learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 1337
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: 25.0
mixed_precision_training: Native AMP

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 Exit	Mar 100 Exit	Map Fireextinguisher	Mar 100 Fireextinguisher	Map Chair	Mar 100 Chair	Map Clock	Mar 100 Clock	Map Trashbin	Mar 100 Trashbin	Map Printer	Mar 100 Printer	Map Screen	Mar 100 Screen
0.8218	1.0	112	0.9142	0.6911	0.8734	0.7937	0.2594	0.6244	0.7302	0.6551	0.7783	0.7887	0.3233	0.7267	0.8169	0.6682	0.7519	0.6719	0.7531	0.6624	0.7687	0.761	0.7963	0.3997	0.64	0.8667	0.9	0.8078	0.9111
0.801	2.0	224	0.8670	0.7147	0.8926	0.8306	0.2687	0.62	0.7324	0.6732	0.7871	0.7982	0.3294	0.7339	0.8116	0.6883	0.7577	0.7121	0.7797	0.6997	0.7982	0.7695	0.7889	0.4928	0.685	0.871	0.9	0.7696	0.8778
0.7682	3.0	336	0.9007	0.7075	0.9011	0.8123	0.2875	0.6152	0.7243	0.6701	0.7838	0.794	0.401	0.7275	0.8217	0.687	0.7442	0.7061	0.7741	0.6667	0.7804	0.7607	0.7963	0.4857	0.72	0.8775	0.8875	0.769	0.8556
0.7507	4.0	448	0.8265	0.7392	0.9242	0.8584	0.3972	0.6715	0.7561	0.6817	0.808	0.8202	0.4516	0.7653	0.8352	0.705	0.7654	0.7247	0.7811	0.709	0.8117	0.783	0.8333	0.5202	0.715	0.8842	0.9125	0.848	0.9222
0.7301	5.0	560	0.8271	0.7227	0.9141	0.8282	0.3326	0.6377	0.7385	0.6691	0.7986	0.8064	0.4591	0.7376	0.8487	0.7092	0.7942	0.7371	0.7902	0.7047	0.7969	0.7924	0.8481	0.5106	0.675	0.8083	0.8625	0.7967	0.8778
0.7197	6.0	672	0.8127	0.7561	0.9331	0.8718	0.3553	0.6725	0.7661	0.6901	0.8124	0.8204	0.4527	0.7664	0.8489	0.7203	0.7788	0.7444	0.8007	0.6974	0.8	0.7891	0.8148	0.5839	0.725	0.8725	0.9125	0.8853	0.9111
0.7041	7.0	784	0.7735	0.7637	0.9447	0.8864	0.4044	0.6757	0.7658	0.6935	0.8205	0.8326	0.5206	0.7725	0.8235	0.7446	0.8	0.7543	0.8154	0.7246	0.8209	0.7881	0.8407	0.5854	0.715	0.8675	0.925	0.8814	0.9111
0.7111	8.0	896	0.7977	0.7312	0.9297	0.8389	0.3618	0.6202	0.7366	0.6735	0.7931	0.8146	0.419	0.7552	0.8517	0.7106	0.7981	0.7485	0.8028	0.7261	0.8178	0.7699	0.8074	0.486	0.665	0.8737	0.9	0.8038	0.9111
0.6902	9.0	1008	0.7777	0.7398	0.9262	0.8423	0.4019	0.6672	0.7482	0.6855	0.8141	0.8267	0.4626	0.7678	0.849	0.7328	0.7981	0.7337	0.8035	0.7393	0.8442	0.777	0.8074	0.499	0.685	0.8638	0.9375	0.8332	0.9111
0.6712	10.0	1120	0.7881	0.7498	0.9418	0.8711	0.4102	0.6709	0.7691	0.6778	0.8058	0.815	0.4473	0.7647	0.843	0.6979	0.7712	0.7497	0.8014	0.7325	0.8325	0.7693	0.8111	0.5833	0.725	0.8505	0.875	0.8655	0.8889
0.6763	11.0	1232	0.7937	0.7666	0.9359	0.8797	0.3943	0.6884	0.7803	0.6931	0.8147	0.8237	0.4368	0.7714	0.8322	0.7247	0.7788	0.7458	0.7944	0.727	0.8215	0.7673	0.8037	0.5958	0.695	0.9138	0.95	0.8916	0.9222
0.6792	12.0	1344	0.8028	0.7465	0.9163	0.8525	0.3173	0.6541	0.7807	0.6781	0.8056	0.8187	0.3531	0.7869	0.882	0.7167	0.775	0.7393	0.7874	0.7217	0.8166	0.7675	0.7889	0.5595	0.695	0.886	0.9125	0.8346	0.9556
0.6844	13.0	1456	0.7748	0.7528	0.9263	0.8639	0.3421	0.6686	0.776	0.6968	0.8168	0.8265	0.4386	0.7729	0.8377	0.7173	0.7923	0.7556	0.8021	0.7344	0.8252	0.7799	0.8222	0.5131	0.695	0.9105	0.9375	0.8591	0.9111
0.667	14.0	1568	0.7876	0.7551	0.9325	0.8741	0.3361	0.6796	0.7733	0.6921	0.8056	0.8199	0.3841	0.7683	0.8661	0.7108	0.7865	0.7369	0.793	0.7277	0.8141	0.7921	0.8259	0.5695	0.685	0.8835	0.9125	0.8654	0.9222
0.6596	15.0	1680	0.7691	0.7683	0.9294	0.8897	0.3874	0.6794	0.787	0.6952	0.8234	0.8342	0.4475	0.7897	0.8442	0.7322	0.7923	0.7666	0.8231	0.7281	0.8307	0.8055	0.837	0.5706	0.72	0.8986	0.925	0.8762	0.9111
0.6349	16.0	1792	0.7581	0.7683	0.9414	0.8772	0.3607	0.6942	0.7746	0.7047	0.8307	0.8391	0.4571	0.8117	0.8584	0.7252	0.8019	0.7592	0.8161	0.7307	0.8331	0.8069	0.8444	0.5626	0.72	0.9082	0.925	0.8853	0.9333
0.6433	17.0	1904	0.7571	0.7604	0.9339	0.881	0.3855	0.6851	0.7799	0.6981	0.8191	0.839	0.4629	0.8256	0.8329	0.7378	0.8077	0.7639	0.8168	0.7335	0.835	0.7824	0.8222	0.5558	0.725	0.8643	0.9	0.8849	0.9667
0.6348	18.0	2016	0.7545	0.7708	0.9345	0.8934	0.3228	0.6765	0.7954	0.7003	0.8238	0.8348	0.4469	0.7905	0.8576	0.7379	0.8019	0.7763	0.8182	0.7278	0.8245	0.7998	0.8407	0.5623	0.7	0.9131	0.925	0.8783	0.9333
0.6322	19.0	2128	0.7642	0.7651	0.931	0.8875	0.3233	0.6763	0.7673	0.6998	0.818	0.8329	0.4618	0.7852	0.835	0.7146	0.7827	0.7603	0.8273	0.7405	0.8294	0.795	0.8333	0.54	0.71	0.9184	0.925	0.887	0.9222
0.6219	20.0	2240	0.7587	0.7702	0.9434	0.8849	0.4091	0.6879	0.7744	0.6981	0.8254	0.8447	0.4631	0.8049	0.8641	0.734	0.8019	0.7715	0.8252	0.7387	0.8356	0.8136	0.8481	0.5692	0.72	0.8794	0.9375	0.8849	0.9444
0.6105	21.0	2352	0.7471	0.7701	0.9393	0.8868	0.4202	0.678	0.7805	0.6996	0.8218	0.8356	0.4765	0.7799	0.8389	0.7193	0.7923	0.7781	0.8259	0.7343	0.8337	0.8069	0.8444	0.5684	0.715	0.9144	0.9375	0.8691	0.9
0.6123	22.0	2464	0.7461	0.7691	0.9426	0.8919	0.4237	0.6834	0.7659	0.6897	0.8198	0.8336	0.4648	0.7924	0.8395	0.7271	0.7942	0.7794	0.828	0.7359	0.8301	0.7978	0.8333	0.5777	0.715	0.8965	0.9125	0.8695	0.9222
0.6125	23.0	2576	0.7466	0.7649	0.9344	0.8811	0.4351	0.6806	0.7737	0.6987	0.821	0.8323	0.4805	0.7883	0.8369	0.7274	0.7962	0.7802	0.8259	0.7339	0.8221	0.7908	0.8296	0.5378	0.705	0.911	0.925	0.8735	0.9222
0.616	24.0	2688	0.7563	0.7612	0.9365	0.872	0.4332	0.6812	0.7718	0.698	0.8246	0.8337	0.4809	0.7915	0.8422	0.7376	0.8019	0.7817	0.8273	0.7296	0.8184	0.793	0.8333	0.5472	0.72	0.8612	0.9125	0.8778	0.9222
0.6079	25.0	2800	0.7451	0.7714	0.9402	0.8807	0.4279	0.6977	0.7784	0.704	0.8268	0.8378	0.4723	0.7949	0.8414	0.7325	0.7923	0.7845	0.8203	0.7354	0.8301	0.7962	0.837	0.5619	0.725	0.8914	0.9375	0.8976	0.9222

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Vasily-Lapinsky/Indoor-RT-Detrv2

rtdetr_v2_r18vd-finetuned-indoor-batch8-loss-finall

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Vasily-Lapinsky/
Indoor-RT-Detrv2