chickens-repro-2

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: 0.2380

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: 2
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss
2.8642	1.0	227	2.7786
2.3721	2.0	454	2.2758
2.3709	3.0	681	2.1522
1.992	4.0	908	1.8385
2.1679	5.0	1135	1.9064
1.758	6.0	1362	1.6010
1.5898	7.0	1589	1.4712
1.5394	8.0	1816	1.3826
1.587	9.0	2043	1.4157
1.4699	10.0	2270	1.2894
1.3848	11.0	2497	1.2382
1.3815	12.0	2724	1.2361
1.3613	13.0	2951	1.3203
1.4138	14.0	3178	1.2496
1.3376	15.0	3405	1.6375
1.2191	16.0	3632	1.1517
1.2034	17.0	3859	1.1106
1.2332	18.0	4086	1.0974
1.2246	19.0	4313	1.0629
1.2412	20.0	4540	1.1839
1.1197	21.0	4767	1.2765
1.2092	22.0	4994	0.9938
1.1339	23.0	5221	0.9137
1.0476	24.0	5448	0.9967
1.1219	25.0	5675	1.0872
1.032	26.0	5902	1.0375
1.0126	27.0	6129	0.8812
0.994	28.0	6356	0.8283
0.9812	29.0	6583	0.8342
0.8992	30.0	6810	0.8800
1.0294	31.0	7037	0.8117
0.9669	32.0	7264	0.8216
0.956	33.0	7491	0.8803
0.9274	34.0	7718	0.8257
0.9307	35.0	7945	0.8582
0.8248	36.0	8172	0.9441
0.8924	37.0	8399	0.7222
0.8552	38.0	8626	0.7476
0.8801	39.0	8853	0.8598
0.8812	40.0	9080	0.8358
0.8214	41.0	9307	0.7525
0.8671	42.0	9534	0.7123
0.8931	43.0	9761	0.7301
0.7545	44.0	9988	0.6438
0.8072	45.0	10215	0.5928
0.7908	46.0	10442	0.6737
0.8407	47.0	10669	0.5894
0.7697	48.0	10896	0.6289
0.7428	49.0	11123	0.8339
0.7736	50.0	11350	0.6534
0.7625	51.0	11577	0.6981
0.735	52.0	11804	0.6412
0.7195	53.0	12031	0.6030
0.693	54.0	12258	0.5586
0.7213	55.0	12485	0.5616
0.77	56.0	12712	0.5286
0.7317	57.0	12939	0.5960
0.6903	58.0	13166	0.5512
0.6516	59.0	13393	0.5363
0.6507	60.0	13620	0.5430
0.6778	61.0	13847	0.5447
0.6809	62.0	14074	0.5097
0.6508	63.0	14301	0.5164
0.6251	64.0	14528	0.5230
0.6374	65.0	14755	0.5548
0.6111	66.0	14982	0.4891
0.6163	67.0	15209	0.5317
0.5782	68.0	15436	0.5339
0.5976	69.0	15663	0.5699
0.5988	70.0	15890	0.4710
0.5744	71.0	16117	0.4559
0.6212	72.0	16344	0.5214
0.6087	73.0	16571	0.4709
0.6014	74.0	16798	0.4691
0.5496	75.0	17025	0.4391
0.5636	76.0	17252	0.4778
0.535	77.0	17479	0.4294
0.5198	78.0	17706	0.4557
0.5691	79.0	17933	0.5150
0.5253	80.0	18160	0.4082
0.5217	81.0	18387	0.4121
0.524	82.0	18614	0.4222
0.5445	83.0	18841	0.5020
0.5398	84.0	19068	0.3842
0.5395	85.0	19295	0.4417
0.5229	86.0	19522	0.3844
0.5029	87.0	19749	0.4135
0.5317	88.0	19976	0.4352
0.4964	89.0	20203	0.4285
0.5111	90.0	20430	0.4970
0.4912	91.0	20657	0.4222
0.5329	92.0	20884	0.3773
0.5028	93.0	21111	0.3613
0.4981	94.0	21338	0.3819
0.483	95.0	21565	0.4335
0.501	96.0	21792	0.4052
0.5115	97.0	22019	0.4089
0.4829	98.0	22246	0.3903
0.4995	99.0	22473	0.3389
0.4905	100.0	22700	0.3421
0.5061	101.0	22927	0.3613
0.4718	102.0	23154	0.3889
0.4949	103.0	23381	0.3943
0.4822	104.0	23608	0.3837
0.4707	105.0	23835	0.3587
0.4895	106.0	24062	0.3607
0.4346	107.0	24289	0.3733
0.4656	108.0	24516	0.3329
0.4466	109.0	24743	0.3684
0.4727	110.0	24970	0.3618
0.4426	111.0	25197	0.3540
0.454	112.0	25424	0.3167
0.439	113.0	25651	0.3334
0.4624	114.0	25878	0.3527
0.4308	115.0	26105	0.3511
0.4407	116.0	26332	0.3275
0.4517	117.0	26559	0.3365
0.4601	118.0	26786	0.3527
0.4454	119.0	27013	0.3842
0.4281	120.0	27240	0.3746
0.4453	121.0	27467	0.3427
0.436	122.0	27694	0.3067
0.4403	123.0	27921	0.3150
0.4143	124.0	28148	0.3164
0.4048	125.0	28375	0.3616
0.428	126.0	28602	0.3720
0.4188	127.0	28829	0.3234
0.4172	128.0	29056	0.3124
0.4494	129.0	29283	0.2888
0.4267	130.0	29510	0.2965
0.4243	131.0	29737	0.3622
0.4136	132.0	29964	0.3205
0.4333	133.0	30191	0.3315
0.4185	134.0	30418	0.3479
0.395	135.0	30645	0.3305
0.4046	136.0	30872	0.3213
0.4118	137.0	31099	0.3258
0.4209	138.0	31326	0.2947
0.4129	139.0	31553	0.3013
0.4286	140.0	31780	0.2957
0.4043	141.0	32007	0.3560
0.4028	142.0	32234	0.2829
0.3998	143.0	32461	0.3024
0.3926	144.0	32688	0.3162
0.4089	145.0	32915	0.2693
0.4075	146.0	33142	0.3138
0.4059	147.0	33369	0.3018
0.393	148.0	33596	0.2809
0.3981	149.0	33823	0.3162
0.4073	150.0	34050	0.3090
0.398	151.0	34277	0.2970
0.3778	152.0	34504	0.2866
0.3808	153.0	34731	0.3098
0.3883	154.0	34958	0.2856
0.3817	155.0	35185	0.2766
0.3783	156.0	35412	0.3010
0.3826	157.0	35639	0.3383
0.4134	158.0	35866	0.3586
0.3847	159.0	36093	0.2574
0.3839	160.0	36320	0.2916
0.3876	161.0	36547	0.2743
0.3923	162.0	36774	0.2743
0.3557	163.0	37001	0.2901
0.362	164.0	37228	0.2476
0.3867	165.0	37455	0.3223
0.3745	166.0	37682	0.2784
0.3714	167.0	37909	0.3123
0.364	168.0	38136	0.2759
0.3873	169.0	38363	0.2787
0.3835	170.0	38590	0.3225
0.3797	171.0	38817	0.2866
0.376	172.0	39044	0.2592
0.3576	173.0	39271	0.2962
0.3627	174.0	39498	0.2839
0.3932	175.0	39725	0.2616
0.3605	176.0	39952	0.2960
0.3729	177.0	40179	0.2642
0.3613	178.0	40406	0.2763
0.3744	179.0	40633	0.2748
0.3574	180.0	40860	0.2835
0.3471	181.0	41087	0.2893
0.3631	182.0	41314	0.2618
0.3517	183.0	41541	0.2641
0.3526	184.0	41768	0.2523
0.3705	185.0	41995	0.2873
0.3736	186.0	42222	0.2636
0.3635	187.0	42449	0.2791
0.3607	188.0	42676	0.2664
0.3435	189.0	42903	0.2487
0.3729	190.0	43130	0.2833
0.3611	191.0	43357	0.2557
0.3624	192.0	43584	0.2985
0.3615	193.0	43811	0.2616
0.346	194.0	44038	0.2669
0.3478	195.0	44265	0.2753
0.3392	196.0	44492	0.2974
0.3537	197.0	44719	0.2647
0.3665	198.0	44946	0.2623
0.3256	199.0	45173	0.2381
0.3508	200.0	45400	0.2741
0.335	201.0	45627	0.2800
0.3535	202.0	45854	0.2947
0.3414	203.0	46081	0.2443
0.3334	204.0	46308	0.2493
0.3399	205.0	46535	0.2597
0.3317	206.0	46762	0.2701
0.328	207.0	46989	0.2622
0.3589	208.0	47216	0.2319
0.3305	209.0	47443	0.2423
0.3578	210.0	47670	0.2300
0.3427	211.0	47897	0.2728
0.3278	212.0	48124	0.2541
0.3624	213.0	48351	0.2530
0.3292	214.0	48578	0.2817
0.3349	215.0	48805	0.2558
0.3345	216.0	49032	0.2464
0.3476	217.0	49259	0.2799
0.3276	218.0	49486	0.2618
0.3495	219.0	49713	0.2551
0.3451	220.0	49940	0.2640
0.3319	221.0	50167	0.2483
0.3348	222.0	50394	0.2460
0.3399	223.0	50621	0.2517
0.3619	224.0	50848	0.2425
0.3416	225.0	51075	0.2626
0.3156	226.0	51302	0.2513
0.3398	227.0	51529	0.2561
0.3424	228.0	51756	0.2573
0.3386	229.0	51983	0.2471
0.3382	230.0	52210	0.2462
0.331	231.0	52437	0.2557
0.3329	232.0	52664	0.2513
0.3319	233.0	52891	0.2750
0.3314	234.0	53118	0.2377
0.3437	235.0	53345	0.2416
0.3278	236.0	53572	0.2448
0.3206	237.0	53799	0.2478
0.3305	238.0	54026	0.2441
0.3269	239.0	54253	0.2497
0.3343	240.0	54480	0.2314
0.3235	241.0	54707	0.2492
0.3034	242.0	54934	0.2438
0.3124	243.0	55161	0.2455
0.356	244.0	55388	0.2515
0.3276	245.0	55615	0.2452
0.3383	246.0	55842	0.2466
0.3168	247.0	56069	0.2527
0.3451	248.0	56296	0.2529
0.3263	249.0	56523	0.2477
0.3332	250.0	56750	0.2479
0.3281	251.0	56977	0.2359
0.3352	252.0	57204	0.2411
0.3271	253.0	57431	0.2384
0.3373	254.0	57658	0.2499
0.3366	255.0	57885	0.2498
0.3276	256.0	58112	0.2550
0.3368	257.0	58339	0.2485
0.3371	258.0	58566	0.2395
0.3187	259.0	58793	0.2413
0.3208	260.0	59020	0.2375
0.3296	261.0	59247	0.2437
0.3191	262.0	59474	0.2478
0.3097	263.0	59701	0.2350
0.3125	264.0	59928	0.2378
0.3146	265.0	60155	0.2415
0.3163	266.0	60382	0.2437
0.3106	267.0	60609	0.2406
0.3135	268.0	60836	0.2405
0.3219	269.0	61063	0.2410
0.3287	270.0	61290	0.2350
0.3264	271.0	61517	0.2412
0.3196	272.0	61744	0.2355
0.3216	273.0	61971	0.2403
0.3125	274.0	62198	0.2394
0.3356	275.0	62425	0.2365
0.3116	276.0	62652	0.2407
0.3201	277.0	62879	0.2397
0.3188	278.0	63106	0.2407
0.3056	279.0	63333	0.2380
0.3173	280.0	63560	0.2395
0.3349	281.0	63787	0.2387
0.3206	282.0	64014	0.2354
0.3172	283.0	64241	0.2363
0.3099	284.0	64468	0.2439
0.339	285.0	64695	0.2364
0.32	286.0	64922	0.2350
0.3251	287.0	65149	0.2366
0.3271	288.0	65376	0.2390
0.3033	289.0	65603	0.2381
0.3226	290.0	65830	0.2385
0.3104	291.0	66057	0.2388
0.3118	292.0	66284	0.2397
0.3188	293.0	66511	0.2385
0.3103	294.0	66738	0.2385
0.319	295.0	66965	0.2387
0.3145	296.0	67192	0.2380
0.3197	297.0	67419	0.2380
0.3132	298.0	67646	0.2380
0.3348	299.0	67873	0.2380
0.3086	300.0	68100	0.2380

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.1+cu121
• Datasets 2.19.2
• Tokenizers 0.20.0