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: 1.0598
Map: 0.3166
Map 50: 0.5255
Map 75: 0.3725
Map Small: 0.3115
Map Medium: 0.6744
Map Large: -1.0
Mar 1: 0.1043
Mar 10: 0.3801
Mar 100: 0.4224
Mar Small: 0.4186
Mar Medium: 0.7234
Mar Large: -1.0
Map Falciparum Trophozoite: 0.0341
Mar 100 Falciparum Trophozoite: 0.1663
Map Wbc: 0.599
Mar 100 Wbc: 0.6785

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	1.1493	0.2788	0.5002	0.3024	0.274	0.6184	-1.0	0.0918	0.3473	0.386	0.3823	0.6785	-1.0	0.0196	0.1372	0.5381	0.6348
No log	2.0	172	1.1199	0.2924	0.5063	0.3371	0.2866	0.6545	-1.0	0.0924	0.3509	0.3873	0.3805	0.729	-1.0	0.0204	0.1264	0.5644	0.6483
No log	3.0	258	1.1616	0.2802	0.4941	0.3138	0.2746	0.6231	-1.0	0.0891	0.3378	0.377	0.374	0.6598	-1.0	0.0167	0.129	0.5438	0.6249
No log	4.0	344	1.1263	0.3014	0.517	0.3393	0.296	0.6609	-1.0	0.0981	0.3588	0.3857	0.3806	0.7187	-1.0	0.0258	0.1135	0.577	0.6579
No log	5.0	430	1.1219	0.2801	0.5117	0.297	0.2734	0.6555	-1.0	0.0905	0.3458	0.3795	0.3737	0.7028	-1.0	0.0218	0.1254	0.5385	0.6337
1.0831	6.0	516	1.1299	0.2646	0.485	0.2705	0.2581	0.6103	-1.0	0.0885	0.3294	0.371	0.3636	0.7	-1.0	0.0117	0.1288	0.5175	0.6131
1.0831	7.0	602	1.1003	0.2934	0.5064	0.3254	0.286	0.6706	-1.0	0.0933	0.357	0.3962	0.3905	0.7206	-1.0	0.0207	0.1397	0.5661	0.6528
1.0831	8.0	688	1.1063	0.2945	0.5028	0.3407	0.2871	0.6606	-1.0	0.0946	0.3568	0.3975	0.3938	0.6925	-1.0	0.0243	0.1454	0.5647	0.6495
1.0831	9.0	774	1.1364	0.2824	0.4979	0.3114	0.2774	0.622	-1.0	0.0928	0.3445	0.3844	0.3818	0.671	-1.0	0.017	0.1297	0.5479	0.6392
1.0831	10.0	860	1.0997	0.2904	0.501	0.3299	0.2841	0.6483	-1.0	0.0908	0.3515	0.3917	0.387	0.7065	-1.0	0.02	0.1329	0.5609	0.6505
1.0831	11.0	946	1.1198	0.2826	0.496	0.3186	0.277	0.6299	-1.0	0.0915	0.3426	0.3822	0.3778	0.6832	-1.0	0.0225	0.1342	0.5427	0.6303
1.0585	12.0	1032	1.0999	0.2921	0.5038	0.3196	0.2867	0.6334	-1.0	0.0953	0.3556	0.3954	0.3916	0.6897	-1.0	0.0244	0.1454	0.5599	0.6453
1.0585	13.0	1118	1.1097	0.2966	0.5183	0.3365	0.29	0.6667	-1.0	0.0995	0.3549	0.3983	0.3923	0.7178	-1.0	0.0297	0.1493	0.5636	0.6472
1.0585	14.0	1204	1.0932	0.2964	0.5113	0.335	0.2913	0.6494	-1.0	0.0969	0.3556	0.396	0.391	0.7037	-1.0	0.0279	0.1474	0.565	0.6447
1.0585	15.0	1290	1.0951	0.2958	0.5173	0.3287	0.2915	0.6321	-1.0	0.0969	0.3596	0.4018	0.3962	0.7187	-1.0	0.0321	0.1505	0.5595	0.653
1.0585	16.0	1376	1.1036	0.3048	0.5215	0.3482	0.2997	0.6588	-1.0	0.102	0.3633	0.4029	0.3974	0.7234	-1.0	0.0321	0.1481	0.5775	0.6578
1.0585	17.0	1462	1.0973	0.2997	0.5169	0.3437	0.2943	0.6445	-1.0	0.1006	0.3589	0.3994	0.3963	0.6907	-1.0	0.0306	0.1448	0.5688	0.654
0.968	18.0	1548	1.1322	0.3029	0.5193	0.3482	0.2973	0.6525	-1.0	0.0983	0.3636	0.4015	0.3977	0.6991	-1.0	0.032	0.1499	0.5738	0.6532
0.968	19.0	1634	1.0698	0.3049	0.5114	0.3471	0.2989	0.6757	-1.0	0.0992	0.3665	0.4138	0.4084	0.729	-1.0	0.0288	0.1634	0.581	0.6643
0.968	20.0	1720	1.0780	0.3093	0.516	0.3556	0.3036	0.6647	-1.0	0.101	0.3694	0.4145	0.4096	0.7252	-1.0	0.0299	0.1618	0.5887	0.6673
0.968	21.0	1806	1.0825	0.3044	0.522	0.3357	0.2981	0.6642	-1.0	0.0982	0.3653	0.4071	0.4029	0.7075	-1.0	0.0319	0.1564	0.5768	0.6578
0.968	22.0	1892	1.0660	0.3142	0.5195	0.3691	0.3096	0.6679	-1.0	0.1028	0.3764	0.4195	0.4158	0.7187	-1.0	0.0352	0.164	0.5933	0.675
0.968	23.0	1978	1.0604	0.3145	0.5256	0.3633	0.3093	0.674	-1.0	0.1031	0.3774	0.4199	0.4152	0.729	-1.0	0.0368	0.1669	0.5922	0.6729
0.9092	24.0	2064	1.0607	0.3168	0.5266	0.3768	0.3114	0.6848	-1.0	0.1039	0.3785	0.4233	0.4186	0.7374	-1.0	0.034	0.1654	0.5996	0.6812
0.9092	25.0	2150	1.0681	0.3163	0.5283	0.3656	0.3113	0.6751	-1.0	0.1053	0.3769	0.4185	0.4148	0.7196	-1.0	0.0352	0.1616	0.5975	0.6755
0.9092	26.0	2236	1.0641	0.3158	0.5239	0.3708	0.3106	0.6715	-1.0	0.1045	0.378	0.4217	0.4181	0.7196	-1.0	0.0339	0.1656	0.5977	0.6777
0.9092	27.0	2322	1.0644	0.3162	0.526	0.3721	0.311	0.6785	-1.0	0.1035	0.3775	0.42	0.4164	0.7206	-1.0	0.0336	0.1624	0.5988	0.6777
0.9092	28.0	2408	1.0606	0.3165	0.5241	0.374	0.3114	0.6784	-1.0	0.1052	0.3794	0.4223	0.4184	0.7252	-1.0	0.0343	0.1665	0.5988	0.6782
0.9092	29.0	2494	1.0600	0.3161	0.5249	0.3728	0.311	0.6744	-1.0	0.1043	0.3795	0.4219	0.418	0.7234	-1.0	0.0341	0.1661	0.5981	0.6777
0.8509	30.0	2580	1.0598	0.3166	0.5255	0.3725	0.3115	0.6744	-1.0	0.1043	0.3801	0.4224	0.4186	0.7234	-1.0	0.0341	0.1663	0.599	0.6785

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

LynnKukunda/detr_finetunned_ocular

detr_finetunned_ocular

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

LynnKukunda/
detr_finetunned_ocular