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	12.9857	0.3347	0.6338	0.3131	0.267	0.442	-1.0	0.2994	0.5688	0.6143	0.5328	0.7263	-1.0	0.3347	0.6143
157.0165	2.0	500	6.5751	0.4678	0.8273	0.4773	0.3817	0.5874	-1.0	0.3629	0.5972	0.6355	0.5839	0.7066	-1.0	0.4678	0.6355
157.0165	3.0	750	6.6310	0.448	0.811	0.4343	0.3512	0.5929	-1.0	0.3636	0.6093	0.6442	0.5898	0.719	-1.0	0.448	0.6442
10.4609	4.0	1000	6.1485	0.4613	0.8478	0.4233	0.3763	0.5831	-1.0	0.3639	0.5919	0.6296	0.5801	0.6978	-1.0	0.4613	0.6296
10.4609	5.0	1250	6.2256	0.4705	0.8631	0.4396	0.3925	0.585	-1.0	0.3807	0.5978	0.6093	0.5634	0.6715	-1.0	0.4705	0.6093
9.2738	6.0	1500	6.3437	0.444	0.8241	0.3952	0.3603	0.5807	-1.0	0.3701	0.5941	0.6134	0.5613	0.6854	-1.0	0.444	0.6134
9.2738	7.0	1750	6.0862	0.4454	0.8374	0.3983	0.362	0.5811	-1.0	0.3692	0.5816	0.5847	0.5204	0.6745	-1.0	0.4454	0.5847
8.7948	8.0	2000	6.4203	0.4313	0.8177	0.3773	0.3434	0.5833	-1.0	0.3751	0.5885	0.5919	0.5296	0.6788	-1.0	0.4313	0.5919
8.7948	9.0	2250	6.4039	0.4502	0.8489	0.3832	0.368	0.575	-1.0	0.3748	0.5888	0.5919	0.5296	0.6781	-1.0	0.4502	0.5919
8.2001	10.0	2500	6.5057	0.4151	0.7943	0.3739	0.3291	0.5755	-1.0	0.3542	0.5654	0.5713	0.5059	0.6628	-1.0	0.4151	0.5713
8.2001	11.0	2750	6.5232	0.4134	0.7934	0.3477	0.3294	0.5773	-1.0	0.3667	0.5726	0.5791	0.5091	0.6766	-1.0	0.4134	0.5791
7.9274	12.0	3000	6.4883	0.3994	0.7426	0.3546	0.3398	0.5915	-1.0	0.371	0.5769	0.581	0.507	0.6839	-1.0	0.3994	0.581
7.9274	13.0	3250	6.7455	0.4084	0.7899	0.3769	0.3246	0.5669	-1.0	0.3502	0.5769	0.5782	0.514	0.6679	-1.0	0.4084	0.5782
7.5337	14.0	3500	6.6870	0.3522	0.6867	0.3056	0.2868	0.5696	-1.0	0.3414	0.5601	0.5617	0.4871	0.6642	-1.0	0.3522	0.5617
7.5337	15.0	3750	6.7858	0.4071	0.7759	0.3628	0.3161	0.5839	-1.0	0.3474	0.5611	0.5645	0.4882	0.6708	-1.0	0.4071	0.5645
7.2274	16.0	4000	6.8005	0.4138	0.799	0.3658	0.3296	0.5672	-1.0	0.353	0.5526	0.5548	0.4801	0.6584	-1.0	0.4138	0.5548
7.2274	17.0	4250	6.8378	0.4063	0.7773	0.3584	0.3103	0.5838	-1.0	0.3495	0.5567	0.5589	0.4774	0.6715	-1.0	0.4063	0.5589
6.8940	18.0	4500	6.7952	0.4196	0.7879	0.3808	0.3343	0.5752	-1.0	0.3551	0.5555	0.5555	0.4919	0.6445	-1.0	0.4196	0.5555
6.8940	19.0	4750	6.9049	0.4091	0.7615	0.3621	0.3192	0.5836	-1.0	0.3561	0.5607	0.5623	0.4925	0.6584	-1.0	0.4091	0.5623
6.6243	20.0	5000	6.8862	0.4061	0.7768	0.3702	0.3177	0.5704	-1.0	0.3533	0.5517	0.5533	0.4823	0.6518	-1.0	0.4061	0.5533
6.6243	21.0	5250	6.9503	0.3998	0.768	0.3541	0.3144	0.5676	-1.0	0.3514	0.5458	0.5464	0.4677	0.6562	-1.0	0.3998	0.5464
6.4517	22.0	5500	7.1249	0.3814	0.7396	0.3305	0.2965	0.5668	-1.0	0.3555	0.5639	0.5648	0.5022	0.6526	-1.0	0.3814	0.5648
6.4517	23.0	5750	7.2326	0.3854	0.7306	0.3575	0.2971	0.5691	-1.0	0.3489	0.5567	0.5567	0.486	0.6555	-1.0	0.3854	0.5567
6.1793	24.0	6000	7.3171	0.3809	0.7254	0.3487	0.2863	0.5721	-1.0	0.3561	0.5561	0.5561	0.4887	0.6496	-1.0	0.3809	0.5561
6.1793	25.0	6250	7.2272	0.3811	0.7305	0.3234	0.2839	0.577	-1.0	0.3427	0.553	0.5536	0.4753	0.662	-1.0	0.3811	0.5536
6.0039	26.0	6500	7.4822	0.3686	0.7205	0.311	0.2728	0.5591	-1.0	0.3421	0.5399	0.5402	0.4672	0.6416	-1.0	0.3686	0.5402
6.0039	27.0	6750	7.1129	0.3963	0.7627	0.3446	0.3078	0.5676	-1.0	0.3442	0.5542	0.5551	0.4844	0.654	-1.0	0.3963	0.5551
5.9360	28.0	7000	7.3370	0.3633	0.6967	0.3139	0.2715	0.5692	-1.0	0.3461	0.5551	0.5564	0.4887	0.6518	-1.0	0.3633	0.5564
5.9360	29.0	7250	7.3790	0.3946	0.7533	0.3366	0.3045	0.5678	-1.0	0.3436	0.5495	0.5502	0.4737	0.6562	-1.0	0.3946	0.5502
5.6833	30.0	7500	7.5877	0.3488	0.6846	0.2918	0.2522	0.5678	-1.0	0.3383	0.5523	0.553	0.4866	0.646	-1.0	0.3488	0.553
5.6833	31.0	7750	7.6738	0.3587	0.7003	0.3105	0.2594	0.5736	-1.0	0.3293	0.5433	0.5433	0.472	0.6423	-1.0	0.3587	0.5433
5.5370	32.0	8000	7.4927	0.3634	0.7046	0.3142	0.2691	0.5788	-1.0	0.3324	0.5508	0.5511	0.4823	0.6474	-1.0	0.3634	0.5511
5.5370	33.0	8250	7.6265	0.3601	0.6808	0.2988	0.2552	0.5764	-1.0	0.3402	0.5526	0.5526	0.4796	0.654	-1.0	0.3601	0.5526
5.4335	34.0	8500	7.5527	0.3667	0.6913	0.3242	0.2684	0.5691	-1.0	0.3439	0.5567	0.5567	0.4925	0.6467	-1.0	0.3667	0.5567
5.4335	35.0	8750	7.5992	0.3519	0.6694	0.3206	0.2541	0.5682	-1.0	0.3374	0.5651	0.5651	0.5027	0.6511	-1.0	0.3519	0.5651
5.3546	36.0	9000	7.7407	0.3656	0.6858	0.3187	0.2683	0.5692	-1.0	0.3358	0.5508	0.5508	0.479	0.6511	-1.0	0.3656	0.5508
5.3546	37.0	9250	7.6993	0.3698	0.703	0.3116	0.2759	0.5684	-1.0	0.3561	0.5586	0.5589	0.4909	0.654	-1.0	0.3698	0.5589
5.2478	38.0	9500	7.6946	0.3554	0.6717	0.3016	0.2573	0.5761	-1.0	0.3433	0.5573	0.5573	0.4855	0.6577	-1.0	0.3554	0.5573
5.2478	39.0	9750	7.7415	0.3505	0.6636	0.3001	0.2561	0.5714	-1.0	0.3343	0.5558	0.5561	0.4855	0.6547	-1.0	0.3505	0.5561
5.1150	40.0	10000	7.8854	0.365	0.6955	0.3161	0.2712	0.5712	-1.0	0.3464	0.5536	0.5539	0.478	0.6606	-1.0	0.365	0.5539
5.1150	41.0	10250	7.7656	0.3785	0.7156	0.3428	0.285	0.5718	-1.0	0.3526	0.562	0.5623	0.4962	0.6547	-1.0	0.3785	0.5623
4.9974	42.0	10500	8.1017	0.3537	0.676	0.2878	0.2535	0.5676	-1.0	0.3411	0.5536	0.5539	0.4844	0.6511	-1.0	0.3537	0.5539
4.9974	43.0	10750	7.8787	0.3805	0.7285	0.338	0.2813	0.5666	-1.0	0.3411	0.5477	0.5477	0.4747	0.6496	-1.0	0.3805	0.5477
4.9032	44.0	11000	7.8028	0.3897	0.7379	0.352	0.2995	0.5745	-1.0	0.3461	0.5508	0.5511	0.4774	0.654	-1.0	0.3897	0.5511
4.9032	45.0	11250	7.8030	0.3833	0.7301	0.3402	0.2889	0.577	-1.0	0.3458	0.5533	0.5533	0.4833	0.6511	-1.0	0.3833	0.5533
4.9094	46.0	11500	7.8685	0.3819	0.7237	0.3314	0.2807	0.5733	-1.0	0.3389	0.5511	0.5514	0.4763	0.6562	-1.0	0.3819	0.5514
4.9094	47.0	11750	7.7993	0.3892	0.7266	0.3494	0.2933	0.5821	-1.0	0.3433	0.5555	0.5555	0.4828	0.6569	-1.0	0.3892	0.5555
4.7600	48.0	12000	8.2617	0.3738	0.7051	0.3334	0.2819	0.5775	-1.0	0.343	0.553	0.553	0.478	0.6577	-1.0	0.3738	0.553
4.7600	49.0	12250	8.1176	0.3898	0.739	0.3463	0.2967	0.5776	-1.0	0.3483	0.5483	0.5483	0.4726	0.654	-1.0	0.3898	0.5483
4.6856	50.0	12500	8.3029	0.3703	0.6945	0.3261	0.2795	0.5736	-1.0	0.3442	0.5583	0.5583	0.4892	0.6547	-1.0	0.3703	0.5583
4.6856	51.0	12750	8.3532	0.3556	0.676	0.2974	0.2521	0.5704	-1.0	0.3405	0.5511	0.5511	0.4763	0.6555	-1.0	0.3556	0.5511
4.6088	52.0	13000	8.3079	0.3668	0.6899	0.3316	0.2638	0.5765	-1.0	0.3421	0.5539	0.5542	0.4812	0.6562	-1.0	0.3668	0.5542
4.6088	53.0	13250	8.4766	0.3584	0.6791	0.3158	0.2618	0.5767	-1.0	0.3464	0.5536	0.5539	0.4796	0.6577	-1.0	0.3584	0.5539
4.6155	54.0	13500	8.3768	0.3643	0.6913	0.3242	0.2686	0.5771	-1.0	0.3442	0.5517	0.5517	0.4817	0.6496	-1.0	0.3643	0.5517
4.6155	55.0	13750	8.3807	0.3671	0.6944	0.3329	0.2675	0.5715	-1.0	0.334	0.5514	0.5514	0.4823	0.6482	-1.0	0.3671	0.5514
4.4666	56.0	14000	8.3214	0.3711	0.6971	0.3298	0.2765	0.5735	-1.0	0.3424	0.5558	0.5558	0.486	0.6533	-1.0	0.3711	0.5558
4.4666	57.0	14250	8.4580	0.3482	0.6644	0.2976	0.25	0.569	-1.0	0.3274	0.5474	0.5474	0.472	0.6526	-1.0	0.3482	0.5474
4.4798	58.0	14500	8.3691	0.3789	0.7161	0.3422	0.2857	0.5717	-1.0	0.3433	0.552	0.552	0.4823	0.6496	-1.0	0.3789	0.552
4.4798	59.0	14750	8.5290	0.3591	0.68	0.3026	0.2622	0.5697	-1.0	0.3349	0.5508	0.5508	0.4801	0.6496	-1.0	0.3591	0.5508
4.3353	60.0	15000	8.4913	0.3662	0.6945	0.328	0.2663	0.5737	-1.0	0.3439	0.5542	0.5542	0.4849	0.6511	-1.0	0.3662	0.5542
4.3353	61.0	15250	8.5404	0.3617	0.6784	0.3117	0.2586	0.5705	-1.0	0.3293	0.547	0.547	0.472	0.6518	-1.0	0.3617	0.547
4.3049	62.0	15500	8.7612	0.3372	0.6465	0.2786	0.243	0.5484	-1.0	0.3324	0.534	0.534	0.464	0.627	-1.0	0.3372	0.534
4.3049	63.0	15750	8.6870	0.3567	0.6746	0.3091	0.2537	0.5672	-1.0	0.3405	0.5452	0.5452	0.4667	0.6496	-1.0	0.3567	0.5452
4.1902	64.0	16000	8.7313	0.3652	0.6879	0.3255	0.2639	0.5718	-1.0	0.3414	0.5508	0.5508	0.4758	0.6555	-1.0	0.3652	0.5508
4.1902	65.0	16250	8.6428	0.3752	0.7192	0.3084	0.2793	0.5655	-1.0	0.3458	0.548	0.548	0.4758	0.6489	-1.0	0.3752	0.548
4.1008	66.0	16500	8.5482	0.3812	0.7147	0.3394	0.2834	0.5699	-1.0	0.3474	0.5548	0.5548	0.4833	0.6547	-1.0	0.3812	0.5548
4.1008	67.0	16750	8.6272	0.369	0.6999	0.3153	0.2701	0.5705	-1.0	0.347	0.5498	0.5498	0.4742	0.6555	-1.0	0.369	0.5498
4.1115	68.0	17000	8.5289	0.3701	0.7013	0.3144	0.2733	0.57	-1.0	0.3489	0.5517	0.5517	0.479	0.6533	-1.0	0.3701	0.5517
4.1115	69.0	17250	8.6782	0.3781	0.7201	0.3197	0.2813	0.5703	-1.0	0.347	0.5492	0.5492	0.4726	0.6562	-1.0	0.3781	0.5492
4.0182	70.0	17500	8.6656	0.3891	0.7379	0.3305	0.293	0.5678	-1.0	0.348	0.5492	0.5492	0.4747	0.6533	-1.0	0.3891	0.5492
4.0182	71.0	17750	8.8023	0.3811	0.7164	0.3351	0.2843	0.5672	-1.0	0.3464	0.5483	0.5483	0.4737	0.6526	-1.0	0.3811	0.5483
4.0148	72.0	18000	8.8612	0.3798	0.7097	0.3335	0.2813	0.5769	-1.0	0.3502	0.5536	0.5536	0.4774	0.6599	-1.0	0.3798	0.5536
4.0148	73.0	18250	8.7673	0.3712	0.7022	0.3169	0.2739	0.5663	-1.0	0.3411	0.5461	0.5461	0.471	0.6511	-1.0	0.3712	0.5461
3.9108	74.0	18500	8.8232	0.3684	0.6966	0.321	0.271	0.5644	-1.0	0.3405	0.5442	0.5442	0.4677	0.6511	-1.0	0.3684	0.5442
3.9108	75.0	18750	8.9208	0.3693	0.6976	0.3152	0.27	0.5718	-1.0	0.3424	0.5467	0.5467	0.4704	0.6533	-1.0	0.3693	0.5467
3.8770	76.0	19000	8.9224	0.3713	0.7053	0.3085	0.2756	0.5644	-1.0	0.3417	0.5495	0.5495	0.4753	0.6533	-1.0	0.3713	0.5495
3.8770	77.0	19250	8.9318	0.3705	0.7005	0.3131	0.2725	0.5644	-1.0	0.3436	0.5452	0.5452	0.4694	0.6511	-1.0	0.3705	0.5452
3.8061	78.0	19500	8.9961	0.3708	0.7003	0.3155	0.2723	0.5672	-1.0	0.3449	0.5474	0.5474	0.471	0.654	-1.0	0.3708	0.5474
3.8061	79.0	19750	9.0419	0.3688	0.6937	0.3143	0.2691	0.5646	-1.0	0.3449	0.5455	0.5455	0.4688	0.6474	-1.0	0.3688	0.5455
3.8157	80.0	20000	9.0529	0.3654	0.6883	0.3105	0.2663	0.5634	-1.0	0.3436	0.5455	0.5455	0.4688	0.6474	-1.0	0.3654	0.5455

Framework versions

Transformers 5.9.0
Pytorch 2.8.0+cu128
Datasets 4.2.0
Tokenizers 0.22.2

dariacuna/rtdetr-v2-r34-final

rtdetr-v2-r34-final

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dariacuna/
rtdetr-v2-r34-final