rfdetr-road-signs-agree2

merve/rfdetr-road-signs-agree2

rfdetr-road-signs-agree2 is a object detection model from merve. 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: rf_detr
Access: Public
Created: Jun 19, 2026
Updated: Jun 19, 2026
SHA: 707e6426

Base models

Roboflow/rf-detr-medium

Model card

From the Hugging Face model README

rfdetr-road-signs-agree2

This model is a fine-tuned version of Roboflow/rf-detr-medium on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 13.9987
Map: 0.185
Map 50: 0.2141
Map 75: 0.206
Map Small: 0.0727
Map Medium: 0.2826
Map Large: 0.1566
Mar 1: 0.7158
Mar 10: 0.8337
Mar 100: 0.8425
Mar Small: 0.225
Mar Medium: 0.7327
Mar Large: 0.8234
Map Bus Stop: 0.5739
Mar 100 Bus Stop: 1.0
Map Do Not Enter: 0.151
Mar 100 Do Not Enter: 0.9462
Map Do Not Stop: 0.0515
Mar 100 Do Not Stop: 0.9625
Map Do Not Turn L: 0.0614
Mar 100 Do Not Turn L: 1.0
Map Do Not Turn R: 0.1336
Mar 100 Do Not Turn R: 0.94
Map Do Not U Turn: 0.0672
Mar 100 Do Not U Turn: 0.9222
Map Enter Left Lane: -1.0
Mar 100 Enter Left Lane: -1.0
Map Green Light: 0.5422
Mar 100 Green Light: 0.8667
Map Left Right Lane: 0.2297
Mar 100 Left Right Lane: 0.9778
Map No Parking: 0.0649
Mar 100 No Parking: 0.975
Map Parking: 0.01
Mar 100 Parking: 0.9667
Map Ped Crossing: 0.1775
Mar 100 Ped Crossing: 0.92
Map Ped Zebra Cross: 0.0
Mar 100 Ped Zebra Cross: 0.0
Map Railway Crossing: 0.0396
Mar 100 Railway Crossing: 0.94
Map Red Light: 0.0853
Mar 100 Red Light: 0.4636
Map Stop: 0.3227
Mar 100 Stop: 0.9538
Map Traffic Light: 0.0872
Mar 100 Traffic Light: 0.7065
Map U Turn: 0.0204
Mar 100 U Turn: 0.975
Map Warning: 0.6498
Mar 100 Warning: 0.9588
Map Yellow Light: 0.2469
Mar 100 Yellow Light: 0.5333
Map T Intersection L: -1.0
Mar 100 T Intersection L: -1.0

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 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
lr_scheduler_warmup_steps: 0.05
num_epochs: 10

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 Bus Stop	Mar 100 Bus Stop	Map Do Not Enter	Mar 100 Do Not Enter	Map Do Not Stop	Mar 100 Do Not Stop	Map Do Not Turn L	Mar 100 Do Not Turn L	Map Do Not Turn R	Mar 100 Do Not Turn R	Map Do Not U Turn	Mar 100 Do Not U Turn	Map Green Light	Mar 100 Green Light	Map Left Right Lane	Mar 100 Left Right Lane	Map No Parking	Mar 100 No Parking	Map Parking	Mar 100 Parking	Map Ped Crossing	Mar 100 Ped Crossing	Map Ped Zebra Cross	Mar 100 Ped Zebra Cross	Map Railway Crossing	Mar 100 Railway Crossing	Map Red Light	Mar 100 Red Light	Map Stop	Mar 100 Stop	Map Traffic Light	Mar 100 Traffic Light	Map U Turn	Mar 100 U Turn	Map Warning	Mar 100 Warning	Map Yellow Light	Mar 100 Yellow Light	Map Enter Left Lane	Mar 100 Enter Left Lane	Map T Intersection L	Mar 100 T Intersection L
45.9286	1.0	117	29.2449	0.0	0.0001	0.0	0.0	0.0	0.0	0.0	0.0019	0.0054	0.0	0.0	0.0057	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0003	0.1029	0.0	0.0	-1.0	-1.0	-1.0	-1.0
15.6887	2.0	234	20.3756	0.0002	0.0007	0.0001	0.0	0.0	0.0003	0.0023	0.0211	0.0415	0.0	0.0	0.0453	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0444	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0002	0.1355	0.0	0.0	0.0043	0.6088	0.0	0.0	-1.0	-1.0	-1.0	-1.0
12.5560	3.0	351	16.4438	0.0062	0.012	0.006	0.0	0.0	0.0066	0.0468	0.085	0.1182	0.0	0.0	0.1283	0.0	0.0	0.0141	0.2385	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0008	0.2444	0.0152	0.5375	0.0	0.0	0.0001	0.05	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0014	0.3258	0.0	0.0	0.0862	0.85	0.0	0.0	-1.0	-1.0	-1.0	-1.0
10.7193	4.0	468	13.3856	0.0183	0.0254	0.0214	0.0	0.0521	0.0193	0.1882	0.2513	0.2741	0.0	0.1033	0.2766	0.0	0.0	0.0304	0.7692	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0029	0.3167	0.0081	0.4056	0.0191	0.825	0.0	0.0	0.0174	0.84	0.0	0.0	0.0	0.0	0.0	0.0	0.0237	0.5692	0.0295	0.571	0.0	0.0	0.2174	0.9118	0.0	0.0	-1.0	-1.0	-1.0	-1.0
10.0170	5.0	585	13.3941	0.0787	0.1073	0.0886	0.0252	0.1605	0.0601	0.435	0.5432	0.5632	0.15	0.5753	0.5459	0.0	0.0	0.0199	0.9077	0.0	0.0	0.0595	0.9667	0.092	0.96	0.0181	0.9111	0.2831	0.8	0.0501	0.9667	0.0737	0.8375	0.0	0.0	0.0246	0.97	0.0	0.0	0.1993	0.4	0.0323	0.2273	0.1234	0.8769	0.0578	0.671	0.0	0.0	0.3084	0.95	0.1541	0.2556	-1.0	-1.0	-1.0	-1.0
9.6411	6.0	702	12.7907	0.0803	0.1014	0.0911	0.0144	0.1578	0.0668	0.4997	0.6113	0.6256	0.1	0.61	0.6068	0.0	0.0	0.1028	0.9462	0.0	0.0	0.0162	0.9667	0.0417	0.96	0.0587	0.9222	-1.0	-1.0	0.3469	0.85	0.068	0.9556	0.0575	0.9375	0.0048	0.2333	0.0588	0.95	0.0	0.0	0.0127	0.6	0.0446	0.3	0.074	0.8923	0.057	0.6645	0.0251	0.475	0.4683	0.9441	0.0876	0.2889	-1.0	-1.0
9.0429	7.0	819	13.1367	0.1297	0.1578	0.1486	0.061	0.2856	0.1026	0.6832	0.822	0.8312	0.1875	0.6993	0.8165	0.0689	0.95	0.1095	0.9615	0.0783	0.9375	0.0369	1.0	0.1225	0.94	0.0494	0.9444	-1.0	-1.0	0.5129	0.8167	0.0868	0.9778	0.0413	0.9625	0.0211	0.9333	0.1412	0.94	0.0	0.0	0.0246	0.88	0.0469	0.3909	0.127	0.9846	0.0899	0.6839	0.0169	1.0	0.58	0.9559	0.3108	0.5333	-1.0	-1.0
9.1348	8.0	936	13.6453	0.1427	0.1709	0.1623	0.0748	0.2836	0.1126	0.7124	0.818	0.8314	0.2125	0.742	0.8127	0.0392	1.0	0.1617	0.9538	0.0805	0.975	0.0277	0.9667	0.0774	0.96	0.0586	0.9333	-1.0	-1.0	0.5348	0.85	0.1807	0.9667	0.0992	0.975	0.0154	0.9333	0.2365	0.93	0.0	0.0	0.0381	0.78	0.0664	0.4455	0.1491	0.9308	0.0932	0.7161	0.0236	0.975	0.5626	0.9618	0.2663	0.5444	-1.0	-1.0
8.7580	9.0	1053	14.0408	0.1607	0.1895	0.1806	0.0819	0.297	0.1369	0.7046	0.8278	0.8351	0.2625	0.7227	0.8199	0.1769	1.0	0.145	0.9462	0.1206	0.9625	0.0458	1.0	0.0676	0.94	0.0674	0.9111	-1.0	-1.0	0.5253	0.8167	0.2417	0.9722	0.0866	0.9625	0.0153	0.9667	0.1702	0.93	0.0	0.0	0.0372	0.86	0.0996	0.4818	0.2811	0.9462	0.0924	0.7161	0.0173	0.975	0.6062	0.9471	0.2577	0.5333	-1.0	-1.0
8.4909	10.0	1170	13.9987	0.185	0.2141	0.206	0.0727	0.2826	0.1566	0.7158	0.8337	0.8425	0.225	0.7327	0.8234	0.5739	1.0	0.151	0.9462	0.0515	0.9625	0.0614	1.0	0.1336	0.94	0.0672	0.9222	-1.0	-1.0	0.5422	0.8667	0.2297	0.9778	0.0649	0.975	0.01	0.9667	0.1775	0.92	0.0	0.0	0.0396	0.94	0.0853	0.4636	0.3227	0.9538	0.0872	0.7065	0.0204	0.975	0.6498	0.9588	0.2469	0.5333	-1.0	-1.0

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: rf_detr
License: apache-2.0
Created: Jun 19, 2026
Updated: Jun 19, 2026