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

Loss: 0.5911
Map: 0.5587
Map 50: 0.8202
Map 75: 0.6128
Map Small: 0.2752
Map Medium: 0.5141
Map Large: 0.7114
Mar 1: 0.4061
Mar 10: 0.644
Mar 100: 0.7208
Mar Small: 0.4679
Mar Medium: 0.6882
Mar Large: 0.8062
Map Platelets: 0.3331
Mar 100 Platelets: 0.5556
Map Rbc: 0.5785
Mar 100 Rbc: 0.7543
Map Wbc: 0.7646
Mar 100 Wbc: 0.8525

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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 Platelets	Mar 100 Platelets	Map Rbc	Mar 100 Rbc	Map Wbc	Mar 100 Wbc
No log	1.0	26	1.0745	0.0791	0.1484	0.076	0.0	0.0782	0.1361	0.014	0.0973	0.2035	0.0	0.1939	0.3557	0.0	0.0	0.2372	0.6104	0.0	0.0
No log	2.0	52	0.9278	0.1125	0.1831	0.1244	0.0	0.1171	0.1836	0.0194	0.1209	0.2319	0.0	0.2248	0.3856	0.0	0.0	0.3376	0.6958	0.0	0.0
No log	3.0	78	0.9425	0.1198	0.2245	0.1231	0.0039	0.1425	0.1743	0.032	0.1437	0.2465	0.0321	0.2464	0.3827	0.0199	0.0778	0.3394	0.6618	0.0	0.0
No log	4.0	104	0.8965	0.1344	0.245	0.1438	0.0109	0.1677	0.2048	0.0305	0.1928	0.2909	0.2179	0.2829	0.3606	0.0214	0.2028	0.3818	0.67	0.0	0.0
No log	5.0	130	0.8165	0.1802	0.3282	0.1851	0.0431	0.2059	0.2791	0.0791	0.2832	0.3782	0.3357	0.3734	0.3918	0.0778	0.3778	0.4536	0.7232	0.0093	0.0338
No log	6.0	156	0.8918	0.1743	0.3513	0.15	0.0796	0.1922	0.2253	0.0987	0.3532	0.4564	0.3286	0.3935	0.4931	0.105	0.45	0.4008	0.6716	0.0173	0.2475
No log	7.0	182	0.7744	0.2888	0.4691	0.3181	0.0538	0.2335	0.4002	0.2864	0.554	0.6618	0.4714	0.4136	0.7749	0.103	0.5222	0.451	0.7081	0.3123	0.755
No log	8.0	208	0.7615	0.3452	0.5449	0.3663	0.1073	0.2042	0.4669	0.3263	0.5709	0.6729	0.3286	0.6103	0.8227	0.1204	0.4625	0.4565	0.7075	0.4586	0.8487
No log	9.0	234	0.7013	0.4743	0.7399	0.5305	0.1527	0.4342	0.6532	0.3489	0.6016	0.6871	0.3821	0.6618	0.7912	0.2204	0.4917	0.506	0.7271	0.6966	0.8425
No log	10.0	260	0.6729	0.4978	0.7687	0.5505	0.1813	0.464	0.6733	0.3692	0.6146	0.7015	0.4214	0.68	0.774	0.2453	0.5292	0.5233	0.734	0.725	0.8413
No log	11.0	286	0.6773	0.4691	0.7251	0.5163	0.1795	0.2625	0.6377	0.3542	0.6153	0.6964	0.4714	0.6676	0.7592	0.2314	0.5347	0.5234	0.717	0.6525	0.8375
No log	12.0	312	0.6581	0.5059	0.7516	0.5704	0.1709	0.4819	0.6766	0.3816	0.6253	0.7074	0.4643	0.6775	0.7854	0.251	0.5458	0.5416	0.7326	0.725	0.8438
No log	13.0	338	0.6483	0.514	0.7879	0.5714	0.1836	0.4694	0.688	0.3821	0.6225	0.7026	0.4964	0.6524	0.7954	0.2628	0.5125	0.5389	0.7327	0.7404	0.8625
No log	14.0	364	0.6440	0.5223	0.7986	0.5865	0.2324	0.4881	0.6661	0.387	0.626	0.7069	0.5429	0.6719	0.7583	0.3157	0.5667	0.5396	0.7229	0.7114	0.8313
No log	15.0	390	0.6845	0.5126	0.7979	0.5487	0.2364	0.4547	0.6495	0.3829	0.6194	0.6939	0.5643	0.6301	0.7302	0.3246	0.5722	0.5149	0.6984	0.6984	0.8112
No log	16.0	416	0.6464	0.5213	0.8164	0.5725	0.2683	0.4662	0.6724	0.3855	0.6211	0.7027	0.4643	0.6797	0.7676	0.3011	0.5486	0.5368	0.7295	0.726	0.83
No log	17.0	442	0.6218	0.5267	0.8101	0.5803	0.2126	0.5258	0.6783	0.3862	0.6234	0.7034	0.4964	0.6768	0.7769	0.3032	0.55	0.5597	0.7441	0.7173	0.8163
No log	18.0	468	0.6168	0.5384	0.8041	0.5897	0.2356	0.5064	0.6929	0.4012	0.6352	0.7101	0.5143	0.6716	0.7907	0.3075	0.5472	0.558	0.7456	0.7496	0.8375
No log	19.0	494	0.6173	0.5408	0.8057	0.5921	0.2388	0.5413	0.6875	0.3997	0.635	0.709	0.4536	0.7108	0.8094	0.3092	0.5444	0.561	0.7402	0.7522	0.8425
0.9152	20.0	520	0.6054	0.541	0.8049	0.5917	0.2594	0.505	0.7075	0.3964	0.6354	0.7166	0.4821	0.6822	0.8011	0.2986	0.5569	0.5609	0.7442	0.7636	0.8487
0.9152	21.0	546	0.5996	0.547	0.8152	0.5945	0.2915	0.5	0.7014	0.4018	0.6383	0.7138	0.4857	0.6833	0.7896	0.3255	0.5569	0.5676	0.7456	0.7479	0.8388
0.9152	22.0	572	0.6045	0.5503	0.828	0.5925	0.2747	0.5212	0.7048	0.3997	0.6391	0.7141	0.4429	0.6881	0.803	0.3225	0.5528	0.5714	0.7457	0.757	0.8438
0.9152	23.0	598	0.6001	0.5523	0.8268	0.6055	0.2581	0.5133	0.7072	0.4009	0.6432	0.7149	0.4893	0.6839	0.7892	0.3332	0.5583	0.5729	0.7465	0.7508	0.84
0.9152	24.0	624	0.6008	0.5545	0.8279	0.6021	0.2653	0.5119	0.7072	0.4051	0.6453	0.7221	0.4786	0.6921	0.7969	0.3337	0.5681	0.5727	0.7496	0.757	0.8487
0.9152	25.0	650	0.5943	0.5568	0.8286	0.6102	0.2926	0.511	0.7093	0.4082	0.6479	0.7241	0.4857	0.6915	0.8	0.3384	0.5694	0.5768	0.7503	0.7551	0.8525
0.9152	26.0	676	0.5919	0.5562	0.8211	0.6161	0.273	0.5281	0.7125	0.4062	0.6453	0.7213	0.4786	0.6849	0.8065	0.3267	0.5556	0.5778	0.7509	0.764	0.8575
0.9152	27.0	702	0.5939	0.5555	0.8187	0.6006	0.275	0.5102	0.7088	0.4047	0.643	0.7197	0.4786	0.6843	0.8053	0.3282	0.5556	0.5745	0.7498	0.7636	0.8537
0.9152	28.0	728	0.5925	0.5556	0.8192	0.6068	0.2763	0.5107	0.71	0.4044	0.6414	0.7166	0.4714	0.6816	0.8035	0.329	0.5472	0.5766	0.7501	0.7613	0.8525
0.9152	29.0	754	0.5916	0.5597	0.8205	0.612	0.2759	0.514	0.713	0.4065	0.6442	0.7214	0.4679	0.6884	0.8068	0.3332	0.5556	0.5788	0.7548	0.767	0.8537
0.9152	30.0	780	0.5911	0.5587	0.8202	0.6128	0.2752	0.5141	0.7114	0.4061	0.644	0.7208	0.4679	0.6882	0.8062	0.3331	0.5556	0.5785	0.7543	0.7646	0.8525

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

Larbutsri/detr_finetuned_bccd

detr_finetuned_bccd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Larbutsri/
detr_finetuned_bccd