rtdetr-v2-r18-chkbx

This model is a fine-tuned version of PekingU/rtdetr_v2_r18vd on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 4.4842
• Map: 0.5987
• Map 50: 0.8738
• Map 75: 0.7039
• Map Small: 0.502
• Map Medium: 0.7704
• Map Large: 0.7529
• Mar 1: 0.2319
• Mar 10: 0.5636
• Mar 100: 0.7272
• Mar Small: 0.6541
• Mar Medium: 0.8382
• Mar Large: 0.8409
• Map Checked-unchecked: -1.0
• Mar 100 Checked-unchecked: -1.0
• Map Checked: 0.5973
• Mar 100 Checked: 0.7657
• Map Unchecked: 0.6001
• Mar 100 Unchecked: 0.6886

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: 16
• 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_with_restarts
• lr_scheduler_warmup_steps: 300
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Checked	Map Checked-unchecked	Map Large	Map Medium	Map Small	Map Unchecked	Mar 1	Mar 10	Mar 100	Mar 100 Checked	Mar 100 Checked-unchecked	Mar 100 Unchecked	Mar Large	Mar Medium	Mar Small
No log	1.0	45	41.6291	0.0006	0.0021	0.0003	0.0011	-1.0	0.001	0.0007	0.0008	0.0001	0.001	0.0168	0.0694	0.1106	-1.0	0.0281	0.0384	0.1169	0.0552
No log	2.0	90	22.2922	0.0262	0.0434	0.0271	0.0286	-1.0	0.011	0.0979	0.0111	0.0237	0.0527	0.1254	0.1616	0.2049	-1.0	0.1182	0.2451	0.314	0.0793
No log	3.0	135	13.8175	0.1248	0.1961	0.1332	0.1221	-1.0	0.1424	0.236	0.1057	0.1275	0.1264	0.2977	0.4227	0.471	-1.0	0.3744	0.546	0.6483	0.3003
No log	4.0	180	8.8445	0.2482	0.3986	0.2703	0.254	-1.0	0.2724	0.3975	0.2201	0.2423	0.1658	0.4436	0.6224	0.7173	-1.0	0.5276	0.7228	0.8022	0.5239
No log	5.0	225	6.7102	0.4177	0.6659	0.4639	0.4061	-1.0	0.4542	0.6142	0.3615	0.4293	0.1899	0.4831	0.6874	0.7353	-1.0	0.6395	0.8224	0.7959	0.6124
No log	6.0	270	6.0112	0.4191	0.6537	0.4843	0.3641	-1.0	0.4843	0.6275	0.3755	0.4741	0.2018	0.5005	0.693	0.7417	-1.0	0.6443	0.8185	0.8044	0.619
No log	7.0	315	5.4113	0.4432	0.6864	0.5161	0.4496	-1.0	0.559	0.674	0.3883	0.4368	0.2113	0.5198	0.689	0.7297	-1.0	0.6483	0.8223	0.8073	0.6095
No log	8.0	360	5.0758	0.4997	0.7682	0.5818	0.4962	-1.0	0.6691	0.7045	0.4303	0.5032	0.2149	0.5279	0.7027	0.7512	-1.0	0.6543	0.8327	0.8125	0.6291
No log	9.0	405	4.9946	0.5426	0.8212	0.6339	0.562	-1.0	0.7287	0.7152	0.4604	0.5233	0.2206	0.5423	0.7181	0.7647	-1.0	0.6716	0.8556	0.8252	0.6441
No log	10.0	450	4.8400	0.5308	0.8058	0.6155	0.538	-1.0	0.7092	0.7136	0.4281	0.5237	0.2196	0.5293	0.7044	0.7611	-1.0	0.6477	0.8416	0.8136	0.6325
No log	11.0	495	4.6875	0.535	0.8049	0.6239	0.5128	-1.0	0.6637	0.7219	0.4634	0.5571	0.2244	0.5421	0.7196	0.7597	-1.0	0.6795	0.8391	0.8188	0.651
16.3849	12.0	540	4.7347	0.5365	0.8085	0.6182	0.5084	-1.0	0.7087	0.7386	0.4567	0.5646	0.2252	0.5536	0.7213	0.7682	-1.0	0.6744	0.857	0.8287	0.647
16.3849	13.0	585	4.6583	0.5669	0.8287	0.6625	0.5498	-1.0	0.7286	0.7453	0.4726	0.5839	0.2274	0.5552	0.7236	0.7597	-1.0	0.6875	0.8368	0.8269	0.6547
16.3849	14.0	630	4.5310	0.5708	0.8394	0.6632	0.5633	-1.0	0.7665	0.7367	0.4886	0.5784	0.2254	0.56	0.7291	0.7682	-1.0	0.6901	0.8649	0.8318	0.6569
16.3849	15.0	675	4.4842	0.5987	0.8738	0.7039	0.5973	-1.0	0.7529	0.7704	0.502	0.6001	0.2319	0.5636	0.7272	0.7657	-1.0	0.6886	0.8409	0.8382	0.6541
16.3849	16.0	720	4.5336	0.5882	0.8529	0.6968	0.5864	-1.0	0.7495	0.7633	0.4995	0.5899	0.2263	0.5633	0.7353	0.7795	-1.0	0.6912	0.8576	0.8395	0.6654
16.3849	17.0	765	4.5010	0.5604	0.8143	0.6583	0.5618	-1.0	0.7133	0.758	0.4845	0.5591	0.2297	0.5573	0.7329	0.776	-1.0	0.6898	0.8597	0.8434	0.6578
16.3849	18.0	810	4.4966	0.5878	0.8533	0.6936	0.4993	0.7672	0.7421	0.2325	0.5644	0.7324	0.6614	0.8398	0.8402	-1.0	-1.0	0.5818	0.7724	0.5937	0.6923
16.3849	19.0	855	4.5446	0.5836	0.8469	0.6881	0.4941	0.7627	0.7538	0.2295	0.5633	0.7325	0.6591	0.84	0.8559	-1.0	-1.0	0.5747	0.7693	0.5924	0.6957
16.3849	20.0	900	4.5455	0.589	0.8579	0.6926	0.4971	0.7623	0.7609	0.2326	0.5651	0.7338	0.6618	0.8389	0.8517	-1.0	-1.0	0.5771	0.7728	0.6008	0.6949

Framework versions

• Transformers 4.56.0
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0