learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Artemia	Mar 100 Artemia
No log	1.0	250	14.4631	0.4356	0.7885	0.4451	0.3341	0.5718	-1.0	0.3442	0.5723	0.6283	0.5349	0.7562	-1.0	0.4356	0.6283
172.3915	2.0	500	8.3680	0.4508	0.8175	0.4463	0.3608	0.5821	-1.0	0.3611	0.5944	0.6374	0.5914	0.7036	-1.0	0.4508	0.6374
172.3915	3.0	750	7.9072	0.486	0.8594	0.5019	0.414	0.6008	-1.0	0.3741	0.6081	0.6498	0.6086	0.7073	-1.0	0.486	0.6498
13.7544	4.0	1000	8.1075	0.4813	0.8567	0.472	0.396	0.6033	-1.0	0.3723	0.5813	0.6243	0.5661	0.7036	-1.0	0.4813	0.6243
13.7544	5.0	1250	8.1847	0.4643	0.8472	0.479	0.3805	0.5916	-1.0	0.3698	0.5713	0.605	0.5419	0.692	-1.0	0.4643	0.605
11.8514	6.0	1500	8.0372	0.4639	0.8532	0.451	0.3786	0.5943	-1.0	0.3648	0.5782	0.6184	0.5608	0.6971	-1.0	0.4639	0.6184
11.8514	7.0	1750	8.0814	0.4674	0.8747	0.4302	0.391	0.5842	-1.0	0.3701	0.581	0.6106	0.5511	0.6927	-1.0	0.4674	0.6106
10.9011	8.0	2000	8.2653	0.4398	0.8378	0.4285	0.355	0.5941	-1.0	0.3601	0.5779	0.6112	0.5538	0.6905	-1.0	0.4398	0.6112
10.9011	9.0	2250	8.4868	0.4652	0.8473	0.4409	0.3851	0.5969	-1.0	0.3676	0.5894	0.6134	0.5543	0.6949	-1.0	0.4652	0.6134
9.8457	10.0	2500	8.6205	0.4452	0.8283	0.4359	0.3576	0.5895	-1.0	0.3601	0.5654	0.5745	0.507	0.6672	-1.0	0.4452	0.5745
9.8457	11.0	2750	8.5778	0.4562	0.8496	0.4099	0.3742	0.5877	-1.0	0.3583	0.566	0.5829	0.5075	0.6854	-1.0	0.4562	0.5829
9.3075	12.0	3000	8.7906	0.4436	0.8376	0.4091	0.3568	0.5928	-1.0	0.3592	0.5561	0.5592	0.4833	0.6628	-1.0	0.4436	0.5592
9.3075	13.0	3250	8.6636	0.4424	0.8438	0.4042	0.3615	0.5735	-1.0	0.3592	0.5564	0.5648	0.493	0.6635	-1.0	0.4424	0.5648
8.8441	14.0	3500	8.8710	0.4264	0.8051	0.4021	0.3356	0.5918	-1.0	0.3489	0.5623	0.5682	0.4978	0.6642	-1.0	0.4264	0.5682
8.8441	15.0	3750	9.2949	0.4249	0.7926	0.3942	0.3319	0.5922	-1.0	0.3495	0.5648	0.576	0.4973	0.6832	-1.0	0.4249	0.576
8.4733	16.0	4000	8.7027	0.4493	0.8336	0.4386	0.3685	0.5931	-1.0	0.3642	0.5548	0.5561	0.4785	0.662	-1.0	0.4493	0.5561
8.4733	17.0	4250	9.4468	0.4115	0.78	0.3713	0.3177	0.5912	-1.0	0.3417	0.5679	0.5891	0.5075	0.7007	-1.0	0.4115	0.5891
7.9193	18.0	4500	9.5585	0.3914	0.7409	0.3668	0.2885	0.5945	-1.0	0.3299	0.5835	0.596	0.5199	0.7	-1.0	0.3914	0.596
7.9193	19.0	4750	9.1970	0.414	0.7637	0.3963	0.3164	0.5856	-1.0	0.3402	0.5723	0.5804	0.4973	0.6942	-1.0	0.414	0.5804
7.8449	20.0	5000	9.4783	0.3908	0.7315	0.366	0.2966	0.5865	-1.0	0.3259	0.5701	0.5782	0.507	0.6752	-1.0	0.3908	0.5782
7.8449	21.0	5250	9.7154	0.3996	0.7527	0.3839	0.2972	0.5816	-1.0	0.3411	0.5645	0.566	0.4898	0.6708	-1.0	0.3996	0.566
7.4024	22.0	5500	9.3798	0.4209	0.7972	0.3731	0.3241	0.5837	-1.0	0.3374	0.571	0.5798	0.4925	0.6993	-1.0	0.4209	0.5798
7.4024	23.0	5750	9.3620	0.4082	0.7616	0.3938	0.3133	0.5793	-1.0	0.3442	0.5567	0.5592	0.486	0.6599	-1.0	0.4082	0.5592
7.0500	24.0	6000	9.8907	0.4099	0.7555	0.3939	0.3104	0.5943	-1.0	0.3386	0.553	0.5548	0.4747	0.6642	-1.0	0.4099	0.5548
7.0500	25.0	6250	9.7498	0.4134	0.7797	0.383	0.3178	0.5839	-1.0	0.348	0.5442	0.5452	0.4565	0.6664	-1.0	0.4134	0.5452
6.9048	26.0	6500	9.9386	0.4129	0.7763	0.3863	0.3119	0.5851	-1.0	0.3411	0.5477	0.5492	0.4629	0.6679	-1.0	0.4129	0.5492
6.9048	27.0	6750	10.0945	0.3989	0.748	0.3444	0.2991	0.5861	-1.0	0.3399	0.5564	0.5632	0.472	0.6883	-1.0	0.3989	0.5632
6.7264	28.0	7000	9.8866	0.4076	0.7616	0.3996	0.3075	0.5857	-1.0	0.343	0.5542	0.5567	0.4774	0.665	-1.0	0.4076	0.5567
6.7264	29.0	7250	9.7453	0.3964	0.7388	0.3569	0.2948	0.5835	-1.0	0.3352	0.5495	0.5517	0.4694	0.6642	-1.0	0.3964	0.5517
6.5454	30.0	7500	9.9308	0.405	0.7548	0.3789	0.302	0.5921	-1.0	0.3396	0.5424	0.5452	0.4597	0.662	-1.0	0.405	0.5452
6.5454	31.0	7750	9.8308	0.4182	0.7866	0.3799	0.322	0.5837	-1.0	0.3458	0.5511	0.5514	0.4688	0.665	-1.0	0.4182	0.5514
6.3073	32.0	8000	9.6804	0.4192	0.7808	0.3836	0.3246	0.5842	-1.0	0.3449	0.547	0.5483	0.4677	0.6591	-1.0	0.4192	0.5483
6.3073	33.0	8250	9.9787	0.408	0.7603	0.3814	0.3051	0.5923	-1.0	0.3461	0.5526	0.5533	0.4667	0.6723	-1.0	0.408	0.5533
6.2181	34.0	8500	10.1687	0.4028	0.7557	0.3845	0.3022	0.59	-1.0	0.3464	0.5561	0.5564	0.4737	0.6701	-1.0	0.4028	0.5564
6.2181	35.0	8750	10.1298	0.3873	0.7302	0.3667	0.2856	0.5852	-1.0	0.3421	0.5424	0.5449	0.4597	0.662	-1.0	0.3873	0.5449
6.0297	36.0	9000	10.0816	0.3652	0.6895	0.3325	0.2675	0.5632	-1.0	0.3349	0.543	0.547	0.4629	0.6628	-1.0	0.3652	0.547
6.0297	37.0	9250	11.2706	0.362	0.6849	0.3487	0.2587	0.575	-1.0	0.329	0.5511	0.5561	0.478	0.6635	-1.0	0.362	0.5561
6.0182	38.0	9500	10.2335	0.3826	0.7131	0.3568	0.2856	0.5688	-1.0	0.3346	0.5361	0.5377	0.4511	0.6562	-1.0	0.3826	0.5377
6.0182	39.0	9750	9.9745	0.3891	0.7372	0.3551	0.2936	0.5774	-1.0	0.3315	0.5461	0.5467	0.4656	0.6584	-1.0	0.3891	0.5467
5.7984	40.0	10000	10.3874	0.3801	0.7088	0.3675	0.2762	0.5912	-1.0	0.333	0.5433	0.5483	0.4591	0.6708	-1.0	0.3801	0.5483
5.7984	41.0	10250	10.5826	0.3795	0.7072	0.3694	0.2795	0.573	-1.0	0.334	0.5464	0.5477	0.4624	0.6642	-1.0	0.3795	0.5477
5.6742	42.0	10500	11.0054	0.3509	0.6604	0.3317	0.245	0.5826	-1.0	0.3259	0.5436	0.5467	0.4565	0.6708	-1.0	0.3509	0.5467
5.6742	43.0	10750	10.4947	0.3784	0.7003	0.3535	0.2857	0.5742	-1.0	0.3371	0.5421	0.543	0.4548	0.6642	-1.0	0.3784	0.543
5.5503	44.0	11000	10.2264	0.3893	0.7205	0.3594	0.2853	0.5909	-1.0	0.3377	0.5486	0.5489	0.4602	0.6708	-1.0	0.3893	0.5489
5.5503	45.0	11250	10.9303	0.3524	0.6437	0.3317	0.2591	0.5397	-1.0	0.3308	0.5455	0.5539	0.4667	0.6737	-1.0	0.3524	0.5539
5.4227	46.0	11500	10.5823	0.3508	0.6572	0.3243	0.2568	0.5511	-1.0	0.3246	0.5421	0.5458	0.4581	0.6664	-1.0	0.3508	0.5458
5.4227	47.0	11750	11.2549	0.3394	0.6428	0.3142	0.2564	0.5368	-1.0	0.3274	0.5517	0.553	0.4758	0.6591	-1.0	0.3394	0.553
5.2645	48.0	12000	10.5458	0.3934	0.7392	0.361	0.2914	0.5843	-1.0	0.3386	0.5364	0.5364	0.4468	0.6599	-1.0	0.3934	0.5364
5.2645	49.0	12250	11.6902	0.3063	0.5692	0.3054	0.231	0.4689	-1.0	0.3044	0.49	0.4928	0.4253	0.5818	-1.0	0.3063	0.4928
5.1936	50.0	12500	11.0786	0.3531	0.6576	0.3268	0.2595	0.5654	-1.0	0.3417	0.5389	0.5393	0.4672	0.6387	-1.0	0.3531	0.5393
5.1936	51.0	12750	10.5634	0.3821	0.7104	0.3497	0.2779	0.5857	-1.0	0.3383	0.5474	0.5477	0.457	0.6715	-1.0	0.3821	0.5477
5.0483	52.0	13000	10.6670	0.3724	0.691	0.3583	0.2679	0.592	-1.0	0.3393	0.5467	0.5514	0.4656	0.6693	-1.0	0.3724	0.5514
5.0483	53.0	13250	11.0286	0.3797	0.7086	0.3553	0.2761	0.5911	-1.0	0.3389	0.5511	0.5517	0.4677	0.6672	-1.0	0.3797	0.5517
5.0368	54.0	13500	11.2718	0.3602	0.6668	0.3426	0.2625	0.5682	-1.0	0.333	0.5483	0.5502	0.4683	0.6628	-1.0	0.3602	0.5502
5.0368	55.0	13750	10.9760	0.407	0.7375	0.3933	0.3114	0.5827	-1.0	0.3477	0.5442	0.5442	0.464	0.6547	-1.0	0.407	0.5442
4.8627	56.0	14000	11.2549	0.3839	0.7189	0.3607	0.2786	0.5895	-1.0	0.3361	0.5417	0.5427	0.4505	0.6693	-1.0	0.3839	0.5427
4.8627	57.0	14250	11.1423	0.382	0.7048	0.3683	0.2798	0.5878	-1.0	0.3489	0.5436	0.5439	0.4575	0.6628	-1.0	0.382	0.5439
4.8442	58.0	14500	10.8398	0.3947	0.7216	0.3773	0.2989	0.5843	-1.0	0.3408	0.5436	0.5436	0.4591	0.6599	-1.0	0.3947	0.5436
4.8442	59.0	14750	11.2393	0.3921	0.7317	0.3712	0.2867	0.589	-1.0	0.3461	0.5396	0.5396	0.4468	0.6672	-1.0	0.3921	0.5396
4.6770	60.0	15000	11.6135	0.3645	0.67	0.3507	0.2666	0.5635	-1.0	0.3343	0.5249	0.5249	0.4441	0.6365	-1.0	0.3645	0.5249
4.6770	61.0	15250	12.1106	0.3731	0.69	0.3486	0.2731	0.582	-1.0	0.3368	0.5433	0.5433	0.4608	0.6569	-1.0	0.3731	0.5433
4.6057	62.0	15500	12.0707	0.3668	0.6774	0.347	0.2669	0.5791	-1.0	0.3405	0.5411	0.5411	0.4591	0.654	-1.0	0.3668	0.5411
4.6057	63.0	15750	11.3291	0.3849	0.7104	0.3654	0.2827	0.5906	-1.0	0.3393	0.5464	0.5467	0.4618	0.6635	-1.0	0.3849	0.5467
4.4775	64.0	16000	11.3800	0.3765	0.6993	0.3553	0.2811	0.577	-1.0	0.3411	0.5421	0.5433	0.4591	0.6591	-1.0	0.3765	0.5433
4.4775	65.0	16250	11.8352	0.3569	0.6716	0.3304	0.267	0.5431	-1.0	0.3383	0.5137	0.5137	0.4376	0.619	-1.0	0.3569	0.5137
4.3231	66.0	16500	11.9371	0.3397	0.6269	0.3129	0.2694	0.4909	-1.0	0.3368	0.4941	0.4944	0.4435	0.5657	-1.0	0.3397	0.4944
4.3231	67.0	16750	11.6985	0.2112	0.3917	0.192	0.1705	0.297	-1.0	0.2495	0.3143	0.3143	0.2882	0.3453	-1.0	0.2112	0.3143
4.2943	68.0	17000	11.6084	0.3814	0.7077	0.3569	0.2846	0.5818	-1.0	0.3389	0.5411	0.5411	0.4522	0.6635	-1.0	0.3814	0.5411
4.2943	69.0	17250	11.8180	0.3346	0.6182	0.3136	0.26	0.4899	-1.0	0.3265	0.4857	0.4857	0.4242	0.5715	-1.0	0.3346	0.4857
4.1620	70.0	17500	11.8973	0.3303	0.6154	0.3059	0.2583	0.484	-1.0	0.3231	0.4829	0.4829	0.4237	0.5657	-1.0	0.3303	0.4829
4.1620	71.0	17750	11.9496	0.3844	0.7109	0.3613	0.2836	0.5825	-1.0	0.3449	0.5421	0.5421	0.4559	0.6606	-1.0	0.3844	0.5421
4.1195	72.0	18000	11.9163	0.3543	0.6575	0.338	0.2653	0.5384	-1.0	0.3424	0.505	0.505	0.4296	0.6095	-1.0	0.3543	0.505
4.1195	73.0	18250	12.1284	0.3799	0.6992	0.3559	0.2808	0.5809	-1.0	0.3374	0.538	0.538	0.4478	0.662	-1.0	0.3799	0.538
3.9959	74.0	18500	12.3617	0.3486	0.649	0.3336	0.2694	0.5219	-1.0	0.329	0.5103	0.5103	0.4473	0.5978	-1.0	0.3486	0.5103
3.9959	75.0	18750	12.1991	0.3657	0.6775	0.3466	0.279	0.5496	-1.0	0.3358	0.5255	0.5255	0.4522	0.627	-1.0	0.3657	0.5255
3.9293	76.0	19000	12.2925	0.3502	0.6477	0.3346	0.2734	0.5142	-1.0	0.3296	0.5044	0.5044	0.4446	0.5876	-1.0	0.3502	0.5044
3.9293	77.0	19250	12.3037	0.3425	0.6383	0.3213	0.2742	0.4895	-1.0	0.3321	0.4894	0.4894	0.4398	0.5591	-1.0	0.3425	0.4894
3.8107	78.0	19500	12.3505	0.3535	0.6548	0.3336	0.2773	0.5183	-1.0	0.3343	0.5072	0.5072	0.4468	0.5912	-1.0	0.3535	0.5072
3.8107	79.0	19750	12.4484	0.3741	0.6989	0.3568	0.2812	0.5647	-1.0	0.3361	0.5343	0.5343	0.4543	0.6445	-1.0	0.3741	0.5343
3.8050	80.0	20000	12.4474	0.3698	0.6838	0.3481	0.2804	0.5595	-1.0	0.3355	0.5318	0.5343	0.4575	0.6401	-1.0	0.3698	0.5343

Framework versions

Transformers 5.9.0
Pytorch 2.8.0+cu128
Datasets 4.2.0
Tokenizers 0.22.2

dariacuna/rtdetr-v2-r101-final

rtdetr-v2-r101-final

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dariacuna/
rtdetr-v2-r101-final