rtdetr-tray-cart-paper-strict-20260303-213437

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: 7.7488
Map: 0.5123
Map 50: 0.8312
Map 75: 0.5756
Map Small: 0.5427
Map Medium: 0.4995
Map Large: 0.6368
Mar 1: 0.0606
Mar 10: 0.352
Mar 100: 0.6281
Mar Small: 0.6286
Mar Medium: 0.6069
Mar Large: 0.8005
Map Tray: 0.4236
Mar 100 Tray: 0.5353
Map Cart: 0.601
Mar 100 Cart: 0.721

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
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: 3
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	7	24.4270	0.0027	0.0083	0.0011	0.0013	0.006	0.0019	0.0002	0.013	0.1039	0.1714	0.1015	0.0917	0.0019	0.0416	0.0035	0.1661
No log	2.0	14	24.7677	0.0099	0.0318	0.0045	0.0115	0.0213	0.0073	0.006	0.0213	0.1215	0.2429	0.1033	0.2115	0.0178	0.1302	0.002	0.1129
No log	3.0	21	29.9358	0.0257	0.0601	0.0183	0.0389	0.0472	0.021	0.0014	0.0552	0.2108	0.369	0.2031	0.207	0.0434	0.186	0.008	0.2355
No log	4.0	28	18.1668	0.071	0.1455	0.0575	0.1004	0.0849	0.1491	0.0077	0.0838	0.2631	0.419	0.2405	0.3733	0.1304	0.2698	0.0116	0.2565
No log	5.0	35	14.1994	0.1355	0.2703	0.1304	0.3838	0.141	0.1668	0.0314	0.1476	0.383	0.5214	0.3628	0.4838	0.1791	0.3596	0.0918	0.4065
No log	6.0	42	13.0165	0.1973	0.3781	0.1808	0.5304	0.1727	0.386	0.0348	0.1737	0.4277	0.5905	0.3991	0.5893	0.2806	0.4151	0.114	0.4403
No log	7.0	49	10.3521	0.2974	0.566	0.2797	0.5651	0.2726	0.4396	0.0443	0.2392	0.4844	0.65	0.4563	0.6416	0.3526	0.4673	0.2421	0.5016
No log	8.0	56	9.8570	0.3216	0.627	0.3042	0.5535	0.2954	0.4889	0.0467	0.2548	0.4931	0.619	0.4628	0.6819	0.3664	0.4716	0.2768	0.5145
No log	9.0	63	9.2473	0.3408	0.6535	0.3297	0.5577	0.3142	0.5401	0.0443	0.2587	0.5066	0.6381	0.4771	0.6861	0.383	0.4921	0.2987	0.521
No log	10.0	70	9.2426	0.3651	0.6789	0.353	0.5311	0.3415	0.5686	0.0461	0.281	0.5382	0.5976	0.5117	0.7234	0.3606	0.4925	0.3697	0.5839
No log	11.0	77	8.8889	0.3784	0.6815	0.3876	0.5388	0.3518	0.5622	0.0566	0.2766	0.5586	0.6048	0.5411	0.6839	0.3568	0.4931	0.3999	0.6242
No log	12.0	84	8.8623	0.4275	0.7368	0.4325	0.542	0.4082	0.5757	0.0587	0.3091	0.5848	0.6238	0.5612	0.7588	0.3841	0.4953	0.471	0.6742
No log	13.0	91	8.6457	0.4439	0.75	0.4639	0.5296	0.4249	0.5931	0.0543	0.3301	0.5888	0.6333	0.563	0.7776	0.3978	0.5116	0.4901	0.6661
No log	14.0	98	8.6612	0.4525	0.739	0.5128	0.5498	0.4392	0.5629	0.0546	0.3287	0.6136	0.681	0.5929	0.7557	0.3595	0.5239	0.5456	0.7032
No log	15.0	105	8.4938	0.4608	0.7716	0.5252	0.5199	0.4482	0.5838	0.0548	0.3415	0.6005	0.5976	0.5781	0.784	0.3778	0.5091	0.5439	0.6919
No log	16.0	112	8.3763	0.4418	0.7365	0.5038	0.5426	0.4322	0.573	0.057	0.3187	0.5927	0.6143	0.574	0.7381	0.3706	0.508	0.513	0.6774
No log	17.0	119	8.3364	0.4657	0.7701	0.5345	0.5525	0.4544	0.6064	0.0592	0.3308	0.6154	0.6048	0.5916	0.8126	0.3766	0.513	0.5548	0.7177
No log	18.0	126	8.1705	0.4663	0.7863	0.5371	0.5667	0.4535	0.6102	0.0571	0.3357	0.6211	0.619	0.5965	0.82	0.3844	0.5083	0.5482	0.7339
No log	19.0	133	8.2220	0.4546	0.7768	0.4899	0.5599	0.4382	0.6214	0.0556	0.3399	0.6119	0.5952	0.5878	0.8137	0.378	0.5093	0.5312	0.7145
No log	20.0	140	8.1171	0.4784	0.7886	0.5462	0.5823	0.4604	0.6401	0.0572	0.3449	0.6196	0.6262	0.5979	0.7937	0.3769	0.5183	0.5798	0.721
No log	21.0	147	8.0277	0.4701	0.7813	0.5438	0.5729	0.4563	0.6339	0.0564	0.3424	0.6174	0.6167	0.5964	0.7873	0.3875	0.5202	0.5527	0.7145
No log	22.0	154	7.9652	0.4803	0.7996	0.5529	0.5348	0.4674	0.6099	0.0586	0.3424	0.6139	0.6048	0.5945	0.7764	0.3904	0.523	0.5701	0.7048
No log	23.0	161	8.0631	0.4822	0.8089	0.562	0.5207	0.4694	0.5962	0.0531	0.3444	0.6078	0.5881	0.5869	0.784	0.3878	0.5188	0.5767	0.6968
No log	24.0	168	7.9490	0.4739	0.7841	0.5487	0.5397	0.4637	0.6113	0.0543	0.3427	0.625	0.6357	0.604	0.7911	0.3868	0.5226	0.561	0.7274
No log	25.0	175	7.9127	0.4929	0.8043	0.5536	0.572	0.4826	0.611	0.0551	0.3483	0.6328	0.6286	0.6131	0.7972	0.4037	0.527	0.5822	0.7387
No log	26.0	182	7.8650	0.5027	0.8151	0.5898	0.5361	0.4904	0.6398	0.0542	0.3505	0.6304	0.6071	0.6097	0.8092	0.4102	0.5286	0.5952	0.7323
No log	27.0	189	7.8510	0.5112	0.8266	0.5859	0.5593	0.4957	0.6725	0.0644	0.3574	0.627	0.6238	0.6056	0.8031	0.4152	0.5218	0.6072	0.7323
No log	28.0	196	7.8427	0.5127	0.8328	0.575	0.5613	0.4995	0.6797	0.0654	0.3494	0.6232	0.6262	0.6019	0.7952	0.4218	0.5271	0.6035	0.7194
No log	29.0	203	7.9177	0.498	0.8326	0.5616	0.5249	0.4813	0.6596	0.0602	0.3455	0.6102	0.6071	0.5865	0.8013	0.4098	0.5188	0.5862	0.7016
No log	30.0	210	7.8359	0.4855	0.8076	0.5454	0.5357	0.4705	0.6136	0.0608	0.3465	0.6192	0.6214	0.5961	0.8047	0.407	0.5255	0.564	0.7129
No log	31.0	217	7.8632	0.4949	0.8205	0.5677	0.5348	0.4818	0.6333	0.0595	0.3435	0.6245	0.6214	0.6014	0.8118	0.4076	0.5313	0.5822	0.7177
No log	32.0	224	7.8101	0.5021	0.8255	0.5956	0.5453	0.4885	0.6335	0.0608	0.3477	0.6295	0.6262	0.6085	0.8016	0.4052	0.5316	0.5991	0.7274
No log	33.0	231	7.8108	0.4881	0.8102	0.5664	0.5285	0.4709	0.6988	0.06	0.3499	0.6268	0.6167	0.6043	0.8118	0.3946	0.5278	0.5817	0.7258
No log	34.0	238	7.7570	0.4896	0.8106	0.5771	0.5609	0.4781	0.5904	0.0634	0.3366	0.6186	0.6262	0.5947	0.8069	0.3995	0.5307	0.5797	0.7065
No log	35.0	245	7.7686	0.5069	0.8261	0.6068	0.5533	0.4932	0.6384	0.0552	0.3475	0.6263	0.6357	0.6036	0.8069	0.4152	0.53	0.5987	0.7226
No log	36.0	252	7.7488	0.5123	0.8312	0.5756	0.5427	0.4995	0.6368	0.0606	0.352	0.6281	0.6286	0.6069	0.8005	0.4236	0.5353	0.601	0.721

Framework versions

Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2

nielsr/rtdetr-tray-cart-paper-strict-20260303-213437

rtdetr-tray-cart-paper-strict-20260303-213437

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种