rtdetr-tray-cart

nielsr/rtdetr-tray-cart

rtdetr-tray-cart is a object detection model from nielsr. 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: Mar 3, 2026
Updated: Mar 3, 2026
SHA: 510ae5ee

Base models

PekingU/rtdetr_r101vd_coco_o365

Model card

From the Hugging Face model README

rtdetr-tray-cart

This model is a fine-tuned version of PekingU/rtdetr_r101vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 8.0013
Map: 0.3254
Map 50: 0.5528
Map 75: 0.3324
Map Small: 0.6086
Map Medium: 0.3016
Map Large: 0.5975
Mar 1: 0.0588
Mar 10: 0.3009
Mar 100: 0.5992
Mar Small: 0.675
Mar Medium: 0.5598
Mar Large: 0.8468
Map Tray: 0.2404
Mar 100 Tray: 0.505
Map Cart: 0.4104
Mar 100 Cart: 0.6935

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
num_epochs: 50
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 Tray	Mar 100 Tray	Map Cart	Mar 100 Cart
No log	1.0	13	19.9337	0.002	0.0076	0.0004	0.0	0.0027	0.0154	0.0	0.0057	0.0623	0.0	0.0603	0.0895	0.0034	0.066	0.0006	0.0587
No log	2.0	26	22.1722	0.0027	0.0084	0.0012	0.0001	0.0052	0.0111	0.0	0.0057	0.0701	0.0139	0.0649	0.1323	0.0049	0.0685	0.0004	0.0717
No log	3.0	39	17.1149	0.0113	0.0298	0.0068	0.0591	0.0127	0.042	0.0008	0.0178	0.105	0.3056	0.0713	0.2169	0.0201	0.1273	0.0026	0.0826
No log	4.0	52	10.9760	0.1099	0.1888	0.1182	0.2553	0.1004	0.2333	0.0137	0.0908	0.2935	0.2722	0.2621	0.5581	0.2089	0.4088	0.0108	0.1783
No log	5.0	65	10.3979	0.1323	0.2611	0.1276	0.5042	0.1206	0.2902	0.0227	0.1687	0.376	0.5306	0.3309	0.6456	0.2317	0.4215	0.0328	0.3304
No log	6.0	78	10.0574	0.183	0.3502	0.1716	0.5487	0.1395	0.4027	0.0274	0.1741	0.4055	0.6528	0.3589	0.6032	0.2429	0.4392	0.123	0.3717
No log	7.0	91	9.9984	0.2236	0.4243	0.2019	0.5683	0.1733	0.4474	0.0493	0.2187	0.419	0.6167	0.3656	0.7052	0.2473	0.4336	0.2	0.4043
No log	8.0	104	9.6693	0.2453	0.4586	0.2238	0.6032	0.268	0.2727	0.0481	0.2637	0.4929	0.6444	0.4468	0.7573	0.1751	0.4401	0.3156	0.5457
No log	9.0	117	8.7372	0.3333	0.5783	0.3575	0.6196	0.3265	0.3584	0.0581	0.2821	0.5441	0.6639	0.5068	0.7573	0.2562	0.4731	0.4103	0.6152
No log	10.0	130	8.8186	0.2482	0.4278	0.2468	0.5817	0.2468	0.3644	0.0468	0.2827	0.558	0.6028	0.5278	0.7593	0.2212	0.4639	0.2753	0.6522
No log	11.0	143	8.5718	0.2821	0.501	0.2732	0.6277	0.2546	0.4392	0.057	0.2703	0.5479	0.6278	0.5104	0.7843	0.2289	0.4762	0.3353	0.6196
No log	12.0	156	8.7045	0.3346	0.5686	0.3482	0.6173	0.3073	0.394	0.0506	0.2959	0.5566	0.6472	0.5207	0.7589	0.2881	0.4806	0.3811	0.6326
No log	13.0	169	8.3602	0.3594	0.592	0.3995	0.6538	0.3268	0.4867	0.0641	0.2966	0.5952	0.675	0.5618	0.7895	0.3103	0.5013	0.4086	0.6891
No log	14.0	182	8.4438	0.3271	0.5673	0.3338	0.5406	0.3084	0.4213	0.0661	0.28	0.5717	0.6528	0.535	0.7927	0.2647	0.4848	0.3895	0.6587
No log	15.0	195	8.2520	0.335	0.5597	0.3426	0.6311	0.3032	0.5211	0.0562	0.3073	0.58	0.65	0.5397	0.8435	0.2978	0.4948	0.3722	0.6652
No log	16.0	208	8.1700	0.3065	0.5151	0.308	0.6639	0.2903	0.4197	0.0568	0.3025	0.5802	0.7167	0.5423	0.7815	0.2632	0.4973	0.3498	0.663
No log	17.0	221	8.0011	0.3557	0.5819	0.3839	0.6318	0.3133	0.5577	0.061	0.3042	0.5911	0.6333	0.5585	0.796	0.2978	0.5084	0.4136	0.6739
No log	18.0	234	7.9459	0.3566	0.5752	0.3869	0.6684	0.3313	0.5498	0.0578	0.3123	0.614	0.7139	0.5762	0.8351	0.2848	0.5171	0.4284	0.7109
No log	19.0	247	7.9807	0.3556	0.5988	0.3768	0.6516	0.323	0.5591	0.0581	0.3112	0.5848	0.6694	0.5468	0.8177	0.2875	0.5088	0.4237	0.6609
No log	20.0	260	8.2510	0.3793	0.6172	0.3966	0.648	0.3517	0.527	0.0651	0.3108	0.5999	0.7139	0.5596	0.8294	0.2839	0.4846	0.4746	0.7152
No log	21.0	273	8.0173	0.3769	0.6308	0.3901	0.6581	0.348	0.5505	0.0537	0.3094	0.5865	0.7028	0.5526	0.7629	0.2872	0.4992	0.4666	0.6739
No log	22.0	286	8.1768	0.3486	0.5852	0.3254	0.6257	0.3176	0.5186	0.0645	0.3031	0.589	0.6611	0.5523	0.8109	0.2765	0.491	0.4207	0.687
No log	23.0	299	8.1836	0.3417	0.5674	0.3576	0.6202	0.3257	0.5427	0.0599	0.3157	0.5813	0.6639	0.5458	0.7895	0.2469	0.4931	0.4365	0.6696
No log	24.0	312	8.1504	0.3545	0.5978	0.3692	0.6435	0.3284	0.5889	0.058	0.3149	0.5825	0.6639	0.5447	0.8093	0.2753	0.491	0.4338	0.6739
No log	25.0	325	7.8980	0.3769	0.6108	0.4214	0.6247	0.3486	0.5719	0.0639	0.3131	0.5976	0.6583	0.5631	0.8085	0.2873	0.5017	0.4666	0.6935
No log	26.0	338	7.9985	0.3495	0.5762	0.3213	0.6342	0.3189	0.5759	0.0515	0.3032	0.6023	0.6583	0.5678	0.8294	0.2508	0.5046	0.4483	0.7
No log	27.0	351	7.8948	0.3738	0.6191	0.4456	0.6332	0.3502	0.5408	0.0643	0.3083	0.5954	0.6389	0.5632	0.8	0.2775	0.5104	0.4701	0.6804
No log	28.0	364	7.9396	0.3658	0.6121	0.3443	0.5331	0.3397	0.5885	0.0645	0.3105	0.5972	0.6444	0.5625	0.821	0.2744	0.5052	0.4571	0.6891
No log	29.0	377	7.9732	0.3522	0.5863	0.3728	0.5942	0.3264	0.5782	0.0627	0.3028	0.5956	0.65	0.5609	0.8177	0.2709	0.5086	0.4335	0.6826
No log	30.0	390	8.1223	0.3404	0.5936	0.3081	0.6316	0.3191	0.5283	0.05	0.2941	0.5913	0.6861	0.5534	0.8153	0.2474	0.4935	0.4334	0.6891
No log	31.0	403	7.9385	0.3279	0.5698	0.2987	0.5828	0.3084	0.5489	0.0579	0.2968	0.5883	0.65	0.5511	0.8294	0.2384	0.4962	0.4174	0.6804
No log	32.0	416	7.9805	0.3328	0.5745	0.3031	0.5977	0.3111	0.5616	0.0594	0.2946	0.5881	0.6667	0.5528	0.796	0.24	0.5023	0.4257	0.6739
No log	33.0	429	7.9656	0.3372	0.5745	0.3294	0.5334	0.3199	0.5599	0.0503	0.3032	0.5959	0.6639	0.5629	0.7911	0.2371	0.5092	0.4374	0.6826
No log	34.0	442	7.7492	0.3418	0.5842	0.3344	0.6367	0.3147	0.606	0.059	0.3082	0.5934	0.6667	0.5576	0.8101	0.2523	0.5106	0.4313	0.6761
No log	35.0	455	7.9115	0.3336	0.5782	0.3039	0.6315	0.3075	0.6255	0.0594	0.3012	0.5928	0.6639	0.5537	0.8435	0.2453	0.5073	0.4219	0.6783
No log	36.0	468	7.8646	0.3414	0.5791	0.3556	0.6197	0.3159	0.6261	0.0547	0.301	0.5994	0.6639	0.5627	0.8327	0.2447	0.5119	0.4381	0.687
No log	37.0	481	7.9918	0.3504	0.5905	0.3812	0.6226	0.3246	0.6256	0.0507	0.2982	0.5922	0.65	0.5576	0.8093	0.2403	0.5104	0.4604	0.6739
No log	38.0	494	7.9649	0.3396	0.5715	0.3579	0.615	0.317	0.584	0.0589	0.3046	0.6018	0.6611	0.5628	0.8585	0.2443	0.5079	0.4348	0.6957
13.3406	39.0	507	7.9482	0.328	0.5557	0.3271	0.5964	0.3056	0.5675	0.059	0.2983	0.5949	0.6444	0.5575	0.8444	0.2501	0.5115	0.4058	0.6783
13.3406	40.0	520	7.9461	0.3255	0.5618	0.3063	0.5913	0.305	0.5877	0.058	0.299	0.5888	0.6583	0.5516	0.8202	0.2427	0.5038	0.4084	0.6739
13.3406	41.0	533	7.9860	0.3074	0.5349	0.2944	0.6136	0.2866	0.5951	0.0507	0.2954	0.5946	0.6611	0.5582	0.821	0.2397	0.5067	0.3751	0.6826
13.3406	42.0	546	7.9835	0.3154	0.5395	0.3094	0.6094	0.2927	0.6031	0.0584	0.2971	0.5951	0.6583	0.559	0.8202	0.2425	0.5075	0.3883	0.6826
13.3406	43.0	559	8.0506	0.3225	0.5535	0.3255	0.5259	0.3027	0.6104	0.0584	0.296	0.5891	0.6444	0.5528	0.8218	0.2359	0.5021	0.409	0.6761
13.3406	44.0	572	8.0112	0.3321	0.5729	0.3363	0.6113	0.3089	0.5915	0.0516	0.2966	0.5892	0.6611	0.552	0.8194	0.2409	0.5023	0.4233	0.6761
13.3406	45.0	585	7.9716	0.3259	0.5551	0.3343	0.6304	0.3031	0.6173	0.0592	0.2969	0.5952	0.6583	0.559	0.821	0.2432	0.5056	0.4086	0.6848
13.3406	46.0	598	7.9923	0.3291	0.5642	0.3335	0.6106	0.3043	0.6146	0.0589	0.2943	0.5954	0.6806	0.5563	0.8343	0.2424	0.5061	0.4158	0.6848
13.3406	47.0	611	8.0093	0.3266	0.5565	0.3375	0.6017	0.3035	0.6104	0.0512	0.2999	0.5948	0.6583	0.5561	0.8452	0.2408	0.5048	0.4124	0.6848
13.3406	48.0	624	7.9778	0.3309	0.5626	0.3342	0.603	0.3062	0.6507	0.0588	0.2981	0.5993	0.675	0.5602	0.8444	0.2396	0.5029	0.4222	0.6957
13.3406	49.0	637	7.9963	0.3285	0.5612	0.3368	0.6042	0.3051	0.6123	0.0589	0.3028	0.5981	0.6583	0.5598	0.8468	0.2432	0.5071	0.4137	0.6891
13.3406	50.0	650	8.0013	0.3254	0.5528	0.3324	0.6086	0.3016	0.5975	0.0588	0.3009	0.5992	0.675	0.5598	0.8468	0.2404	0.505	0.4104	0.6935

Framework versions

Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: rt_detr
License: apache-2.0
Created: Mar 3, 2026
Updated: Mar 3, 2026