learning_rate: 0.0001
train_batch_size: 2
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: linear
num_epochs: 20

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 Ball	Mar 100 Ball	Map Goalkeeper	Mar 100 Goalkeeper	Map Player	Mar 100 Player
296.278	1.0	149	62.3890	0.0999	0.1535	0.1216	0.0976	0.1516	-1.0	0.0078	0.0616	0.1808	0.1808	0.2414	-1.0	0.0	0.0	0.0	0.0	0.3997	0.7232
74.5246	2.0	298	25.7949	0.1643	0.236	0.1843	0.1638	0.2281	-1.0	0.0162	0.1175	0.3643	0.3753	0.2564	-1.0	0.0	0.0	0.0189	0.6969	0.6384	0.7605
23.5211	3.0	447	16.6520	0.1984	0.25	0.2246	0.1971	0.2759	-1.0	0.0111	0.2288	0.4151	0.4131	0.5609	-1.0	0.0	0.0	0.0371	0.8114	0.7566	0.849
18.7573	4.0	596	13.6105	0.2271	0.3101	0.2518	0.2264	0.2742	-1.0	0.0608	0.2627	0.4917	0.4901	0.5426	-1.0	0.1064	0.3159	0.0287	0.8139	0.7732	0.8368
14.4593	5.0	745	11.9880	0.2282	0.309	0.2486	0.2272	0.2805	-1.0	0.0774	0.2571	0.4835	0.4812	0.531	-1.0	0.1022	0.3023	0.028	0.7914	0.7824	0.8405
12.878	6.0	894	10.9109	0.3867	0.5529	0.418	0.3955	0.3944	-1.0	0.2517	0.3723	0.4836	0.4837	0.526	-1.0	0.1759	0.3349	0.6007	0.7629	0.7704	0.8367
11.4964	7.0	1043	10.0801	0.4098	0.5788	0.4432	0.4076	0.5423	-1.0	0.2558	0.3796	0.4805	0.4774	0.5626	-1.0	0.2016	0.3227	0.6587	0.7622	0.7792	0.8372
10.938	8.0	1192	9.6962	0.4168	0.5909	0.4511	0.4136	0.5736	-1.0	0.2622	0.384	0.4829	0.479	0.5953	-1.0	0.2148	0.3045	0.6771	0.7944	0.7753	0.8327
10.2185	9.0	1341	9.2201	0.4414	0.6237	0.4939	0.4461	0.4126	-1.0	0.2975	0.4088	0.5078	0.5043	0.5659	-1.0	0.3159	0.4023	0.6628	0.7886	0.7866	0.8403
10.1417	10.0	1490	8.8414	0.4258	0.5998	0.4616	0.432	0.4459	-1.0	0.2829	0.4079	0.5097	0.5112	0.5104	-1.0	0.2659	0.4256	0.6382	0.76	0.799	0.8533
9.6417	11.0	1639	8.9382	0.4158	0.5798	0.4758	0.4134	0.5169	-1.0	0.2758	0.401	0.5016	0.499	0.5324	-1.0	0.2131	0.3791	0.6644	0.7857	0.7857	0.8414
9.3618	12.0	1788	8.5653	0.4349	0.6074	0.5026	0.4337	0.5436	-1.0	0.2857	0.4042	0.5052	0.5022	0.5646	-1.0	0.2359	0.375	0.7111	0.8	0.7927	0.8458
9.1601	13.0	1937	8.5162	0.4346	0.6159	0.4669	0.4332	0.5417	-1.0	0.2803	0.3997	0.4998	0.497	0.5623	-1.0	0.2628	0.3795	0.6862	0.7778	0.7892	0.842
8.9938	14.0	2086	8.2598	0.4403	0.6165	0.4962	0.4379	0.5494	-1.0	0.2803	0.4002	0.5017	0.4986	0.5674	-1.0	0.2661	0.3773	0.7019	0.7806	0.7931	0.8489
8.7258	15.0	2235	8.2129	0.4507	0.6142	0.5192	0.4505	0.5363	-1.0	0.2931	0.4137	0.5149	0.5123	0.5611	-1.0	0.3076	0.4114	0.7154	0.8028	0.7796	0.8453
8.1655	16.0	2384	8.1459	0.4436	0.6071	0.4985	0.4455	0.4346	-1.0	0.2891	0.4125	0.5136	0.5134	0.5115	-1.0	0.3046	0.4091	0.6794	0.7946	0.7905	0.8507
8.1808	17.0	2533	8.1069	0.4426	0.6231	0.4971	0.4436	0.5167	-1.0	0.2868	0.4078	0.5074	0.5057	0.5667	-1.0	0.2985	0.4023	0.6942	0.7886	0.7776	0.8388
8.45	18.0	2682	7.9322	0.4443	0.6157	0.4739	0.4427	0.5757	-1.0	0.2839	0.4082	0.5104	0.5066	0.5963	-1.0	0.2715	0.4023	0.704	0.7833	0.8018	0.856
8.0344	19.0	2831	7.9556	0.4464	0.6133	0.4963	0.4456	0.5388	-1.0	0.2866	0.4113	0.5129	0.5111	0.5584	-1.0	0.2678	0.4093	0.722	0.7914	0.7956	0.851
7.9178	20.0	2980	7.7323	0.448	0.6146	0.4994	0.4453	0.5526	-1.0	0.286	0.4045	0.5073	0.5039	0.571	-1.0	0.2858	0.4068	0.7008	0.7649	0.8053	0.8576

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.1
Tokenizers 0.21.0

theButcher22/deta-swin-large

deta-swin-large

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

theButcher22/
deta-swin-large