rtdetr

b09501048/rtdetr

rtdetr is a object detection model from b09501048. Use it when you need objects located in an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Object Detection
Library: transformers
License: apache-2.0
Model type: rt_detr
Access: Public
Created: Oct 12, 2024
Updated: Oct 12, 2024
SHA: c8cbc71b

Base models

PekingU/rtdetr_r50vd_coco_o365

Model card

From the Hugging Face model README

rtdetr

This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 10.4377
Map: 0.2587
Map 50: 0.4214
Map 75: 0.2668
Map Small: 0.0132
Map Medium: 0.0707
Map Large: 0.3083
Mar 1: 0.2412
Mar 10: 0.4736
Mar 100: 0.4998
Mar Small: 0.0211
Mar Medium: 0.1684
Mar Large: 0.5787
Map Person: 0.6872
Mar 100 Person: 0.7948
Map Ear: 0.3267
Mar 100 Ear: 0.4363
Map Earmuffs: 0.1061
Mar 100 Earmuffs: 0.3967
Map Face: 0.5362
Mar 100 Face: 0.6549
Map Face-guard: 0.0236
Mar 100 Face-guard: 0.51
Map Face-mask-medical: 0.1466
Mar 100 Face-mask-medical: 0.3479
Map Foot: 0.1167
Mar 100 Foot: 0.3963
Map Tools: 0.125
Mar 100 Tools: 0.3664
Map Glasses: 0.2452
Mar 100 Glasses: 0.4355
Map Gloves: 0.3086
Mar 100 Gloves: 0.4919
Map Helmet: 0.2733
Mar 100 Helmet: 0.4595
Map Hands: 0.4959
Mar 100 Hands: 0.6459
Map Head: 0.6255
Mar 100 Head: 0.7222
Map Medical-suit: 0.0071
Mar 100 Medical-suit: 0.6667
Map Shoes: 0.2826
Mar 100 Shoes: 0.4203
Map Safety-suit: 0.0628
Mar 100 Safety-suit: 0.6043
Map Safety-vest: 0.0284
Mar 100 Safety-vest: 0.1479

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 10
mixed_precision_training: Native AMP

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 Person	Mar 100 Person	Map Ear	Mar 100 Ear	Map Earmuffs	Mar 100 Earmuffs	Map Face	Mar 100 Face	Map Face-guard	Mar 100 Face-guard	Map Face-mask-medical	Mar 100 Face-mask-medical	Map Foot	Mar 100 Foot	Map Tools	Mar 100 Tools	Map Glasses	Mar 100 Glasses	Map Gloves	Mar 100 Gloves	Map Helmet	Mar 100 Helmet	Map Hands	Mar 100 Hands	Map Head	Mar 100 Head	Map Medical-suit	Mar 100 Medical-suit	Map Shoes	Mar 100 Shoes	Map Safety-suit	Mar 100 Safety-suit	Map Safety-vest	Mar 100 Safety-vest
No log	1.0	230	11.8687	0.1809	0.2959	0.1854	0.0001	0.0405	0.2126	0.1745	0.3538	0.3831	0.0003	0.1392	0.4463	0.6713	0.7877	0.2916	0.3832	0.0012	0.27	0.4942	0.6099	0.0003	0.11	0.0051	0.2792	0.0203	0.2963	0.0294	0.2341	0.1226	0.3419	0.1196	0.4622	0.0861	0.3709	0.4235	0.6114	0.5619	0.6973	0.0033	0.3333	0.2386	0.4097	0.0057	0.3043	0.0	0.0106
No log	2.0	460	11.2318	0.2049	0.3336	0.2104	0.0001	0.0546	0.2415	0.2033	0.4067	0.4298	0.0009	0.1818	0.4943	0.6806	0.7963	0.299	0.4008	0.0019	0.2967	0.5212	0.6258	0.0013	0.3	0.0672	0.3167	0.0227	0.3556	0.0463	0.2657	0.1828	0.3808	0.2148	0.4744	0.1592	0.4101	0.4415	0.6258	0.5971	0.708	0.0043	0.45	0.2338	0.4048	0.0095	0.4696	0.0001	0.0255
21.6045	3.0	690	10.5848	0.228	0.3728	0.2323	0.0023	0.0556	0.2715	0.229	0.4415	0.472	0.0054	0.1589	0.5472	0.6736	0.7933	0.3235	0.4293	0.015	0.3133	0.5366	0.6397	0.0043	0.46	0.1355	0.3646	0.0585	0.3759	0.0672	0.3417	0.213	0.425	0.2673	0.4793	0.2207	0.4772	0.4553	0.6371	0.6186	0.7185	0.0054	0.575	0.2525	0.4074	0.0273	0.4957	0.0018	0.0904
21.6045	4.0	920	10.5421	0.2332	0.3782	0.2411	0.0009	0.0614	0.2771	0.223	0.4435	0.4738	0.0048	0.1859	0.5433	0.6844	0.797	0.3263	0.4265	0.0182	0.3733	0.5415	0.651	0.0071	0.35	0.1292	0.3625	0.0606	0.3815	0.0843	0.3587	0.213	0.4349	0.2804	0.4659	0.2003	0.4418	0.4684	0.6475	0.6229	0.7196	0.0096	0.6083	0.2897	0.4371	0.0254	0.5217	0.0029	0.0777
16.2105	5.0	1150	10.5670	0.2425	0.4026	0.2462	0.0026	0.0678	0.2876	0.2248	0.4572	0.49	0.0084	0.175	0.5649	0.6759	0.7959	0.3303	0.4287	0.051	0.37	0.5377	0.6458	0.0389	0.54	0.1382	0.3313	0.0542	0.3833	0.0967	0.339	0.2201	0.4081	0.2746	0.4821	0.2307	0.4747	0.4704	0.6355	0.6247	0.727	0.0079	0.6833	0.2719	0.4297	0.0787	0.5174	0.0212	0.1372
16.2105	6.0	1380	10.5205	0.2454	0.4021	0.2486	0.004	0.0642	0.2915	0.2318	0.466	0.4921	0.009	0.2002	0.565	0.6883	0.7967	0.3229	0.4303	0.0432	0.4167	0.533	0.6475	0.0206	0.49	0.134	0.3417	0.0763	0.3759	0.1108	0.3549	0.2424	0.4302	0.2975	0.4923	0.2172	0.4481	0.4765	0.6418	0.622	0.7188	0.0088	0.675	0.2824	0.4318	0.08	0.5478	0.0164	0.1255
14.9514	7.0	1610	10.4281	0.2503	0.4074	0.2573	0.0122	0.071	0.2971	0.2336	0.4732	0.5003	0.0192	0.1699	0.5787	0.6909	0.797	0.3294	0.4358	0.0799	0.3767	0.5398	0.6511	0.0195	0.55	0.1253	0.3229	0.0995	0.4037	0.1184	0.3798	0.2401	0.4262	0.3016	0.4878	0.2372	0.4582	0.4943	0.6485	0.6247	0.7213	0.0081	0.6583	0.2781	0.4214	0.0502	0.6087	0.019	0.1574
14.9514	8.0	1840	10.4168	0.2591	0.4207	0.2665	0.0129	0.069	0.3082	0.2426	0.471	0.5001	0.0198	0.1687	0.5777	0.6901	0.7971	0.3275	0.4379	0.0953	0.3833	0.539	0.6537	0.0503	0.54	0.1408	0.3333	0.1041	0.387	0.1254	0.3735	0.2417	0.4314	0.3014	0.4854	0.2689	0.4557	0.4984	0.6511	0.6291	0.725	0.0068	0.6667	0.2833	0.4224	0.0785	0.6217	0.0245	0.1372
14.5079	9.0	2070	10.4207	0.2605	0.4265	0.2676	0.013	0.072	0.3104	0.2384	0.4779	0.5015	0.0212	0.1712	0.5805	0.6874	0.7957	0.3287	0.439	0.109	0.3967	0.5364	0.6546	0.0376	0.52	0.1454	0.3333	0.11	0.4019	0.1238	0.3652	0.2454	0.439	0.309	0.4882	0.2851	0.4646	0.4972	0.6475	0.6276	0.7259	0.0068	0.6667	0.2853	0.4249	0.0651	0.6043	0.0291	0.1585
14.5079	10.0	2300	10.4377	0.2587	0.4214	0.2668	0.0132	0.0707	0.3083	0.2412	0.4736	0.4998	0.0211	0.1684	0.5787	0.6872	0.7948	0.3267	0.4363	0.1061	0.3967	0.5362	0.6549	0.0236	0.51	0.1466	0.3479	0.1167	0.3963	0.125	0.3664	0.2452	0.4355	0.3086	0.4919	0.2733	0.4595	0.4959	0.6459	0.6255	0.7222	0.0071	0.6667	0.2826	0.4203	0.0628	0.6043	0.0284	0.1479

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

Task: Object Detection
Library: transformers
Type: rt_detr
License: apache-2.0
Created: Oct 12, 2024
Updated: Oct 12, 2024