panels_detection_rtdetr

This model is a fine-tuned version of PekingU/rtdetr_r101vd_coco_o365 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 9.5718
• Map: 0.5617
• Map 50: 0.6631
• Map 75: 0.6137
• Map Small: -1.0
• Map Medium: 0.3451
• Map Large: 0.5935
• Mar 1: 0.6546
• Mar 10: 0.7877
• Mar 100: 0.8058
• Mar Small: -1.0
• Mar Medium: 0.5802
• Mar Large: 0.8672
• Map Radar (small): 0.3509
• Mar 100 Radar (small): 0.8077
• Map Ship management system (small): 0.6748
• Mar 100 Ship management system (small): 0.8933
• Map Radar (large): 0.5846
• Mar 100 Radar (large): 0.8624
• Map Ship management system (large): 0.7577
• Mar 100 Ship management system (large): 0.9341
• Map Ship management system (top): 0.789
• Mar 100 Ship management system (top): 0.8356
• Map Ecdis (large): 0.3281
• Mar 100 Ecdis (large): 0.7652
• Map Visual observation (small): 0.585
• Mar 100 Visual observation (small): 0.902
• Map Ecdis (small): 0.7635
• Mar 100 Ecdis (small): 0.8967
• Map Ship management system (table top): 0.6306
• Mar 100 Ship management system (table top): 0.7882
• Map Thruster control: 0.4949
• Mar 100 Thruster control: 0.7447
• Map Visual observation (left): 0.6062
• Mar 100 Visual observation (left): 0.8395
• Map Visual observation (mid): 0.7946
• Mar 100 Visual observation (mid): 0.8901
• Map Visual observation (right): 0.7446
• Mar 100 Visual observation (right): 0.8966
• Map Bow thruster: 0.2392
• Mar 100 Bow thruster: 0.5167
• Map Me telegraph: 0.0825
• Mar 100 Me telegraph: 0.5143

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: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• num_epochs: 7

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 Radar (small)	Mar 100 Radar (small)	Map Ship management system (small)	Mar 100 Ship management system (small)	Map Radar (large)	Mar 100 Radar (large)	Map Ship management system (large)	Mar 100 Ship management system (large)	Map Ship management system (top)	Mar 100 Ship management system (top)	Map Ecdis (large)	Mar 100 Ecdis (large)	Map Visual observation (small)	Mar 100 Visual observation (small)	Map Ecdis (small)	Mar 100 Ecdis (small)	Map Ship management system (table top)	Mar 100 Ship management system (table top)	Map Thruster control	Mar 100 Thruster control	Map Visual observation (left)	Mar 100 Visual observation (left)	Map Visual observation (mid)	Mar 100 Visual observation (mid)	Map Visual observation (right)	Mar 100 Visual observation (right)	Map Bow thruster	Mar 100 Bow thruster	Map Me telegraph	Mar 100 Me telegraph
14.2599	1.0	699	9.6242	0.4769	0.5404	0.5144	-1.0	0.2274	0.5416	0.5866	0.755	0.7709	-1.0	0.4884	0.8359	0.7408	0.92	0.672	0.8827	0.7054	0.9504	0.8329	0.926	0.7965	0.8692	0.3419	0.9571	0.2734	0.8627	0.1207	0.6933	0.4841	0.7059	0.3541	0.6947	0.5303	0.8961	0.8393	0.9342	0.2629	0.8466	0.1988	0.3583	0.0011	0.0667
8.9356	2.0	1398	9.1941	0.5527	0.6652	0.6044	-1.0	0.3212	0.574	0.6512	0.7882	0.8015	-1.0	0.6608	0.8085	0.6989	0.8862	0.5273	0.8053	0.7683	0.9145	0.7209	0.9073	0.7995	0.8644	0.4929	0.833	0.4034	0.8392	0.5519	0.8333	0.6453	0.8618	0.4221	0.6447	0.5734	0.8474	0.8714	0.8973	0.412	0.8448	0.3154	0.5333	0.0874	0.5095
8.1388	3.0	2097	9.7524	0.535	0.6013	0.5854	-1.0	0.2545	0.574	0.6219	0.7425	0.7612	-1.0	0.538	0.8183	0.6358	0.8292	0.5844	0.8013	0.6721	0.8368	0.7422	0.8829	0.7144	0.8096	0.4904	0.8562	0.7623	0.9078	0.5667	0.89	0.6409	0.7824	0.1853	0.5763	0.5453	0.7789	0.8362	0.9	0.5862	0.9207	0.0384	0.3833	0.0248	0.2619
7.5951	4.0	2796	9.3983	0.5991	0.7001	0.6587	-1.0	0.3745	0.6167	0.6957	0.8036	0.8188	-1.0	0.6611	0.8746	0.603	0.8538	0.626	0.88	0.6211	0.8496	0.8218	0.9382	0.8062	0.8433	0.3917	0.8804	0.6202	0.851	0.8307	0.9433	0.555	0.8147	0.5143	0.8	0.6609	0.8579	0.887	0.9369	0.7174	0.8759	0.2732	0.5333	0.0579	0.4238
7.1786	5.0	3495	9.1194	0.6117	0.7144	0.6689	-1.0	0.3458	0.6476	0.6904	0.8136	0.8324	-1.0	0.6649	0.8777	0.5	0.8538	0.6723	0.8733	0.7272	0.8795	0.778	0.9398	0.7803	0.8385	0.3389	0.8509	0.6484	0.8804	0.7914	0.9433	0.7053	0.8059	0.6257	0.8447	0.5945	0.8658	0.8411	0.9009	0.7812	0.9397	0.2863	0.5792	0.1053	0.4905
7.1386	6.0	4194	9.9394	0.5353	0.634	0.5921	-1.0	0.3062	0.5549	0.6429	0.7691	0.7874	-1.0	0.5638	0.8364	0.3431	0.7631	0.6563	0.8813	0.5789	0.8393	0.6941	0.9236	0.721	0.7712	0.4061	0.8018	0.5685	0.8725	0.7656	0.91	0.5317	0.8	0.5194	0.7684	0.5191	0.8039	0.7994	0.8586	0.6714	0.8793	0.2223	0.4958	0.0333	0.4429
7.0912	7.0	4893	9.5718	0.5617	0.6631	0.6137	-1.0	0.3451	0.5935	0.6546	0.7877	0.8058	-1.0	0.5802	0.8672	0.3509	0.8077	0.6748	0.8933	0.5846	0.8624	0.7577	0.9341	0.789	0.8356	0.3281	0.7652	0.585	0.902	0.7635	0.8967	0.6306	0.7882	0.4949	0.7447	0.6062	0.8395	0.7946	0.8901	0.7446	0.8966	0.2392	0.5167	0.0825	0.5143

Framework versions

• Transformers 4.46.0
• Pytorch 2.5.0+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1