detr_finetuned_oculardataset

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

Loss: 1.0672
Map: 0.3032
Map 50: 0.4973
Map 75: 0.3701
Map Small: 0.2981
Map Medium: 0.6746
Map Large: -1.0
Mar 1: 0.1
Mar 10: 0.3678
Mar 100: 0.4114
Mar Small: 0.4054
Mar Medium: 0.7421
Mar Large: -1.0
Map Falciparum Trophozoite: 0.0156
Mar 100 Falciparum Trophozoite: 0.1511
Map Wbc: 0.5908
Mar 100 Wbc: 0.6716

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: 30

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 Falciparum Trophozoite	Mar 100 Falciparum Trophozoite	Map Wbc	Mar 100 Wbc
No log	1.0	86	1.6645	0.131	0.2562	0.1153	0.1289	0.3974	-1.0	0.0647	0.2312	0.3164	0.314	0.6159	-1.0	0.0004	0.0456	0.2616	0.5873
No log	2.0	172	1.4800	0.2028	0.4079	0.1766	0.1993	0.4876	-1.0	0.0677	0.2725	0.3282	0.3251	0.628	-1.0	0.0007	0.0648	0.405	0.5915
No log	3.0	258	1.3829	0.2264	0.4496	0.1936	0.2193	0.5542	-1.0	0.0729	0.2807	0.3215	0.3168	0.629	-1.0	0.0019	0.0706	0.451	0.5725
No log	4.0	344	1.3318	0.2089	0.4403	0.1427	0.2056	0.4726	-1.0	0.0691	0.2751	0.3221	0.3116	0.6748	-1.0	0.002	0.0941	0.4158	0.5502
No log	5.0	430	1.2739	0.2454	0.4562	0.2342	0.2354	0.614	-1.0	0.0777	0.3046	0.3482	0.338	0.7262	-1.0	0.002	0.0906	0.4888	0.6058
1.7665	6.0	516	1.2365	0.2599	0.4744	0.2599	0.2522	0.6258	-1.0	0.0846	0.3217	0.361	0.354	0.7	-1.0	0.005	0.1047	0.5149	0.6173
1.7665	7.0	602	1.2548	0.2488	0.4689	0.2302	0.2434	0.5622	-1.0	0.0788	0.31	0.3519	0.3446	0.6888	-1.0	0.0038	0.1012	0.4938	0.6026
1.7665	8.0	688	1.2031	0.2715	0.474	0.3074	0.2664	0.6153	-1.0	0.0897	0.3309	0.3744	0.3723	0.657	-1.0	0.0058	0.1164	0.5373	0.6325
1.7665	9.0	774	1.2492	0.2417	0.4715	0.2154	0.2349	0.5753	-1.0	0.0789	0.3064	0.3503	0.342	0.686	-1.0	0.0043	0.1129	0.4791	0.5877
1.7665	10.0	860	1.1861	0.2752	0.4772	0.2891	0.2683	0.6259	-1.0	0.0872	0.3342	0.3823	0.379	0.6813	-1.0	0.0061	0.1217	0.5443	0.6429
1.7665	11.0	946	1.1996	0.2607	0.4605	0.2779	0.2565	0.5972	-1.0	0.085	0.326	0.3722	0.3669	0.6813	-1.0	0.0041	0.1254	0.5173	0.6189
1.2663	12.0	1032	1.1664	0.2764	0.4753	0.3137	0.2718	0.6148	-1.0	0.0892	0.333	0.3781	0.3741	0.685	-1.0	0.0054	0.1188	0.5473	0.6375
1.2663	13.0	1118	1.1451	0.2804	0.4694	0.3212	0.2732	0.6595	-1.0	0.092	0.3412	0.3852	0.3787	0.7187	-1.0	0.0051	0.1282	0.5557	0.6421
1.2663	14.0	1204	1.1251	0.2889	0.4761	0.3401	0.2835	0.6619	-1.0	0.0926	0.3496	0.3979	0.393	0.714	-1.0	0.0091	0.1391	0.5687	0.6567
1.2663	15.0	1290	1.1493	0.2778	0.4695	0.3126	0.2706	0.6531	-1.0	0.0911	0.3415	0.3881	0.3792	0.743	-1.0	0.0054	0.1382	0.5502	0.6379
1.2663	16.0	1376	1.1125	0.2846	0.4799	0.3307	0.2804	0.6415	-1.0	0.0926	0.3498	0.4005	0.3954	0.7159	-1.0	0.0075	0.1452	0.5617	0.6558
1.2663	17.0	1462	1.1002	0.2909	0.4816	0.3471	0.2859	0.6545	-1.0	0.0956	0.3554	0.4036	0.3969	0.7421	-1.0	0.0077	0.145	0.5741	0.6622
1.1448	18.0	1548	1.1066	0.2853	0.484	0.3205	0.2796	0.6647	-1.0	0.0918	0.3472	0.3944	0.3883	0.7196	-1.0	0.0092	0.1415	0.5613	0.6474
1.1448	19.0	1634	1.0993	0.2933	0.4838	0.3441	0.2884	0.6683	-1.0	0.0978	0.3581	0.401	0.3958	0.7252	-1.0	0.0079	0.1374	0.5787	0.6645
1.1448	20.0	1720	1.0850	0.298	0.4855	0.3594	0.2923	0.6669	-1.0	0.0963	0.3606	0.4011	0.3952	0.7374	-1.0	0.0093	0.1348	0.5867	0.6675
1.1448	21.0	1806	1.0814	0.3006	0.4908	0.3618	0.2951	0.6868	-1.0	0.0994	0.3628	0.4056	0.4001	0.7355	-1.0	0.0117	0.1413	0.5896	0.67
1.1448	22.0	1892	1.0836	0.2975	0.495	0.3541	0.2924	0.6712	-1.0	0.0989	0.3628	0.4084	0.4036	0.7196	-1.0	0.0135	0.1534	0.5816	0.6633
1.1448	23.0	1978	1.0813	0.2996	0.4965	0.3567	0.2941	0.6792	-1.0	0.0979	0.3625	0.408	0.402	0.7364	-1.0	0.015	0.1505	0.5842	0.6655
1.0601	24.0	2064	1.0707	0.3048	0.4952	0.3624	0.2987	0.6876	-1.0	0.0981	0.3659	0.4118	0.4054	0.7486	-1.0	0.0144	0.1501	0.5951	0.6735
1.0601	25.0	2150	1.0736	0.2982	0.4935	0.3584	0.2931	0.6732	-1.0	0.0992	0.3638	0.41	0.4053	0.7224	-1.0	0.0126	0.1521	0.5839	0.6678
1.0601	26.0	2236	1.0717	0.3034	0.4978	0.3622	0.2986	0.6788	-1.0	0.0995	0.3659	0.411	0.405	0.7421	-1.0	0.015	0.1501	0.5918	0.6719
1.0601	27.0	2322	1.0688	0.3025	0.4978	0.3622	0.2975	0.6747	-1.0	0.1	0.3674	0.4108	0.4047	0.7421	-1.0	0.0161	0.1524	0.5888	0.6693
1.0601	28.0	2408	1.0679	0.3031	0.4968	0.3638	0.2976	0.6805	-1.0	0.0999	0.3679	0.4106	0.4046	0.7421	-1.0	0.0156	0.1507	0.5905	0.6705
1.0601	29.0	2494	1.0669	0.3035	0.4976	0.3717	0.2985	0.6751	-1.0	0.0999	0.368	0.4115	0.4055	0.743	-1.0	0.0156	0.1509	0.5915	0.6721
1.0103	30.0	2580	1.0672	0.3032	0.4973	0.3701	0.2981	0.6746	-1.0	0.1	0.3678	0.4114	0.4054	0.7421	-1.0	0.0156	0.1511	0.5908	0.6716

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

marthakk/detr_finetuned_oculardataset

detr_finetuned_oculardataset

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种