rtdetr-tray-cart-baseline-plus-20260303-205414

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: 8.0668
• Map: 0.3239
• Map 50: 0.5298
• Map 75: 0.3622
• Map Small: 0.6092
• Map Medium: 0.3141
• Map Large: 0.4872
• Mar 1: 0.0678
• Mar 10: 0.3077
• Mar 100: 0.6062
• Mar Small: 0.6444
• Mar Medium: 0.5753
• Mar Large: 0.8
• Map Tray: 0.2839
• Mar 100 Tray: 0.519
• Map Cart: 0.3639
• 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 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: 32
• 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	29.1011	0.0002	0.0006	0.0001	0.0004	0.0004	0.0007	0.0	0.003	0.0265	0.0833	0.0198	0.05	0.0001	0.0073	0.0003	0.0457
No log	2.0	26	24.8957	0.0065	0.0206	0.0039	0.0018	0.0112	0.0128	0.0043	0.0137	0.1362	0.0417	0.1328	0.2214	0.0102	0.1029	0.0027	0.1696
No log	3.0	39	26.9171	0.0201	0.0436	0.0182	0.0287	0.025	0.0127	0.001	0.0258	0.1127	0.0278	0.1044	0.2016	0.0383	0.1384	0.002	0.087
No log	4.0	52	12.3506	0.0876	0.199	0.0624	0.474	0.0843	0.133	0.0235	0.1374	0.3225	0.5944	0.2909	0.3915	0.119	0.3428	0.0562	0.3022
No log	5.0	65	10.8916	0.1656	0.3389	0.1475	0.6146	0.1369	0.3784	0.0576	0.1928	0.4289	0.6917	0.377	0.671	0.2263	0.4599	0.105	0.3978
No log	6.0	78	10.2800	0.2101	0.3909	0.2076	0.6067	0.185	0.4117	0.0608	0.2428	0.4669	0.6444	0.4292	0.6524	0.1929	0.4576	0.2272	0.4761
No log	7.0	91	10.5153	0.1662	0.3545	0.1159	0.5204	0.1758	0.3488	0.0463	0.2319	0.4474	0.6083	0.4219	0.5597	0.1401	0.3687	0.1923	0.5261
No log	8.0	104	10.3591	0.2243	0.4047	0.2082	0.6062	0.233	0.4286	0.0553	0.2359	0.4825	0.6222	0.4525	0.648	0.1757	0.3889	0.2729	0.5761
No log	9.0	117	9.3169	0.276	0.4992	0.242	0.5967	0.2409	0.5647	0.0608	0.2795	0.5201	0.6167	0.4841	0.7359	0.226	0.4597	0.3261	0.5804
No log	10.0	130	9.1134	0.3103	0.5383	0.3182	0.5444	0.2797	0.5383	0.0611	0.295	0.5619	0.6278	0.5294	0.7673	0.2594	0.4651	0.3613	0.6587
No log	11.0	143	8.9282	0.314	0.5423	0.3023	0.5999	0.293	0.4874	0.0689	0.2882	0.5566	0.6028	0.5217	0.7673	0.2658	0.4762	0.3621	0.637
No log	12.0	156	8.9086	0.3107	0.5291	0.3033	0.5632	0.3031	0.4691	0.0574	0.311	0.5915	0.6306	0.559	0.8073	0.2794	0.4939	0.3419	0.6891
No log	13.0	169	8.5357	0.3575	0.5824	0.3955	0.5702	0.3315	0.52	0.0656	0.3147	0.5905	0.625	0.556	0.821	0.3226	0.5027	0.3924	0.6783
No log	14.0	182	8.8271	0.3277	0.5597	0.3201	0.6218	0.3309	0.3784	0.0553	0.3086	0.5806	0.6722	0.5585	0.6875	0.2895	0.4916	0.3659	0.6696
No log	15.0	195	9.2496	0.3241	0.5622	0.327	0.5495	0.3189	0.4744	0.0539	0.2921	0.5659	0.6056	0.5363	0.7536	0.2845	0.4992	0.3637	0.6326
No log	16.0	208	8.5119	0.3067	0.525	0.3106	0.6132	0.3024	0.4534	0.0542	0.2986	0.5831	0.6722	0.5565	0.7177	0.266	0.4944	0.3474	0.6717
No log	17.0	221	8.7720	0.3277	0.5564	0.3283	0.5812	0.3185	0.4697	0.0657	0.2967	0.5685	0.6333	0.5304	0.7992	0.2991	0.4783	0.3563	0.6587
No log	18.0	234	8.3029	0.3555	0.5739	0.3838	0.6214	0.3449	0.4697	0.0645	0.3218	0.6119	0.6667	0.5786	0.8194	0.3127	0.515	0.3982	0.7087
No log	19.0	247	8.4055	0.3121	0.5398	0.2995	0.5808	0.2968	0.4635	0.057	0.3003	0.5699	0.6389	0.5379	0.7565	0.2679	0.5071	0.3562	0.6326
No log	20.0	260	8.5116	0.3402	0.5721	0.3378	0.6327	0.3317	0.5012	0.0574	0.294	0.5892	0.6361	0.5596	0.7815	0.2912	0.5067	0.3892	0.6717
No log	21.0	273	8.3019	0.3193	0.5505	0.3138	0.6234	0.3151	0.384	0.0634	0.2939	0.574	0.6389	0.5479	0.7073	0.2931	0.5154	0.3454	0.6326
No log	22.0	286	8.1803	0.3051	0.5156	0.3132	0.5563	0.2954	0.4624	0.0532	0.3149	0.5978	0.6167	0.5715	0.7633	0.2804	0.5109	0.3299	0.6848
No log	23.0	299	8.0668	0.3239	0.5298	0.3622	0.6092	0.3141	0.4872	0.0678	0.3077	0.6062	0.6444	0.5753	0.8	0.2839	0.519	0.3639	0.6935

Framework versions

• Transformers 5.2.0
• Pytorch 2.10.0+cu128
• Datasets 4.6.1
• Tokenizers 0.22.2