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: 0.8865
Map: 0.32
Map 50: 0.778
Map 75: 0.1885
Map Small: 0.3219
Map Medium: 0.0139
Map Large: -1.0
Mar 1: 0.0252
Mar 10: 0.1994
Mar 100: 0.487
Mar Small: 0.4896
Mar Medium: 0.0122
Mar Large: -1.0
Map Falciparum Trophozoite: 0.32
Mar 100 Falciparum Trophozoite: 0.487
Map Wbc: -1.0
Mar 100 Wbc: -1.0

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: 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	209	1.2045	0.1719	0.4768	0.0896	0.1728	0.0167	-1.0	0.0188	0.139	0.4221	0.4242	0.0184	-1.0	0.1719	0.4221	-1.0	-1.0
No log	2.0	418	1.1177	0.2155	0.5922	0.1044	0.2165	0.005	-1.0	0.021	0.1601	0.4249	0.4272	0.0041	-1.0	0.2155	0.4249	-1.0	-1.0
1.339	3.0	627	1.0571	0.249	0.6576	0.1328	0.2503	0.0229	-1.0	0.0236	0.1739	0.44	0.4422	0.0224	-1.0	0.249	0.44	-1.0	-1.0
1.339	4.0	836	1.0675	0.2358	0.6473	0.1175	0.2369	0.0139	-1.0	0.0216	0.166	0.4242	0.4265	0.0122	-1.0	0.2358	0.4242	-1.0	-1.0
1.0423	5.0	1045	1.0266	0.2509	0.6759	0.1224	0.2525	0.0	-1.0	0.0228	0.1711	0.4318	0.4341	0.0	-1.0	0.2509	0.4318	-1.0	-1.0
1.0423	6.0	1254	1.0054	0.2569	0.6847	0.1269	0.2579	0.0091	-1.0	0.0226	0.1754	0.4467	0.4491	0.0122	-1.0	0.2569	0.4467	-1.0	-1.0
1.0423	7.0	1463	0.9812	0.2767	0.7163	0.1454	0.2782	0.0114	-1.0	0.0238	0.1831	0.4539	0.4562	0.0224	-1.0	0.2767	0.4539	-1.0	-1.0
0.99	8.0	1672	1.0019	0.271	0.7169	0.1358	0.2724	0.0096	-1.0	0.0236	0.1801	0.4515	0.4538	0.0143	-1.0	0.271	0.4515	-1.0	-1.0
0.99	9.0	1881	0.9623	0.2873	0.731	0.1597	0.2886	0.0064	-1.0	0.0251	0.1865	0.4608	0.4633	0.0061	-1.0	0.2873	0.4608	-1.0	-1.0
0.9521	10.0	2090	0.9763	0.273	0.711	0.1419	0.2742	0.011	-1.0	0.0229	0.178	0.4482	0.4506	0.0122	-1.0	0.273	0.4482	-1.0	-1.0
0.9521	11.0	2299	0.9551	0.2906	0.7354	0.1634	0.2925	0.0064	-1.0	0.0248	0.188	0.4654	0.4679	0.0082	-1.0	0.2906	0.4654	-1.0	-1.0
0.92	12.0	2508	0.9430	0.2956	0.7454	0.1685	0.297	0.0052	-1.0	0.0248	0.1886	0.4696	0.4721	0.0061	-1.0	0.2956	0.4696	-1.0	-1.0
0.92	13.0	2717	0.9434	0.2953	0.7445	0.1721	0.2968	0.0233	-1.0	0.0245	0.1895	0.4673	0.4697	0.0265	-1.0	0.2953	0.4673	-1.0	-1.0
0.92	14.0	2926	0.9228	0.3001	0.7498	0.1716	0.3015	0.0132	-1.0	0.0244	0.1899	0.4767	0.4792	0.0143	-1.0	0.3001	0.4767	-1.0	-1.0
0.8986	15.0	3135	0.9194	0.3036	0.7566	0.1757	0.3055	0.0119	-1.0	0.0243	0.1929	0.4778	0.4803	0.0143	-1.0	0.3036	0.4778	-1.0	-1.0
0.8986	16.0	3344	0.9166	0.3063	0.7558	0.1791	0.3081	0.0129	-1.0	0.0253	0.1928	0.4762	0.4787	0.0122	-1.0	0.3063	0.4762	-1.0	-1.0
0.875	17.0	3553	0.9218	0.3021	0.7573	0.1653	0.3041	0.0089	-1.0	0.0251	0.1908	0.4721	0.4746	0.0082	-1.0	0.3021	0.4721	-1.0	-1.0
0.875	18.0	3762	0.9094	0.3032	0.7548	0.1705	0.3052	0.0079	-1.0	0.0242	0.1921	0.4769	0.4795	0.0061	-1.0	0.3032	0.4769	-1.0	-1.0
0.875	19.0	3971	0.8965	0.3156	0.7713	0.1873	0.3171	0.0187	-1.0	0.0247	0.1963	0.484	0.4865	0.0184	-1.0	0.3156	0.484	-1.0	-1.0
0.8575	20.0	4180	0.8995	0.3101	0.7674	0.1854	0.3116	0.0069	-1.0	0.0247	0.1964	0.4803	0.4829	0.0061	-1.0	0.3101	0.4803	-1.0	-1.0
0.8575	21.0	4389	0.8992	0.3118	0.7676	0.1852	0.3131	0.0119	-1.0	0.0252	0.1954	0.4794	0.4819	0.0102	-1.0	0.3118	0.4794	-1.0	-1.0
0.834	22.0	4598	0.8912	0.3169	0.7744	0.1894	0.3186	0.0089	-1.0	0.0254	0.1977	0.4876	0.4902	0.0082	-1.0	0.3169	0.4876	-1.0	-1.0
0.834	23.0	4807	0.8922	0.3175	0.7761	0.1895	0.3195	0.0083	-1.0	0.0255	0.1984	0.4881	0.4907	0.0102	-1.0	0.3175	0.4881	-1.0	-1.0
0.8217	24.0	5016	0.8946	0.3153	0.7735	0.1809	0.3167	0.0119	-1.0	0.0249	0.1972	0.4819	0.4844	0.0102	-1.0	0.3153	0.4819	-1.0	-1.0
0.8217	25.0	5225	0.8891	0.3198	0.7801	0.1877	0.3213	0.0089	-1.0	0.025	0.1981	0.4878	0.4903	0.0082	-1.0	0.3198	0.4878	-1.0	-1.0
0.8217	26.0	5434	0.8867	0.3206	0.7794	0.1894	0.3223	0.0139	-1.0	0.0254	0.1987	0.4875	0.4901	0.0122	-1.0	0.3206	0.4875	-1.0	-1.0
0.8153	27.0	5643	0.8859	0.3207	0.7787	0.1897	0.3224	0.0139	-1.0	0.0255	0.1991	0.4879	0.4905	0.0122	-1.0	0.3207	0.4879	-1.0	-1.0
0.8153	28.0	5852	0.8862	0.3203	0.7785	0.1882	0.3222	0.0139	-1.0	0.0255	0.1994	0.4867	0.4892	0.0122	-1.0	0.3203	0.4867	-1.0	-1.0
0.8022	29.0	6061	0.8864	0.32	0.7776	0.1892	0.3219	0.0139	-1.0	0.0253	0.1994	0.4871	0.4897	0.0122	-1.0	0.32	0.4871	-1.0	-1.0
0.8022	30.0	6270	0.8865	0.32	0.778	0.1885	0.3219	0.0139	-1.0	0.0252	0.1994	0.487	0.4896	0.0122	-1.0	0.32	0.487	-1.0	-1.0

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

LynnKukunda/detr_finetunned_air

detr_finetunned_air

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

LynnKukunda/
detr_finetunned_air