lw-detr-medium-tray-detection

This model is a fine-tuned version of AnnaZhang/lwdetr_medium_60e_coco on the nielsr/tray-cart-detection dataset. It achieves the following results on the evaluation set:

Loss: 8.6378
Map: 0.4419
Map 50: 0.7634
Map 75: 0.4321
Map Small: 0.6228
Map Medium: 0.4287
Map Large: 0.5024
Mar 1: 0.0566
Mar 10: 0.3224
Mar 100: 0.5434
Mar Small: 0.6238
Mar Medium: 0.5245
Mar Large: 0.6596
Map Per Class: -1.0
Mar 100 Per Class: -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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
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: linear
num_epochs: 300.0

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 Per Class	Mar 100 Per Class
8.0517	1.0	25	7.5501	0.1695	0.4286	0.1112	0.2996	0.1636	0.2694	0.0337	0.1580	0.3180	0.4286	0.2886	0.4978	-1.0	-1.0
5.7334	2.0	50	8.1382	0.2317	0.4736	0.2073	0.4546	0.2245	0.3582	0.0351	0.1654	0.3319	0.5452	0.2917	0.5543	-1.0	-1.0
5.0764	3.0	75	8.3725	0.3248	0.6463	0.2534	0.5197	0.3029	0.4731	0.0490	0.2409	0.4048	0.5714	0.3706	0.6008	-1.0	-1.0
4.7140	4.0	100	8.2542	0.3632	0.6653	0.3197	0.5918	0.3403	0.4378	0.0506	0.2716	0.4429	0.6024	0.4128	0.6121	-1.0	-1.0
4.4517	5.0	125	7.5693	0.3250	0.6562	0.2605	0.5206	0.2965	0.4704	0.0469	0.2654	0.4334	0.5952	0.4009	0.6208	-1.0	-1.0
4.1539	6.0	150	8.6084	0.3616	0.6758	0.2926	0.5325	0.3458	0.4462	0.0463	0.2752	0.4371	0.5643	0.4189	0.5285	-1.0	-1.0
4.0950	7.0	175	8.7664	0.3819	0.7026	0.3594	0.5704	0.3568	0.5346	0.0525	0.2822	0.4631	0.5762	0.4350	0.6359	-1.0	-1.0
3.9274	8.0	200	8.8551	0.4033	0.7251	0.3662	0.5597	0.3810	0.5447	0.0559	0.2977	0.4828	0.5881	0.4598	0.6231	-1.0	-1.0
3.7842	9.0	225	8.5001	0.4101	0.7291	0.4116	0.6180	0.3954	0.5028	0.0554	0.2933	0.4974	0.6524	0.4695	0.6552	-1.0	-1.0
3.6846	10.0	250	8.5214	0.3728	0.6703	0.3901	0.5360	0.3613	0.4350	0.0442	0.2828	0.4719	0.6381	0.4574	0.5248	-1.0	-1.0
3.8286	11.0	275	8.6306	0.4163	0.7228	0.3890	0.5807	0.4072	0.4799	0.0543	0.3032	0.4942	0.5929	0.4823	0.5604	-1.0	-1.0
3.6285	12.0	300	8.4890	0.4145	0.7332	0.3865	0.5893	0.3916	0.5381	0.0531	0.3092	0.4921	0.6095	0.4691	0.6255	-1.0	-1.0
3.3944	13.0	325	8.6758	0.4081	0.7184	0.3577	0.5308	0.4049	0.4576	0.0549	0.2953	0.4959	0.5524	0.4857	0.5604	-1.0	-1.0
3.4186	14.0	350	8.1625	0.4200	0.7018	0.4636	0.6063	0.4090	0.4809	0.0548	0.3053	0.5117	0.6595	0.4984	0.5648	-1.0	-1.0
3.3058	15.0	375	8.6068	0.4010	0.7081	0.3977	0.6034	0.3958	0.4440	0.0569	0.3131	0.5060	0.6262	0.4838	0.6347	-1.0	-1.0
3.2623	16.0	400	8.7036	0.3967	0.6959	0.3976	0.5572	0.3982	0.4124	0.0509	0.2900	0.4903	0.5738	0.4846	0.5147	-1.0	-1.0
3.2304	17.0	425	8.6188	0.4032	0.7127	0.3661	0.5780	0.3917	0.4922	0.0487	0.2946	0.5015	0.5833	0.4825	0.6202	-1.0	-1.0
3.1675	18.0	450	8.5400	0.4203	0.7311	0.4205	0.5791	0.3996	0.5425	0.0520	0.3126	0.5058	0.5905	0.4845	0.6413	-1.0	-1.0
3.0644	19.0	475	8.9040	0.4293	0.7462	0.4458	0.6098	0.4091	0.5483	0.0611	0.3135	0.5219	0.6286	0.5031	0.6297	-1.0	-1.0
3.1360	20.0	500	8.5330	0.4412	0.7499	0.4289	0.6234	0.4214	0.5239	0.0550	0.3362	0.5446	0.6548	0.5195	0.6996	-1.0	-1.0
3.0481	21.0	525	8.5831	0.4414	0.7512	0.4634	0.6079	0.4146	0.5592	0.0533	0.3086	0.5313	0.6167	0.5059	0.6946	-1.0	-1.0
3.0654	22.0	550	8.6378	0.4419	0.7634	0.4321	0.6228	0.4287	0.5024	0.0566	0.3224	0.5434	0.6238	0.5245	0.6596	-1.0	-1.0
2.9182	23.0	575	8.7346	0.3946	0.6975	0.3866	0.4916	0.3938	0.4398	0.0477	0.2899	0.4900	0.5024	0.4787	0.5803	-1.0	-1.0
2.8294	24.0	600	8.7305	0.3981	0.7152	0.3830	0.5847	0.3894	0.4705	0.0550	0.3006	0.4947	0.5857	0.4793	0.5807	-1.0	-1.0
2.8585	25.0	625	8.9077	0.3927	0.6933	0.3922	0.5287	0.3899	0.4685	0.0464	0.2964	0.4862	0.5381	0.4636	0.6402	-1.0	-1.0
2.6991	26.0	650	8.4823	0.4155	0.7124	0.4351	0.6226	0.4020	0.4909	0.0572	0.3089	0.5125	0.6286	0.4933	0.6180	-1.0	-1.0
2.8258	27.0	675	8.7625	0.4052	0.7436	0.3913	0.5667	0.3859	0.4814	0.0545	0.3006	0.4930	0.5786	0.4715	0.6240	-1.0	-1.0
2.7473	28.0	700	8.6783	0.4247	0.7304	0.4309	0.6396	0.4069	0.4929	0.0611	0.3044	0.5159	0.6405	0.5027	0.5725	-1.0	-1.0
2.7875	29.0	725	8.6406	0.4045	0.7111	0.4057	0.6242	0.4000	0.4394	0.0540	0.3005	0.5018	0.6500	0.4852	0.5746	-1.0	-1.0
2.6226	30.0	750	8.9299	0.4019	0.7086	0.4041	0.6233	0.3924	0.4977	0.0532	0.2996	0.4843	0.6238	0.4586	0.6271	-1.0	-1.0
2.5618	31.0	775	8.8131	0.4132	0.7018	0.4289	0.6137	0.3936	0.5068	0.0552	0.3073	0.5017	0.6214	0.4854	0.5825	-1.0	-1.0
2.5460	32.0	800	9.0914	0.3757	0.6862	0.3412	0.6066	0.3763	0.4124	0.0576	0.2936	0.4861	0.6143	0.4638	0.6069	-1.0	-1.0

Framework versions

Transformers 5.3.0.dev0
Pytorch 2.10.0+cu128
Datasets 4.8.2
Tokenizers 0.22.2

nielsr/lw-detr-medium-tray-detection

lw-detr-medium-tray-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model