rtdetr-v2-r101-cppe5-finetune-2

Bogdandu24/rtdetr-v2-r101-cppe5-finetune-2

rtdetr-v2-r101-cppe5-finetune-2 is a object detection model from Bogdandu24. 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_v2
Access: Public
Created: May 11, 2025
Updated: May 18, 2025
SHA: 831e2673

Base models

PekingU/rtdetr_v2_r101vd

Model card

From the Hugging Face model README

rtdetr-v2-r101-cppe5-finetune-2

This model is a fine-tuned version of PekingU/rtdetr_v2_r101vd for the detection of coronary artery stenosis on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 9.5258
Map: 0.3781
Map 50: 0.9125
Map 75: 0.2023
Map Small: 0.2722
Map Medium: 0.4358
Map Large: 0.4439
Mar 1: 0.4279
Mar 10: 0.6136
Mar 100: 0.6664
Mar Small: 0.6158
Mar Medium: 0.701
Mar Large: 0.6333

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: 24
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.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: 50

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
No log	1.0	313	18.7992	0.0102	0.037	0.0016	0.0193	0.023	0.0	0.0444	0.1863	0.2535	0.253	0.2555	0.0
112.163	2.0	626	14.0671	0.0806	0.265	0.0204	0.0711	0.0931	0.1998	0.1649	0.3478	0.4882	0.4476	0.5154	0.5667
112.163	3.0	939	13.0106	0.1549	0.5077	0.0356	0.1316	0.1788	0.2388	0.2393	0.4002	0.4952	0.4637	0.5172	0.4
12.4154	4.0	1252	12.8674	0.1861	0.578	0.0506	0.1487	0.2151	0.3809	0.2786	0.4329	0.5228	0.5074	0.5334	0.5
10.9002	5.0	1565	12.7759	0.1994	0.6122	0.0592	0.1562	0.2337	0.4435	0.2923	0.4511	0.5369	0.5092	0.5555	0.5667
10.9002	6.0	1878	12.9410	0.2477	0.6845	0.1046	0.1891	0.2878	0.4869	0.3295	0.4896	0.5643	0.5301	0.5872	0.6333
10.0597	7.0	2191	12.0453	0.2511	0.7021	0.092	0.1894	0.2916	0.4639	0.3144	0.4984	0.5683	0.5179	0.6024	0.6
9.4738	8.0	2504	11.6443	0.262	0.6911	0.1112	0.2077	0.3015	0.2426	0.3408	0.5317	0.5898	0.5452	0.6209	0.4667
9.4738	9.0	2817	11.4795	0.3091	0.7994	0.1487	0.2497	0.3488	0.3159	0.3764	0.5331	0.611	0.5664	0.6427	0.4
9.0887	10.0	3130	11.0460	0.3147	0.8058	0.1438	0.2394	0.365	0.469	0.3837	0.5331	0.5933	0.5301	0.6366	0.5333
9.0887	11.0	3443	10.8509	0.3038	0.7871	0.1337	0.214	0.3601	0.3414	0.3804	0.5429	0.5945	0.5435	0.6304	0.4
8.7187	12.0	3756	11.2568	0.2689	0.7366	0.1041	0.1987	0.3154	0.3722	0.3456	0.5245	0.5972	0.5402	0.6362	0.5667
8.5299	13.0	4069	10.6576	0.2818	0.7436	0.1189	0.2007	0.3285	0.2467	0.3433	0.5424	0.6164	0.5732	0.6466	0.5
8.5299	14.0	4382	10.3815	0.2688	0.6636	0.1296	0.1923	0.3198	0.395	0.3618	0.5586	0.6085	0.5518	0.6482	0.4333
8.2714	15.0	4695	10.2251	0.2846	0.7427	0.1324	0.1992	0.3356	0.3412	0.3408	0.5538	0.6187	0.5554	0.6623	0.5333
8.0708	16.0	5008	10.4468	0.2912	0.7585	0.1371	0.188	0.3503	0.2716	0.3442	0.5606	0.6252	0.5839	0.6551	0.3333
8.0708	17.0	5321	10.0924	0.3174	0.8149	0.1384	0.2167	0.3764	0.4464	0.3772	0.5462	0.6038	0.5426	0.6462	0.5
7.8789	18.0	5634	10.1511	0.292	0.7808	0.1069	0.1984	0.3441	0.4159	0.3466	0.5514	0.6103	0.5554	0.6484	0.5
7.8789	19.0	5947	10.0809	0.2872	0.7423	0.1304	0.1846	0.3477	0.3738	0.3546	0.558	0.6196	0.556	0.6638	0.4667
7.7461	20.0	6260	9.7884	0.3327	0.8237	0.1515	0.2486	0.3814	0.4109	0.396	0.579	0.6268	0.586	0.6555	0.4667
7.6557	21.0	6573	9.9407	0.3006	0.7914	0.1273	0.1849	0.3632	0.442	0.3455	0.5555	0.6118	0.5673	0.6429	0.4667
7.6557	22.0	6886	9.7508	0.3278	0.8365	0.1451	0.2254	0.3855	0.4052	0.372	0.5611	0.6179	0.567	0.6524	0.6333
7.4889	23.0	7199	9.9115	0.3163	0.8201	0.1201	0.2166	0.3695	0.4521	0.3826	0.566	0.6277	0.5869	0.6561	0.5333
7.4078	24.0	7512	9.9078	0.3068	0.7968	0.1297	0.1912	0.3724	0.5057	0.3533	0.5677	0.6291	0.5634	0.6743	0.5333
7.4078	25.0	7825	9.9138	0.2985	0.7844	0.1291	0.1905	0.3604	0.499	0.3514	0.5645	0.6385	0.5744	0.683	0.5
7.3325	26.0	8138	9.6711	0.3194	0.8042	0.1664	0.2146	0.3795	0.3995	0.3785	0.5969	0.6546	0.5905	0.6992	0.5
7.3325	27.0	8451	10.1850	0.2888	0.7886	0.1142	0.1822	0.3504	0.4257	0.3229	0.5677	0.6307	0.5616	0.6781	0.5667
7.2804	28.0	8764	10.1133	0.3185	0.8264	0.1482	0.2248	0.3728	0.4663	0.3688	0.5832	0.6509	0.5872	0.6953	0.4667
7.2029	29.0	9077	9.5707	0.3228	0.8097	0.1547	0.2271	0.3774	0.4653	0.3957	0.6044	0.6565	0.6071	0.6913	0.4667
7.2029	30.0	9390	10.1563	0.3377	0.8745	0.1421	0.2499	0.3885	0.3663	0.3789	0.5962	0.648	0.5988	0.683	0.4
7.0825	31.0	9703	9.8796	0.3364	0.8697	0.1503	0.242	0.3886	0.4629	0.3754	0.606	0.6556	0.6006	0.6939	0.5
7.026	32.0	10016	9.8638	0.345	0.8834	0.1628	0.2468	0.4006	0.4779	0.3856	0.6067	0.6615	0.6149	0.6937	0.5667
7.026	33.0	10329	9.6067	0.3561	0.8764	0.1902	0.2453	0.4162	0.399	0.3958	0.608	0.6585	0.6074	0.6947	0.4
6.8795	34.0	10642	9.5258	0.3781	0.9125	0.2023	0.2722	0.4358	0.4439	0.4279	0.6136	0.6664	0.6158	0.701	0.6333
6.8795	35.0	10955	9.4449	0.368	0.9007	0.188	0.2706	0.4204	0.5	0.4066	0.6088	0.6685	0.6253	0.699	0.5
6.7461	36.0	11268	9.2644	0.3658	0.8989	0.1945	0.2671	0.419	0.4653	0.4136	0.6142	0.6635	0.6202	0.6941	0.4667
6.64	37.0	11581	9.2426	0.3645	0.9017	0.1894	0.2627	0.4184	0.3975	0.4108	0.6078	0.6507	0.5997	0.6866	0.4333
6.64	38.0	11894	9.3995	0.3721	0.899	0.1976	0.2764	0.4243	0.4664	0.4192	0.6066	0.6544	0.6068	0.6877	0.5
6.4988	39.0	12207	9.4093	0.3769	0.9078	0.1964	0.2918	0.4253	0.4664	0.4288	0.6064	0.6531	0.6021	0.6887	0.5
6.4018	40.0	12520	9.3955	0.3615	0.8954	0.1759	0.2773	0.4089	0.4663	0.4116	0.6028	0.6546	0.5982	0.6941	0.4667
6.4018	41.0	12833	9.3966	0.3711	0.8973	0.1989	0.2758	0.4238	0.4993	0.421	0.6102	0.6635	0.6152	0.6968	0.6
6.3285	42.0	13146	9.4656	0.3655	0.8967	0.1968	0.2785	0.4172	0.4694	0.4245	0.6163	0.6701	0.6131	0.7097	0.5333
6.3285	43.0	13459	9.4390	0.3728	0.9149	0.1933	0.2914	0.419	0.4663	0.4295	0.6202	0.6675	0.6187	0.7018	0.4667
6.2397	44.0	13772	9.3682	0.3636	0.8795	0.202	0.2785	0.4146	0.4664	0.428	0.6148	0.6627	0.6065	0.7018	0.5
6.1381	45.0	14085	9.2842	0.3692	0.8998	0.2041	0.279	0.4205	0.4663	0.4279	0.6116	0.6625	0.6074	0.7012	0.4667
6.1381	46.0	14398	9.2333	0.3686	0.8987	0.2006	0.2758	0.4211	0.4663	0.428	0.6155	0.6621	0.6095	0.699	0.4667
6.0162	47.0	14711	9.2259	0.366	0.9051	0.1986	0.2756	0.4155	0.4663	0.4245	0.6157	0.6641	0.6128	0.7002	0.4667
5.9406	48.0	15024	9.2568	0.3632	0.902	0.1941	0.2729	0.4133	0.4733	0.4198	0.6145	0.6623	0.606	0.7014	0.5333
5.9406	49.0	15337	9.2850	0.3635	0.904	0.196	0.2733	0.4147	0.479	0.4223	0.616	0.6669	0.6134	0.704	0.5333
5.8549	50.0	15650	9.2647	0.3663	0.8999	0.1997	0.2746	0.4177	0.4663	0.4232	0.6149	0.664	0.6107	0.7014	0.4667

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Task: Object Detection
Library: transformers
Type: rt_detr_v2
License: apache-2.0
Created: May 11, 2025
Updated: May 18, 2025