rtdetr2

b09501048/rtdetr2

rtdetr2 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 13, 2024
Updated: Oct 13, 2024
SHA: 917b9846

Base models

PekingU/rtdetr_r50vd_coco_o365

Model card

From the Hugging Face model README

rtdetr2

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: 11.8738
Map: 0.2766
Map 50: 0.439
Map 75: 0.2829
Map Small: 0.1319
Map Medium: 0.375
Map Large: 0.4519
Mar 1: 0.2272
Mar 10: 0.438
Mar 100: 0.4559
Mar Small: 0.2268
Mar Medium: 0.551
Mar Large: 0.6909
Map Person: 0.7159
Mar 100 Person: 0.8345
Map Ear: 0.3388
Mar 100 Ear: 0.4368
Map Earmuffs: 0.2418
Mar 100 Earmuffs: 0.5135
Map Face: 0.4365
Mar 100 Face: 0.6137
Map Face-guard: 0.1839
Mar 100 Face-guard: 0.5625
Map Face-mask-medical: 0.1733
Mar 100 Face-mask-medical: 0.2842
Map Foot: 0.0448
Mar 100 Foot: 0.2647
Map Tools: 0.0879
Mar 100 Tools: 0.284
Map Glasses: 0.224
Mar 100 Glasses: 0.3644
Map Gloves: 0.2994
Mar 100 Gloves: 0.4226
Map Helmet: 0.2581
Mar 100 Helmet: 0.3714
Map Hands: 0.5167
Mar 100 Hands: 0.6166
Map Head: 0.5923
Mar 100 Head: 0.6739
Map Medical-suit: 0.1135
Mar 100 Medical-suit: 0.5556
Map Shoes: 0.3707
Mar 100 Shoes: 0.483
Map Safety-suit: 0.0097
Mar 100 Safety-suit: 0.315
Map Safety-vest: 0.0956
Mar 100 Safety-vest: 0.1532

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
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	459	12.5722	0.1349	0.2143	0.143	0.0728	0.1775	0.1605	0.1436	0.2943	0.3139	0.1692	0.4142	0.5087	0.4573	0.8371	0.1857	0.4252	0.0002	0.1162	0.3926	0.613	0.001	0.2625	0.0572	0.1719	0.0004	0.102	0.0248	0.1706	0.0501	0.361	0.0807	0.1743	0.059	0.2911	0.2425	0.521	0.4662	0.6855	0.0	0.0222	0.2745	0.4592	0.0016	0.095	0.0002	0.0274
26.3979	2.0	918	11.9482	0.208	0.3216	0.2213	0.1016	0.2792	0.3172	0.1846	0.363	0.3799	0.2024	0.4917	0.6211	0.633	0.8297	0.2283	0.335	0.0016	0.3622	0.5135	0.6272	0.0562	0.4875	0.1298	0.2737	0.0051	0.2137	0.0393	0.2209	0.1385	0.3452	0.2407	0.3783	0.1832	0.3482	0.4565	0.6032	0.5955	0.694	0.0002	0.0444	0.3128	0.4551	0.0009	0.17	0.0009	0.0694
18.1729	3.0	1377	11.9715	0.2184	0.3418	0.2313	0.1126	0.3017	0.347	0.1909	0.3883	0.4101	0.2354	0.5085	0.6091	0.6705	0.8279	0.3031	0.4063	0.0117	0.3919	0.4866	0.6111	0.0355	0.5375	0.1147	0.2825	0.0111	0.2784	0.0514	0.2397	0.2188	0.4102	0.2171	0.3757	0.1851	0.3554	0.4664	0.5985	0.5942	0.6886	0.0014	0.2167	0.3408	0.4824	0.001	0.14	0.0037	0.129
16.7823	4.0	1836	11.5629	0.2362	0.3642	0.2521	0.1213	0.3333	0.3732	0.2058	0.3925	0.4068	0.2236	0.5135	0.6145	0.6948	0.8366	0.3441	0.4262	0.0691	0.4216	0.5345	0.6544	0.0409	0.4625	0.1528	0.3053	0.0125	0.2647	0.0651	0.2936	0.1319	0.3079	0.2895	0.4088	0.2312	0.3634	0.4867	0.5981	0.6117	0.6891	0.0104	0.2056	0.3356	0.4696	0.001	0.115	0.0039	0.0935
15.8096	5.0	2295	11.8381	0.2593	0.4074	0.2739	0.1281	0.3589	0.4355	0.2155	0.4214	0.4365	0.2201	0.5394	0.6675	0.6888	0.8275	0.3686	0.4572	0.1986	0.4378	0.5029	0.6389	0.1532	0.475	0.1791	0.3421	0.0229	0.2137	0.0583	0.2428	0.2389	0.4034	0.2943	0.4434	0.2326	0.3607	0.486	0.601	0.5887	0.6697	0.0124	0.4278	0.3458	0.4595	0.0035	0.25	0.0332	0.1694
14.7312	6.0	2754	11.8431	0.2451	0.3926	0.2562	0.1243	0.3434	0.3958	0.2131	0.412	0.4263	0.2187	0.5118	0.6653	0.6987	0.8307	0.3293	0.4215	0.1879	0.4162	0.4487	0.6174	0.0248	0.4875	0.1453	0.3263	0.0302	0.249	0.0799	0.2737	0.2061	0.3531	0.2911	0.4035	0.2357	0.333	0.5077	0.6144	0.5511	0.6344	0.0402	0.5222	0.3495	0.4789	0.0018	0.15	0.0394	0.1355
14.5207	7.0	3213	11.9739	0.2614	0.4212	0.2679	0.1246	0.3646	0.4079	0.2235	0.418	0.4289	0.2175	0.5351	0.617	0.6899	0.8326	0.3247	0.419	0.2156	0.4541	0.4293	0.6056	0.1801	0.4875	0.1518	0.2807	0.0507	0.2294	0.084	0.2649	0.2206	0.3678	0.2696	0.3903	0.2628	0.3625	0.5094	0.612	0.5925	0.6774	0.021	0.4056	0.3423	0.4637	0.0132	0.3	0.0863	0.1387
14.0764	8.0	3672	11.9295	0.2646	0.4292	0.2715	0.1278	0.3629	0.4405	0.2236	0.4189	0.4308	0.2232	0.5261	0.668	0.7004	0.8286	0.3267	0.4195	0.2031	0.4459	0.4416	0.6123	0.188	0.45	0.1675	0.3053	0.0274	0.2373	0.0765	0.2644	0.2267	0.3921	0.321	0.446	0.245	0.367	0.5036	0.6054	0.5888	0.6741	0.0382	0.3722	0.3382	0.4527	0.019	0.305	0.0858	0.1452
13.772	9.0	4131	11.8863	0.2678	0.4293	0.2743	0.1306	0.3694	0.4456	0.2276	0.4405	0.4524	0.2331	0.5595	0.6747	0.7065	0.8322	0.3349	0.432	0.2274	0.5568	0.4314	0.6113	0.1785	0.5625	0.1666	0.3228	0.0354	0.2608	0.0893	0.2812	0.2225	0.3842	0.2942	0.4235	0.2533	0.367	0.5127	0.6124	0.5853	0.6719	0.043	0.4611	0.3693	0.4833	0.0097	0.245	0.092	0.1839
13.5604	10.0	4590	11.8738	0.2766	0.439	0.2829	0.1319	0.375	0.4519	0.2272	0.438	0.4559	0.2268	0.551	0.6909	0.7159	0.8345	0.3388	0.4368	0.2418	0.5135	0.4365	0.6137	0.1839	0.5625	0.1733	0.2842	0.0448	0.2647	0.0879	0.284	0.224	0.3644	0.2994	0.4226	0.2581	0.3714	0.5167	0.6166	0.5923	0.6739	0.1135	0.5556	0.3707	0.483	0.0097	0.315	0.0956	0.1532

Framework versions

Transformers 4.46.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

Task: Object Detection
Library: transformers
Type: rt_detr
License: apache-2.0
Created: Oct 13, 2024
Updated: Oct 13, 2024