	268.0	33500	3.4634
0.3234	269.0	33625	3.5966
0.3489	270.0	33750	3.5239
0.3168	271.0	33875	3.6847
0.3151	272.0	34000	3.5559
0.2843	273.0	34125	3.5995
0.3003	274.0	34250	3.6388
0.3154	275.0	34375	3.6759
0.3178	276.0	34500	3.5199
0.3436	277.0	34625	3.5651
0.3136	278.0	34750	3.5722
0.3252	279.0	34875	3.4851
0.3404	280.0	35000	3.6847
0.304	281.0	35125	3.5653
0.3395	282.0	35250	3.6775
0.3431	283.0	35375	3.5556
0.2861	284.0	35500	3.6451
0.3066	285.0	35625	3.6052
0.3151	286.0	35750	3.6406
0.3143	287.0	35875	3.6744
0.2873	288.0	36000	3.6218
0.3296	289.0	36125	3.4993
0.3024	290.0	36250	3.5596
0.3138	291.0	36375	3.5875
0.2964	292.0	36500	3.5953
0.2935	293.0	36625	3.5550
0.2856	294.0	36750	3.5805
0.2998	295.0	36875	3.6153
0.2809	296.0	37000	3.6440
0.3197	297.0	37125	3.6127
0.2863	298.0	37250	3.6362
0.3355	299.0	37375	3.7291
0.2942	300.0	37500	3.6750
0.3187	301.0	37625	3.6617
0.3191	302.0	37750	3.6898
0.2716	303.0	37875	3.6238
0.2911	304.0	38000	3.6409
0.3231	305.0	38125	3.6807
0.2723	306.0	38250	3.7038
0.2812	307.0	38375	3.6565
0.3225	308.0	38500	3.6680
0.2803	309.0	38625	3.7389
0.2852	310.0	38750	3.7257
0.2958	311.0	38875	3.7873
0.3191	312.0	39000	3.8238
0.2815	313.0	39125	3.7388
0.2681	314.0	39250	3.7543
0.3259	315.0	39375	3.8022
0.2804	316.0	39500	3.7106
0.3214	317.0	39625	3.6244
0.2732	318.0	39750	3.7712
0.3054	319.0	39875	3.6776
0.263	320.0	40000	3.6850
0.2644	321.0	40125	3.7169
0.2796	322.0	40250	3.7958
0.2928	323.0	40375	3.7770
0.2774	324.0	40500	3.7142
0.3048	325.0	40625	3.7942
0.2637	326.0	40750	3.7499
0.2549	327.0	40875	3.7323
0.2681	328.0	41000	3.8373
0.2735	329.0	41125	3.7600
0.291	330.0	41250	3.6715
0.278	331.0	41375	3.6660
0.2785	332.0	41500	3.7076
0.2632	333.0	41625	3.7408
0.2994	334.0	41750	3.7214
0.2563	335.0	41875	3.7326
0.2755	336.0	42000	3.7088
0.287	337.0	42125	3.7493
0.3144	338.0	42250	3.7180
0.2816	339.0	42375	3.7289
0.2515	340.0	42500	3.6592
0.2647	341.0	42625	3.6483
0.2833	342.0	42750	3.7359
0.2678	343.0	42875	3.7351
0.2929	344.0	43000	3.7129
0.2604	345.0	43125	3.7604
0.2406	346.0	43250	3.6867
0.2802	347.0	43375	3.6935
0.2702	348.0	43500	3.6744
0.2836	349.0	43625	3.7118
0.2454	350.0	43750	3.7354
0.238	351.0	43875	3.7200
0.2663	352.0	44000	3.7351
0.272	353.0	44125	3.7509
0.2422	354.0	44250	3.7413
0.3032	355.0	44375	3.6266
0.2617	356.0	44500	3.7229
0.2618	357.0	44625	3.7356
0.2619	358.0	44750	3.7178
0.2588	359.0	44875	3.7896
0.2508	360.0	45000	3.7686
0.2791	361.0	45125	3.7918
0.27	362.0	45250	3.7870
0.2841	363.0	45375	3.7675
0.2776	364.0	45500	3.7090
0.2752	365.0	45625	3.6754
0.2727	366.0	45750	3.6542
0.2423	367.0	45875	3.6399
0.263	368.0	46000	3.6337
0.2562	369.0	46125	3.7343
0.2704	370.0	46250	3.6502
0.2604	371.0	46375	3.7519
0.2312	372.0	46500	3.7563
0.2761	373.0	46625	3.6497
0.265	374.0	46750	3.7312
0.2545	375.0	46875	3.7273
0.2551	376.0	47000	3.8266
0.2604	377.0	47125	3.7659
0.252	378.0	47250	3.8003
0.2468	379.0	47375	3.7535
0.289	380.0	47500	3.7376
0.2449	381.0	47625	3.7216
0.2471	382.0	47750	3.7704
0.2627	383.0	47875	3.7510
0.2454	384.0	48000	3.8007
0.2391	385.0	48125	3.7535
0.2452	386.0	48250	3.7905
0.2608	387.0	48375	3.8170
0.2662	388.0	48500	3.7836
0.235	389.0	48625	3.7754
0.2425	390.0	48750	3.7201
0.2463	391.0	48875	3.8298
0.2507	392.0	49000	3.8252
0.2451	393.0	49125	3.7625
0.2418	394.0	49250	3.7482
0.2397	395.0	49375	3.7875
0.2773	396.0	49500	3.8159
0.256	397.0	49625	3.8322
0.2304	398.0	49750	3.8012
0.2363	399.0	49875	3.8033
0.2575	400.0	50000	3.7727

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.0
• Tokenizers 0.15.0