ms_detr_finetuned_diana

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3617
Map: 0.6874
Map 50: 0.789
Map 75: 0.7871
Map Small: -1.0
Map Medium: 0.7147
Map Large: 0.6892
Mar 1: 0.0969
Mar 10: 0.7163
Mar 100: 0.7819
Mar Small: -1.0
Mar Medium: 0.75
Mar Large: 0.7947
Map Per Class: -1.0
Mar 100 Per Class: -1.0
Classes: 0

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 100

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 Per Class	Mar 100 Per Class
2.892	1.0	10	2.2713	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.717	2.0	20	1.6999	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.5162	3.0	30	1.4320	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.3622	4.0	40	1.2202	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.1926	5.0	50	1.1617	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.2362	6.0	60	1.1772	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.3114	7.0	70	1.0437	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.1188	8.0	80	0.9656	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.9169	9.0	90	0.8787	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.7998	10.0	100	0.7928	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.7385	11.0	110	0.6800	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.6697	12.0	120	0.6025	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.4984	13.0	130	0.5722	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.5245	14.0	140	0.5460	0.0079	0.0099	0.0099	-1.0	0.0238	0.0	0.005	0.005	0.005	-1.0	0.0174	0.0	-1.0	-1.0
0.3993	15.0	150	0.5030	0.0168	0.0198	0.0198	-1.0	0.0267	0.0079	0.0106	0.0106	0.0106	-1.0	0.0196	0.007	-1.0	-1.0
0.4789	16.0	160	0.4662	0.2283	0.2665	0.2665	-1.0	0.1539	0.2598	0.0794	0.2438	0.2438	-1.0	0.1543	0.2798	-1.0	-1.0
0.5485	17.0	170	0.4586	0.2041	0.2369	0.2369	-1.0	0.1679	0.2202	0.0731	0.2175	0.2175	-1.0	0.1717	0.236	-1.0	-1.0
0.3419	18.0	180	0.4637	0.3674	0.4292	0.4292	-1.0	0.2666	0.4049	0.0856	0.3994	0.3994	-1.0	0.2696	0.4518	-1.0	-1.0
0.4885	19.0	190	0.5509	0.4468	0.5407	0.5314	-1.0	0.354	0.4898	0.0925	0.5156	0.5156	-1.0	0.3761	0.5719	-1.0	-1.0
0.3336	20.0	200	0.5122	0.1809	0.2149	0.2149	-1.0	0.0802	0.2257	0.0763	0.195	0.195	-1.0	0.0783	0.2421	-1.0	-1.0
0.3471	21.0	210	0.4619	0.4291	0.519	0.519	-1.0	0.3115	0.4787	0.0906	0.465	0.4706	-1.0	0.3174	0.5325	-1.0	-1.0
0.3953	22.0	220	0.4313	0.4938	0.5927	0.581	-1.0	0.3728	0.5476	0.0887	0.5319	0.5437	-1.0	0.3891	0.6061	-1.0	-1.0
0.5373	23.0	230	0.4308	0.5264	0.6339	0.623	-1.0	0.4223	0.5733	0.0938	0.5744	0.5894	-1.0	0.4391	0.65	-1.0	-1.0
0.3092	24.0	240	0.4193	0.5561	0.6661	0.6571	-1.0	0.4775	0.5927	0.0944	0.5844	0.6169	-1.0	0.5022	0.6632	-1.0	-1.0
0.477	25.0	250	0.4125	0.41	0.4769	0.4769	-1.0	0.3283	0.4467	0.1006	0.4387	0.4487	-1.0	0.3348	0.4947	-1.0	-1.0
0.3867	26.0	260	0.4114	0.6146	0.743	0.7168	-1.0	0.6036	0.6294	0.0938	0.6381	0.6981	-1.0	0.6304	0.7254	-1.0	-1.0
0.3658	27.0	270	0.4001	0.6643	0.7856	0.7727	-1.0	0.6505	0.6817	0.1	0.6787	0.7456	-1.0	0.6935	0.7667	-1.0	-1.0
0.3053	28.0	280	0.4282	0.5218	0.6326	0.6201	-1.0	0.4163	0.5676	0.095	0.5631	0.5844	-1.0	0.4261	0.6482	-1.0	-1.0
0.3105	29.0	290	0.4398	0.6187	0.7567	0.7331	-1.0	0.6045	0.6336	0.0919	0.6413	0.6944	-1.0	0.6478	0.7132	-1.0	-1.0
0.3713	30.0	300	0.4202	0.5643	0.6683	0.6673	-1.0	0.4959	0.5996	0.0931	0.5969	0.6306	-1.0	0.5348	0.6693	-1.0	-1.0
0.2874	31.0	310	0.4018	0.6327	0.7547	0.7357	-1.0	0.5647	0.6713	0.0981	0.6575	0.715	-1.0	0.5957	0.7632	-1.0	-1.0
0.2809	32.0	320	0.4007	0.6444	0.7618	0.7412	-1.0	0.6189	0.667	0.1013	0.6694	0.7381	-1.0	0.6587	0.7702	-1.0	-1.0
0.2801	33.0	330	0.4528	0.6125	0.7386	0.727	-1.0	0.5399	0.6519	0.0938	0.6338	0.6994	-1.0	0.5739	0.75	-1.0	-1.0
0.3484	34.0	340	0.4233	0.6226	0.7505	0.7381	-1.0	0.6268	0.642	0.0988	0.64	0.7194	-1.0	0.6543	0.7456	-1.0	-1.0
0.3591	35.0	350	0.4156	0.6475	0.759	0.7576	-1.0	0.6733	0.6536	0.095	0.6712	0.74	-1.0	0.7065	0.7535	-1.0	-1.0
0.2204	36.0	360	0.4363	0.5723	0.6577	0.6577	-1.0	0.548	0.5962	0.0956	0.6112	0.6456	-1.0	0.5717	0.6754	-1.0	-1.0
0.3912	37.0	370	0.4261	0.6855	0.8128	0.8032	-1.0	0.6777	0.7012	0.1006	0.7063	0.7806	-1.0	0.7217	0.8044	-1.0	-1.0
0.3377	38.0	380	0.4260	0.6743	0.7969	0.7847	-1.0	0.6147	0.7086	0.1025	0.69	0.7444	-1.0	0.6413	0.786	-1.0	-1.0
0.3047	39.0	390	0.4211	0.6519	0.7793	0.7647	-1.0	0.6241	0.6742	0.1006	0.6681	0.7175	-1.0	0.6457	0.7465	-1.0	-1.0
0.2563	40.0	400	0.4313	0.6441	0.769	0.769	-1.0	0.724	0.6211	0.1006	0.6669	0.7275	-1.0	0.7543	0.7167	-1.0	-1.0
0.3127	41.0	410	0.4297	0.5739	0.6679	0.6593	-1.0	0.5456	0.5969	0.0975	0.5987	0.6438	-1.0	0.563	0.6763	-1.0	-1.0
0.2782	42.0	420	0.4133	0.6234	0.7267	0.7179	-1.0	0.6279	0.6346	0.0962	0.6394	0.7019	-1.0	0.6457	0.7246	-1.0	-1.0
0.2965	43.0	430	0.4304	0.5708	0.684	0.664	-1.0	0.5152	0.6021	0.0938	0.6225	0.6538	-1.0	0.5326	0.7026	-1.0	-1.0
0.2599	44.0	440	0.4240	0.6451	0.7575	0.7445	-1.0	0.67	0.6528	0.0975	0.6762	0.7506	-1.0	0.6978	0.7719	-1.0	-1.0
0.2821	45.0	450	0.4361	0.695	0.8218	0.796	-1.0	0.7193	0.6945	0.0969	0.7287	0.79	-1.0	0.7587	0.8026	-1.0	-1.0
0.3117	46.0	460	0.4164	0.7032	0.8334	0.8051	-1.0	0.7114	0.7094	0.1019	0.7331	0.7925	-1.0	0.7391	0.814	-1.0	-1.0
0.2484	47.0	470	0.4007	0.6757	0.7805	0.7805	-1.0	0.7268	0.6714	0.1037	0.7088	0.7656	-1.0	0.7543	0.7702	-1.0	-1.0
0.3059	48.0	480	0.4269	0.651	0.7539	0.7496	-1.0	0.599	0.6866	0.0956	0.6819	0.7294	-1.0	0.6174	0.7746	-1.0	-1.0
0.2492	49.0	490	0.3877	0.6452	0.7506	0.7277	-1.0	0.6343	0.6624	0.09	0.6794	0.7244	-1.0	0.6565	0.7518	-1.0	-1.0
0.3828	50.0	500	0.4237	0.6721	0.7953	0.7744	-1.0	0.7174	0.6672	0.0887	0.7056	0.7606	-1.0	0.75	0.7649	-1.0	-1.0
0.2737	51.0	510	0.3713	0.6619	0.7594	0.7453	-1.0	0.6988	0.6615	0.0925	0.6919	0.7419	-1.0	0.7283	0.7474	-1.0	-1.0
0.3283	52.0	520	0.3737	0.6298	0.7286	0.7254	-1.0	0.6199	0.6431	0.0894	0.6575	0.7019	-1.0	0.6478	0.7237	-1.0	-1.0
0.2819	53.0	530	0.4077	0.6919	0.8094	0.8004	-1.0	0.7656	0.6797	0.0894	0.7106	0.7831	-1.0	0.7978	0.7772	-1.0	-1.0
0.2533	54.0	540	0.4056	0.7032	0.8249	0.8045	-1.0	0.724	0.7105	0.0962	0.7381	0.7962	-1.0	0.7522	0.814	-1.0	-1.0
0.3408	55.0	550	0.3916	0.667	0.771	0.7591	-1.0	0.6257	0.6997	0.0969	0.6913	0.7356	-1.0	0.65	0.7702	-1.0	-1.0
0.2069	56.0	560	0.3931	0.7054	0.8197	0.7998	-1.0	0.7399	0.7061	0.0962	0.7188	0.785	-1.0	0.7652	0.793	-1.0	-1.0
0.2572	57.0	570	0.4012	0.6993	0.8117	0.7932	-1.0	0.729	0.699	0.1025	0.7275	0.7862	-1.0	0.7609	0.7965	-1.0	-1.0
0.1786	58.0	580	0.3830	0.7114	0.8231	0.8047	-1.0	0.7666	0.7065	0.0975	0.7325	0.8056	-1.0	0.7935	0.8105	-1.0	-1.0
0.2185	59.0	590	0.3609	0.705	0.8153	0.806	-1.0	0.7388	0.6997	0.0919	0.7194	0.7912	-1.0	0.7783	0.7965	-1.0	-1.0
0.2219	60.0	600	0.3783	0.7086	0.8241	0.8092	-1.0	0.7106	0.7192	0.0969	0.74	0.805	-1.0	0.7391	0.8316	-1.0	-1.0
0.3003	61.0	610	0.4098	0.7118	0.8178	0.8085	-1.0	0.7534	0.7084	0.0919	0.7394	0.8138	-1.0	0.7891	0.8237	-1.0	-1.0
0.3144	62.0	620	0.4339	0.6867	0.7967	0.7794	-1.0	0.7074	0.6913	0.0894	0.7294	0.7875	-1.0	0.7435	0.8053	-1.0	-1.0
0.2323	63.0	630	0.4086	0.6733	0.7901	0.7787	-1.0	0.692	0.6792	0.0944	0.715	0.7719	-1.0	0.7239	0.7912	-1.0	-1.0
0.3114	64.0	640	0.3946	0.6801	0.7905	0.7658	-1.0	0.6803	0.6931	0.0981	0.7188	0.7713	-1.0	0.7043	0.7982	-1.0	-1.0
0.2579	65.0	650	0.3899	0.6658	0.7882	0.7637	-1.0	0.6463	0.6861	0.0988	0.7013	0.7613	-1.0	0.6739	0.7965	-1.0	-1.0
0.2312	66.0	660	0.3816	0.6567	0.7604	0.7548	-1.0	0.6226	0.6793	0.0969	0.6919	0.7506	-1.0	0.6478	0.7921	-1.0	-1.0
0.209	67.0	670	0.3967	0.699	0.8281	0.7953	-1.0	0.7061	0.7074	0.0906	0.7394	0.7994	-1.0	0.7348	0.8254	-1.0	-1.0
0.3113	68.0	680	0.4126	0.6991	0.8308	0.7838	-1.0	0.7222	0.6999	0.0962	0.7337	0.7994	-1.0	0.7522	0.8184	-1.0	-1.0
0.2045	69.0	690	0.4276	0.703	0.8227	0.7956	-1.0	0.7344	0.7016	0.0838	0.735	0.8006	-1.0	0.7652	0.8149	-1.0	-1.0
0.2002	70.0	700	0.4094	0.6843	0.7886	0.7836	-1.0	0.6975	0.6908	0.0919	0.7175	0.7788	-1.0	0.7239	0.8009	-1.0	-1.0
0.2065	71.0	710	0.4052	0.7065	0.8196	0.8056	-1.0	0.7149	0.7146	0.0981	0.7337	0.8	-1.0	0.7457	0.8219	-1.0	-1.0
0.2716	72.0	720	0.4000	0.7039	0.8294	0.8059	-1.0	0.7229	0.7079	0.0956	0.7344	0.7987	-1.0	0.7522	0.8175	-1.0	-1.0
0.2935	73.0	730	0.3905	0.652	0.7532	0.7475	-1.0	0.6084	0.6786	0.0906	0.7038	0.7525	-1.0	0.6326	0.8009	-1.0	-1.0
0.2137	74.0	740	0.3959	0.627	0.7307	0.725	-1.0	0.6212	0.6476	0.0906	0.6794	0.7312	-1.0	0.6522	0.7632	-1.0	-1.0
0.2075	75.0	750	0.3786	0.6542	0.769	0.7535	-1.0	0.659	0.6667	0.0906	0.71	0.7619	-1.0	0.6913	0.7904	-1.0	-1.0
0.1713	76.0	760	0.3836	0.6695	0.7851	0.7703	-1.0	0.6683	0.6859	0.0906	0.7138	0.7781	-1.0	0.7065	0.807	-1.0	-1.0
0.2233	77.0	770	0.3947	0.6659	0.775	0.7694	-1.0	0.714	0.6616	0.0913	0.705	0.7763	-1.0	0.7543	0.7851	-1.0	-1.0
0.2398	78.0	780	0.3835	0.6854	0.7997	0.7883	-1.0	0.7067	0.6903	0.0906	0.7212	0.785	-1.0	0.7391	0.8035	-1.0	-1.0
0.1906	79.0	790	0.3811	0.6901	0.8028	0.7974	-1.0	0.7128	0.6948	0.0956	0.7231	0.7931	-1.0	0.7478	0.8114	-1.0	-1.0
0.1823	80.0	800	0.3831	0.6721	0.7821	0.7709	-1.0	0.7005	0.6764	0.0906	0.7094	0.775	-1.0	0.737	0.7904	-1.0	-1.0
0.2266	81.0	810	0.3831	0.6973	0.8131	0.8007	-1.0	0.6966	0.7103	0.0969	0.7319	0.7969	-1.0	0.7326	0.8228	-1.0	-1.0
0.1812	82.0	820	0.3809	0.6934	0.8057	0.7933	-1.0	0.6984	0.707	0.0962	0.7262	0.7919	-1.0	0.7348	0.8149	-1.0	-1.0
0.1811	83.0	830	0.3820	0.6836	0.7953	0.7829	-1.0	0.6797	0.7021	0.0969	0.7156	0.7819	-1.0	0.7087	0.8114	-1.0	-1.0
0.1677	84.0	840	0.3851	0.6809	0.7891	0.7759	-1.0	0.6854	0.6942	0.0962	0.7163	0.7763	-1.0	0.7109	0.8026	-1.0	-1.0
0.159	85.0	850	0.3791	0.6802	0.7889	0.7765	-1.0	0.6799	0.6954	0.0962	0.715	0.775	-1.0	0.7087	0.8018	-1.0	-1.0
0.1646	86.0	860	0.3712	0.6856	0.7903	0.7785	-1.0	0.6683	0.7073	0.0975	0.7212	0.7788	-1.0	0.6978	0.8114	-1.0	-1.0
0.1618	87.0	870	0.3736	0.6817	0.7861	0.7753	-1.0	0.7037	0.6883	0.0969	0.72	0.7781	-1.0	0.7326	0.7965	-1.0	-1.0
0.144	88.0	880	0.3724	0.6804	0.7917	0.7688	-1.0	0.7067	0.6832	0.0962	0.7138	0.7719	-1.0	0.7304	0.7886	-1.0	-1.0
0.2508	89.0	890	0.3643	0.6792	0.7823	0.7692	-1.0	0.7062	0.6828	0.0969	0.7125	0.7706	-1.0	0.7283	0.7877	-1.0	-1.0
0.1579	90.0	900	0.3623	0.6996	0.8094	0.7962	-1.0	0.7204	0.7044	0.0962	0.7256	0.7937	-1.0	0.7522	0.8105	-1.0	-1.0
0.1625	91.0	910	0.3630	0.6985	0.8083	0.7959	-1.0	0.718	0.7034	0.0962	0.7256	0.7937	-1.0	0.7522	0.8105	-1.0	-1.0
0.1734	92.0	920	0.3626	0.6876	0.7894	0.787	-1.0	0.7165	0.6891	0.0969	0.7169	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1452	93.0	930	0.3619	0.6838	0.7887	0.7781	-1.0	0.7163	0.684	0.0969	0.7119	0.7769	-1.0	0.7522	0.7868	-1.0	-1.0
0.1764	94.0	940	0.3633	0.6833	0.7888	0.7782	-1.0	0.7162	0.6831	0.0969	0.7113	0.7763	-1.0	0.7522	0.786	-1.0	-1.0
0.1862	95.0	950	0.3633	0.6825	0.7889	0.7781	-1.0	0.7153	0.6826	0.0969	0.71	0.7756	-1.0	0.75	0.786	-1.0	-1.0
0.1855	96.0	960	0.3622	0.6878	0.7891	0.7873	-1.0	0.7147	0.6899	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.2982	97.0	970	0.3622	0.6877	0.789	0.7873	-1.0	0.7147	0.6896	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1764	98.0	980	0.3620	0.6877	0.789	0.7873	-1.0	0.7147	0.6896	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.2245	99.0	990	0.3618	0.6874	0.789	0.7871	-1.0	0.7147	0.6892	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1775	100.0	1000	0.3617	0.6874	0.789	0.7871	-1.0	0.7147	0.6892	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0

Framework versions

Transformers 4.41.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

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ms_detr_finetuned_diana

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3617

Map: 0.6874

Map 50: 0.789

Map 75: 0.7871

Map Small: -1.0

Map Medium: 0.7147

Map Large: 0.6892

Mar 1: 0.0969

Mar 10: 0.7163

Mar 100: 0.7819

Mar Small: -1.0

Mar Medium: 0.75

Mar Large: 0.7947

Map Per Class: -1.0

Mar 100 Per Class: -1.0

Classes: 0

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05

train_batch_size: 8

eval_batch_size: 8

seed: 42

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: cosine

num_epochs: 100

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 Per Class	Mar 100 Per Class
2.892	1.0	10	2.2713	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.717	2.0	20	1.6999	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.5162	3.0	30	1.4320	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.3622	4.0	40	1.2202	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.1926	5.0	50	1.1617	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.2362	6.0	60	1.1772	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.3114	7.0	70	1.0437	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
1.1188	8.0	80	0.9656	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.9169	9.0	90	0.8787	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.7998	10.0	100	0.7928	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.7385	11.0	110	0.6800	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.6697	12.0	120	0.6025	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.4984	13.0	130	0.5722	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	-1.0	-1.0
0.5245	14.0	140	0.5460	0.0079	0.0099	0.0099	-1.0	0.0238	0.0	0.005	0.005	0.005	-1.0	0.0174	0.0	-1.0	-1.0
0.3993	15.0	150	0.5030	0.0168	0.0198	0.0198	-1.0	0.0267	0.0079	0.0106	0.0106	0.0106	-1.0	0.0196	0.007	-1.0	-1.0
0.4789	16.0	160	0.4662	0.2283	0.2665	0.2665	-1.0	0.1539	0.2598	0.0794	0.2438	0.2438	-1.0	0.1543	0.2798	-1.0	-1.0
0.5485	17.0	170	0.4586	0.2041	0.2369	0.2369	-1.0	0.1679	0.2202	0.0731	0.2175	0.2175	-1.0	0.1717	0.236	-1.0	-1.0
0.3419	18.0	180	0.4637	0.3674	0.4292	0.4292	-1.0	0.2666	0.4049	0.0856	0.3994	0.3994	-1.0	0.2696	0.4518	-1.0	-1.0
0.4885	19.0	190	0.5509	0.4468	0.5407	0.5314	-1.0	0.354	0.4898	0.0925	0.5156	0.5156	-1.0	0.3761	0.5719	-1.0	-1.0
0.3336	20.0	200	0.5122	0.1809	0.2149	0.2149	-1.0	0.0802	0.2257	0.0763	0.195	0.195	-1.0	0.0783	0.2421	-1.0	-1.0
0.3471	21.0	210	0.4619	0.4291	0.519	0.519	-1.0	0.3115	0.4787	0.0906	0.465	0.4706	-1.0	0.3174	0.5325	-1.0	-1.0
0.3953	22.0	220	0.4313	0.4938	0.5927	0.581	-1.0	0.3728	0.5476	0.0887	0.5319	0.5437	-1.0	0.3891	0.6061	-1.0	-1.0
0.5373	23.0	230	0.4308	0.5264	0.6339	0.623	-1.0	0.4223	0.5733	0.0938	0.5744	0.5894	-1.0	0.4391	0.65	-1.0	-1.0
0.3092	24.0	240	0.4193	0.5561	0.6661	0.6571	-1.0	0.4775	0.5927	0.0944	0.5844	0.6169	-1.0	0.5022	0.6632	-1.0	-1.0
0.477	25.0	250	0.4125	0.41	0.4769	0.4769	-1.0	0.3283	0.4467	0.1006	0.4387	0.4487	-1.0	0.3348	0.4947	-1.0	-1.0
0.3867	26.0	260	0.4114	0.6146	0.743	0.7168	-1.0	0.6036	0.6294	0.0938	0.6381	0.6981	-1.0	0.6304	0.7254	-1.0	-1.0
0.3658	27.0	270	0.4001	0.6643	0.7856	0.7727	-1.0	0.6505	0.6817	0.1	0.6787	0.7456	-1.0	0.6935	0.7667	-1.0	-1.0
0.3053	28.0	280	0.4282	0.5218	0.6326	0.6201	-1.0	0.4163	0.5676	0.095	0.5631	0.5844	-1.0	0.4261	0.6482	-1.0	-1.0
0.3105	29.0	290	0.4398	0.6187	0.7567	0.7331	-1.0	0.6045	0.6336	0.0919	0.6413	0.6944	-1.0	0.6478	0.7132	-1.0	-1.0
0.3713	30.0	300	0.4202	0.5643	0.6683	0.6673	-1.0	0.4959	0.5996	0.0931	0.5969	0.6306	-1.0	0.5348	0.6693	-1.0	-1.0
0.2874	31.0	310	0.4018	0.6327	0.7547	0.7357	-1.0	0.5647	0.6713	0.0981	0.6575	0.715	-1.0	0.5957	0.7632	-1.0	-1.0
0.2809	32.0	320	0.4007	0.6444	0.7618	0.7412	-1.0	0.6189	0.667	0.1013	0.6694	0.7381	-1.0	0.6587	0.7702	-1.0	-1.0
0.2801	33.0	330	0.4528	0.6125	0.7386	0.727	-1.0	0.5399	0.6519	0.0938	0.6338	0.6994	-1.0	0.5739	0.75	-1.0	-1.0
0.3484	34.0	340	0.4233	0.6226	0.7505	0.7381	-1.0	0.6268	0.642	0.0988	0.64	0.7194	-1.0	0.6543	0.7456	-1.0	-1.0
0.3591	35.0	350	0.4156	0.6475	0.759	0.7576	-1.0	0.6733	0.6536	0.095	0.6712	0.74	-1.0	0.7065	0.7535	-1.0	-1.0
0.2204	36.0	360	0.4363	0.5723	0.6577	0.6577	-1.0	0.548	0.5962	0.0956	0.6112	0.6456	-1.0	0.5717	0.6754	-1.0	-1.0
0.3912	37.0	370	0.4261	0.6855	0.8128	0.8032	-1.0	0.6777	0.7012	0.1006	0.7063	0.7806	-1.0	0.7217	0.8044	-1.0	-1.0
0.3377	38.0	380	0.4260	0.6743	0.7969	0.7847	-1.0	0.6147	0.7086	0.1025	0.69	0.7444	-1.0	0.6413	0.786	-1.0	-1.0
0.3047	39.0	390	0.4211	0.6519	0.7793	0.7647	-1.0	0.6241	0.6742	0.1006	0.6681	0.7175	-1.0	0.6457	0.7465	-1.0	-1.0
0.2563	40.0	400	0.4313	0.6441	0.769	0.769	-1.0	0.724	0.6211	0.1006	0.6669	0.7275	-1.0	0.7543	0.7167	-1.0	-1.0
0.3127	41.0	410	0.4297	0.5739	0.6679	0.6593	-1.0	0.5456	0.5969	0.0975	0.5987	0.6438	-1.0	0.563	0.6763	-1.0	-1.0
0.2782	42.0	420	0.4133	0.6234	0.7267	0.7179	-1.0	0.6279	0.6346	0.0962	0.6394	0.7019	-1.0	0.6457	0.7246	-1.0	-1.0
0.2965	43.0	430	0.4304	0.5708	0.684	0.664	-1.0	0.5152	0.6021	0.0938	0.6225	0.6538	-1.0	0.5326	0.7026	-1.0	-1.0
0.2599	44.0	440	0.4240	0.6451	0.7575	0.7445	-1.0	0.67	0.6528	0.0975	0.6762	0.7506	-1.0	0.6978	0.7719	-1.0	-1.0
0.2821	45.0	450	0.4361	0.695	0.8218	0.796	-1.0	0.7193	0.6945	0.0969	0.7287	0.79	-1.0	0.7587	0.8026	-1.0	-1.0
0.3117	46.0	460	0.4164	0.7032	0.8334	0.8051	-1.0	0.7114	0.7094	0.1019	0.7331	0.7925	-1.0	0.7391	0.814	-1.0	-1.0
0.2484	47.0	470	0.4007	0.6757	0.7805	0.7805	-1.0	0.7268	0.6714	0.1037	0.7088	0.7656	-1.0	0.7543	0.7702	-1.0	-1.0
0.3059	48.0	480	0.4269	0.651	0.7539	0.7496	-1.0	0.599	0.6866	0.0956	0.6819	0.7294	-1.0	0.6174	0.7746	-1.0	-1.0
0.2492	49.0	490	0.3877	0.6452	0.7506	0.7277	-1.0	0.6343	0.6624	0.09	0.6794	0.7244	-1.0	0.6565	0.7518	-1.0	-1.0
0.3828	50.0	500	0.4237	0.6721	0.7953	0.7744	-1.0	0.7174	0.6672	0.0887	0.7056	0.7606	-1.0	0.75	0.7649	-1.0	-1.0
0.2737	51.0	510	0.3713	0.6619	0.7594	0.7453	-1.0	0.6988	0.6615	0.0925	0.6919	0.7419	-1.0	0.7283	0.7474	-1.0	-1.0
0.3283	52.0	520	0.3737	0.6298	0.7286	0.7254	-1.0	0.6199	0.6431	0.0894	0.6575	0.7019	-1.0	0.6478	0.7237	-1.0	-1.0
0.2819	53.0	530	0.4077	0.6919	0.8094	0.8004	-1.0	0.7656	0.6797	0.0894	0.7106	0.7831	-1.0	0.7978	0.7772	-1.0	-1.0
0.2533	54.0	540	0.4056	0.7032	0.8249	0.8045	-1.0	0.724	0.7105	0.0962	0.7381	0.7962	-1.0	0.7522	0.814	-1.0	-1.0
0.3408	55.0	550	0.3916	0.667	0.771	0.7591	-1.0	0.6257	0.6997	0.0969	0.6913	0.7356	-1.0	0.65	0.7702	-1.0	-1.0
0.2069	56.0	560	0.3931	0.7054	0.8197	0.7998	-1.0	0.7399	0.7061	0.0962	0.7188	0.785	-1.0	0.7652	0.793	-1.0	-1.0
0.2572	57.0	570	0.4012	0.6993	0.8117	0.7932	-1.0	0.729	0.699	0.1025	0.7275	0.7862	-1.0	0.7609	0.7965	-1.0	-1.0
0.1786	58.0	580	0.3830	0.7114	0.8231	0.8047	-1.0	0.7666	0.7065	0.0975	0.7325	0.8056	-1.0	0.7935	0.8105	-1.0	-1.0
0.2185	59.0	590	0.3609	0.705	0.8153	0.806	-1.0	0.7388	0.6997	0.0919	0.7194	0.7912	-1.0	0.7783	0.7965	-1.0	-1.0
0.2219	60.0	600	0.3783	0.7086	0.8241	0.8092	-1.0	0.7106	0.7192	0.0969	0.74	0.805	-1.0	0.7391	0.8316	-1.0	-1.0
0.3003	61.0	610	0.4098	0.7118	0.8178	0.8085	-1.0	0.7534	0.7084	0.0919	0.7394	0.8138	-1.0	0.7891	0.8237	-1.0	-1.0
0.3144	62.0	620	0.4339	0.6867	0.7967	0.7794	-1.0	0.7074	0.6913	0.0894	0.7294	0.7875	-1.0	0.7435	0.8053	-1.0	-1.0
0.2323	63.0	630	0.4086	0.6733	0.7901	0.7787	-1.0	0.692	0.6792	0.0944	0.715	0.7719	-1.0	0.7239	0.7912	-1.0	-1.0
0.3114	64.0	640	0.3946	0.6801	0.7905	0.7658	-1.0	0.6803	0.6931	0.0981	0.7188	0.7713	-1.0	0.7043	0.7982	-1.0	-1.0
0.2579	65.0	650	0.3899	0.6658	0.7882	0.7637	-1.0	0.6463	0.6861	0.0988	0.7013	0.7613	-1.0	0.6739	0.7965	-1.0	-1.0
0.2312	66.0	660	0.3816	0.6567	0.7604	0.7548	-1.0	0.6226	0.6793	0.0969	0.6919	0.7506	-1.0	0.6478	0.7921	-1.0	-1.0
0.209	67.0	670	0.3967	0.699	0.8281	0.7953	-1.0	0.7061	0.7074	0.0906	0.7394	0.7994	-1.0	0.7348	0.8254	-1.0	-1.0
0.3113	68.0	680	0.4126	0.6991	0.8308	0.7838	-1.0	0.7222	0.6999	0.0962	0.7337	0.7994	-1.0	0.7522	0.8184	-1.0	-1.0
0.2045	69.0	690	0.4276	0.703	0.8227	0.7956	-1.0	0.7344	0.7016	0.0838	0.735	0.8006	-1.0	0.7652	0.8149	-1.0	-1.0
0.2002	70.0	700	0.4094	0.6843	0.7886	0.7836	-1.0	0.6975	0.6908	0.0919	0.7175	0.7788	-1.0	0.7239	0.8009	-1.0	-1.0
0.2065	71.0	710	0.4052	0.7065	0.8196	0.8056	-1.0	0.7149	0.7146	0.0981	0.7337	0.8	-1.0	0.7457	0.8219	-1.0	-1.0
0.2716	72.0	720	0.4000	0.7039	0.8294	0.8059	-1.0	0.7229	0.7079	0.0956	0.7344	0.7987	-1.0	0.7522	0.8175	-1.0	-1.0
0.2935	73.0	730	0.3905	0.652	0.7532	0.7475	-1.0	0.6084	0.6786	0.0906	0.7038	0.7525	-1.0	0.6326	0.8009	-1.0	-1.0
0.2137	74.0	740	0.3959	0.627	0.7307	0.725	-1.0	0.6212	0.6476	0.0906	0.6794	0.7312	-1.0	0.6522	0.7632	-1.0	-1.0
0.2075	75.0	750	0.3786	0.6542	0.769	0.7535	-1.0	0.659	0.6667	0.0906	0.71	0.7619	-1.0	0.6913	0.7904	-1.0	-1.0
0.1713	76.0	760	0.3836	0.6695	0.7851	0.7703	-1.0	0.6683	0.6859	0.0906	0.7138	0.7781	-1.0	0.7065	0.807	-1.0	-1.0
0.2233	77.0	770	0.3947	0.6659	0.775	0.7694	-1.0	0.714	0.6616	0.0913	0.705	0.7763	-1.0	0.7543	0.7851	-1.0	-1.0
0.2398	78.0	780	0.3835	0.6854	0.7997	0.7883	-1.0	0.7067	0.6903	0.0906	0.7212	0.785	-1.0	0.7391	0.8035	-1.0	-1.0
0.1906	79.0	790	0.3811	0.6901	0.8028	0.7974	-1.0	0.7128	0.6948	0.0956	0.7231	0.7931	-1.0	0.7478	0.8114	-1.0	-1.0
0.1823	80.0	800	0.3831	0.6721	0.7821	0.7709	-1.0	0.7005	0.6764	0.0906	0.7094	0.775	-1.0	0.737	0.7904	-1.0	-1.0
0.2266	81.0	810	0.3831	0.6973	0.8131	0.8007	-1.0	0.6966	0.7103	0.0969	0.7319	0.7969	-1.0	0.7326	0.8228	-1.0	-1.0
0.1812	82.0	820	0.3809	0.6934	0.8057	0.7933	-1.0	0.6984	0.707	0.0962	0.7262	0.7919	-1.0	0.7348	0.8149	-1.0	-1.0
0.1811	83.0	830	0.3820	0.6836	0.7953	0.7829	-1.0	0.6797	0.7021	0.0969	0.7156	0.7819	-1.0	0.7087	0.8114	-1.0	-1.0
0.1677	84.0	840	0.3851	0.6809	0.7891	0.7759	-1.0	0.6854	0.6942	0.0962	0.7163	0.7763	-1.0	0.7109	0.8026	-1.0	-1.0
0.159	85.0	850	0.3791	0.6802	0.7889	0.7765	-1.0	0.6799	0.6954	0.0962	0.715	0.775	-1.0	0.7087	0.8018	-1.0	-1.0
0.1646	86.0	860	0.3712	0.6856	0.7903	0.7785	-1.0	0.6683	0.7073	0.0975	0.7212	0.7788	-1.0	0.6978	0.8114	-1.0	-1.0
0.1618	87.0	870	0.3736	0.6817	0.7861	0.7753	-1.0	0.7037	0.6883	0.0969	0.72	0.7781	-1.0	0.7326	0.7965	-1.0	-1.0
0.144	88.0	880	0.3724	0.6804	0.7917	0.7688	-1.0	0.7067	0.6832	0.0962	0.7138	0.7719	-1.0	0.7304	0.7886	-1.0	-1.0
0.2508	89.0	890	0.3643	0.6792	0.7823	0.7692	-1.0	0.7062	0.6828	0.0969	0.7125	0.7706	-1.0	0.7283	0.7877	-1.0	-1.0
0.1579	90.0	900	0.3623	0.6996	0.8094	0.7962	-1.0	0.7204	0.7044	0.0962	0.7256	0.7937	-1.0	0.7522	0.8105	-1.0	-1.0
0.1625	91.0	910	0.3630	0.6985	0.8083	0.7959	-1.0	0.718	0.7034	0.0962	0.7256	0.7937	-1.0	0.7522	0.8105	-1.0	-1.0
0.1734	92.0	920	0.3626	0.6876	0.7894	0.787	-1.0	0.7165	0.6891	0.0969	0.7169	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1452	93.0	930	0.3619	0.6838	0.7887	0.7781	-1.0	0.7163	0.684	0.0969	0.7119	0.7769	-1.0	0.7522	0.7868	-1.0	-1.0
0.1764	94.0	940	0.3633	0.6833	0.7888	0.7782	-1.0	0.7162	0.6831	0.0969	0.7113	0.7763	-1.0	0.7522	0.786	-1.0	-1.0
0.1862	95.0	950	0.3633	0.6825	0.7889	0.7781	-1.0	0.7153	0.6826	0.0969	0.71	0.7756	-1.0	0.75	0.786	-1.0	-1.0
0.1855	96.0	960	0.3622	0.6878	0.7891	0.7873	-1.0	0.7147	0.6899	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.2982	97.0	970	0.3622	0.6877	0.789	0.7873	-1.0	0.7147	0.6896	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1764	98.0	980	0.3620	0.6877	0.789	0.7873	-1.0	0.7147	0.6896	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.2245	99.0	990	0.3618	0.6874	0.789	0.7871	-1.0	0.7147	0.6892	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0
0.1775	100.0	1000	0.3617	0.6874	0.789	0.7871	-1.0	0.7147	0.6892	0.0969	0.7163	0.7819	-1.0	0.75	0.7947	-1.0	-1.0

Framework versions

Transformers 4.41.1

Pytorch 2.2.1+cu121

Datasets 2.19.1

Tokenizers 0.19.1

LLyq/ms_detr_finetuned_diana

ms_detr_finetuned_diana

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ms_detr_finetuned_diana

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions