detr_finetuned_kitti_mots

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5468
Map: 0.5754
Map 50: 0.8688
Map 75: 0.625
Map Small: 0.3516
Map Medium: 0.5857
Map Large: 0.783
Mar 1: 0.1695
Mar 10: 0.6195
Mar 100: 0.6862
Mar Small: 0.5362
Mar Medium: 0.6977
Mar Large: 0.8372
Map Car: 0.6938
Mar 100 Car: 0.769
Map Pedestrian: 0.4569
Mar 100 Pedestrian: 0.6034

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 50

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 Car	Mar 100 Car	Map Pedestrian	Mar 100 Pedestrian
1.5904	1.0	743	1.2276	0.1644	0.3572	0.1419	0.0253	0.1267	0.3984	0.08	0.2466	0.3977	0.1364	0.4022	0.7009	0.277	0.4472	0.0518	0.3482
1.3176	2.0	1486	1.1301	0.2261	0.5107	0.1714	0.0479	0.1929	0.5	0.1024	0.3066	0.4201	0.201	0.4196	0.6825	0.3095	0.4709	0.1426	0.3694
1.1187	3.0	2229	1.0565	0.2699	0.5705	0.2147	0.0416	0.2365	0.5833	0.1142	0.332	0.4328	0.1875	0.4434	0.7003	0.3674	0.4924	0.1725	0.3733
1.0835	4.0	2972	1.0326	0.2851	0.5903	0.2363	0.0572	0.2557	0.6048	0.116	0.3481	0.4474	0.2339	0.4447	0.7149	0.3781	0.5115	0.1921	0.3832
1.0681	5.0	3715	1.0095	0.3017	0.5998	0.2728	0.0538	0.2878	0.6051	0.1205	0.3625	0.4664	0.2237	0.4787	0.7298	0.4003	0.5288	0.2031	0.404
1.042	6.0	4458	1.0315	0.271	0.6062	0.1981	0.0628	0.2597	0.5656	0.1073	0.3465	0.4393	0.2258	0.4469	0.6762	0.3601	0.4929	0.182	0.3857
1.0051	7.0	5201	0.9796	0.2875	0.6189	0.2331	0.0695	0.2789	0.5719	0.1122	0.367	0.473	0.2403	0.4912	0.7076	0.3936	0.5423	0.1814	0.4036
0.9798	8.0	5944	0.9567	0.3178	0.6257	0.2915	0.0823	0.3041	0.6211	0.124	0.379	0.485	0.2552	0.5059	0.7105	0.4214	0.5633	0.2142	0.4068
0.9814	9.0	6687	1.0122	0.2799	0.579	0.239	0.0493	0.2585	0.5948	0.1124	0.3488	0.4607	0.2195	0.4725	0.7226	0.3877	0.5279	0.1721	0.3934
1.0057	10.0	7430	0.9464	0.3156	0.6365	0.2797	0.081	0.3083	0.6042	0.1201	0.3814	0.4929	0.275	0.5011	0.736	0.4326	0.5579	0.1986	0.428
0.9267	11.0	8173	0.9137	0.3364	0.6634	0.2955	0.118	0.326	0.6291	0.1276	0.3995	0.5073	0.3059	0.5147	0.7301	0.4369	0.5728	0.2359	0.4417
0.8938	12.0	8916	0.8808	0.3622	0.6811	0.3348	0.1147	0.3497	0.6639	0.1358	0.4187	0.5212	0.2956	0.536	0.7572	0.4784	0.5986	0.246	0.4438
0.8575	13.0	9659	0.8632	0.3614	0.6888	0.34	0.1005	0.3647	0.6565	0.1305	0.4254	0.5258	0.2989	0.5449	0.7523	0.4629	0.5893	0.2599	0.4624
0.8442	14.0	10402	0.8544	0.3746	0.6922	0.3628	0.1129	0.3759	0.6419	0.1356	0.4284	0.5352	0.3325	0.5545	0.7302	0.4878	0.6007	0.2614	0.4696
0.8213	15.0	11145	0.8441	0.3792	0.7034	0.3561	0.1321	0.3742	0.6534	0.1365	0.4317	0.5378	0.324	0.56	0.7363	0.5039	0.6128	0.2544	0.4628
0.8115	16.0	11888	0.8210	0.3888	0.709	0.3807	0.128	0.392	0.6675	0.1399	0.4413	0.5468	0.3479	0.5616	0.7491	0.5037	0.6212	0.2739	0.4723
0.8093	17.0	12631	0.8127	0.385	0.7092	0.3751	0.1383	0.3858	0.6626	0.1381	0.4399	0.5493	0.3673	0.556	0.7529	0.5141	0.6257	0.2559	0.4729
0.7893	18.0	13374	0.8204	0.3901	0.7194	0.3675	0.1295	0.3842	0.6851	0.1378	0.4404	0.5416	0.3486	0.5481	0.7602	0.5048	0.6092	0.2755	0.474
0.7459	19.0	14117	0.7880	0.4058	0.729	0.4085	0.1582	0.4043	0.6709	0.1407	0.46	0.5627	0.3686	0.5794	0.7528	0.53	0.6421	0.2816	0.4832
0.7383	20.0	14860	0.7477	0.4264	0.7551	0.4264	0.1563	0.4328	0.7092	0.1441	0.48	0.5888	0.3989	0.6004	0.788	0.538	0.6577	0.3149	0.5199
0.7207	21.0	15603	0.7688	0.4188	0.7599	0.4087	0.1591	0.419	0.7057	0.1419	0.4691	0.5641	0.3832	0.5686	0.772	0.5306	0.6361	0.307	0.492
0.7127	22.0	16346	0.7450	0.4368	0.7622	0.4379	0.1733	0.4368	0.7099	0.1483	0.4828	0.5834	0.3873	0.5959	0.7875	0.5601	0.6532	0.3136	0.5136
0.698	23.0	17089	0.7429	0.4401	0.7626	0.4515	0.1739	0.4456	0.7194	0.1498	0.4841	0.5901	0.3878	0.6079	0.7878	0.5536	0.6524	0.3266	0.5278
0.6836	24.0	17832	0.7642	0.422	0.7565	0.4217	0.154	0.428	0.7066	0.1433	0.47	0.5743	0.3941	0.5791	0.7801	0.5423	0.6404	0.3017	0.5081
0.6684	25.0	18575	0.7016	0.4599	0.7889	0.4717	0.1873	0.4706	0.7334	0.1497	0.5093	0.6116	0.4221	0.6271	0.8006	0.5767	0.6837	0.3431	0.5395
0.6471	26.0	19318	0.6890	0.4724	0.791	0.4869	0.2053	0.4825	0.7304	0.1552	0.516	0.6068	0.4245	0.6179	0.7994	0.6004	0.6895	0.3443	0.5241
0.6259	27.0	20061	0.6788	0.4726	0.7816	0.4914	0.2182	0.481	0.7202	0.1555	0.5212	0.6199	0.4374	0.6387	0.7919	0.6137	0.7058	0.3316	0.5339
0.6038	28.0	20804	0.6664	0.4844	0.8057	0.5206	0.221	0.4985	0.7352	0.1539	0.5336	0.6236	0.4423	0.6382	0.8047	0.6049	0.6987	0.3639	0.5486
0.5963	29.0	21547	0.6562	0.4952	0.8143	0.5265	0.2398	0.5087	0.7436	0.1549	0.54	0.6299	0.4495	0.6445	0.8094	0.6177	0.7083	0.3728	0.5514
0.5821	30.0	22290	0.6533	0.5019	0.8238	0.5289	0.2385	0.5151	0.7553	0.1579	0.5441	0.6328	0.4478	0.6471	0.8184	0.6045	0.6965	0.3992	0.5691
0.5642	31.0	23033	0.6434	0.506	0.8291	0.532	0.2451	0.5199	0.7421	0.1575	0.5505	0.6342	0.4598	0.6482	0.8086	0.6226	0.7103	0.3893	0.558
0.5547	32.0	23776	0.6382	0.5041	0.8261	0.5454	0.2484	0.5193	0.7529	0.1566	0.551	0.6357	0.4587	0.6488	0.8154	0.6221	0.7123	0.3861	0.5591
0.536	33.0	24519	0.6175	0.5188	0.8382	0.5556	0.2732	0.5318	0.753	0.1607	0.565	0.644	0.4731	0.659	0.8112	0.6352	0.7259	0.4024	0.5621
0.5231	34.0	25262	0.6037	0.531	0.8421	0.5703	0.2879	0.5412	0.765	0.1636	0.5774	0.6578	0.4848	0.6731	0.827	0.6485	0.7353	0.4134	0.5803
0.5042	35.0	26005	0.5947	0.5373	0.846	0.5869	0.3039	0.5495	0.7621	0.1622	0.583	0.6614	0.507	0.6715	0.8218	0.6552	0.7398	0.4194	0.5829
0.4956	36.0	26748	0.5955	0.5379	0.8496	0.5835	0.2997	0.5484	0.7641	0.1651	0.5818	0.6599	0.498	0.6726	0.8222	0.6606	0.7391	0.4152	0.5808
0.4838	37.0	27491	0.5849	0.549	0.8514	0.591	0.3076	0.5609	0.7749	0.1669	0.5937	0.6703	0.5038	0.6858	0.8311	0.6618	0.7439	0.4362	0.5967
0.4586	38.0	28234	0.5708	0.5568	0.8591	0.6044	0.3296	0.5658	0.7782	0.1678	0.6007	0.6776	0.5205	0.6896	0.8356	0.6762	0.7563	0.4374	0.5989
0.455	39.0	28977	0.5749	0.5525	0.8581	0.6	0.3221	0.5597	0.7801	0.1669	0.5965	0.6695	0.5138	0.6786	0.834	0.6722	0.7511	0.4327	0.5879
0.4426	40.0	29720	0.5670	0.5605	0.8626	0.6086	0.3276	0.573	0.7795	0.1678	0.6052	0.6765	0.5188	0.6894	0.8331	0.6771	0.7574	0.4439	0.5957
0.4337	41.0	30463	0.5652	0.5621	0.8631	0.6117	0.3368	0.5715	0.7822	0.1677	0.6074	0.6774	0.5267	0.686	0.8367	0.6783	0.7547	0.4459	0.6001
0.4181	42.0	31206	0.5612	0.5625	0.8635	0.6096	0.3284	0.5754	0.782	0.1677	0.6074	0.678	0.5179	0.6906	0.8381	0.6785	0.7569	0.4466	0.5992
0.4198	43.0	31949	0.5575	0.5692	0.8651	0.6197	0.3385	0.5803	0.7836	0.1698	0.6139	0.6811	0.5308	0.6905	0.8384	0.6867	0.7632	0.4516	0.599
0.4058	44.0	32692	0.5524	0.5706	0.8675	0.6212	0.3436	0.5816	0.7814	0.1692	0.6154	0.6847	0.5345	0.6962	0.8357	0.6871	0.7652	0.4541	0.6042
0.4026	45.0	33435	0.5508	0.5722	0.8664	0.6223	0.3448	0.5838	0.7817	0.1686	0.618	0.6842	0.5337	0.6959	0.8356	0.6902	0.767	0.4541	0.6015
0.398	46.0	34178	0.5498	0.5733	0.8701	0.6257	0.348	0.5843	0.7826	0.169	0.6185	0.6856	0.5347	0.6974	0.8371	0.6898	0.7665	0.4569	0.6048
0.3954	47.0	34921	0.5471	0.5749	0.8687	0.6261	0.3515	0.5856	0.784	0.1695	0.6195	0.6866	0.5355	0.6977	0.8395	0.6925	0.7683	0.4573	0.6049
0.3967	48.0	35664	0.5491	0.5736	0.87	0.6228	0.3481	0.5848	0.7816	0.169	0.6181	0.6848	0.5329	0.697	0.8362	0.6918	0.7674	0.4554	0.6022
0.3874	49.0	36407	0.5468	0.5756	0.8689	0.6253	0.3525	0.5857	0.783	0.1697	0.62	0.6863	0.5366	0.6977	0.8372	0.694	0.7691	0.4571	0.6035
0.3861	50.0	37150	0.5468	0.5754	0.8688	0.625	0.3516	0.5857	0.783	0.1695	0.6195	0.6862	0.5362	0.6977	0.8372	0.6938	0.769	0.4569	0.6034

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.3.2
Tokenizers 0.21.0

加载中…

detr_finetuned_kitti_mots

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5468

Map: 0.5754

Map 50: 0.8688

Map 75: 0.625

Map Small: 0.3516

Map Medium: 0.5857

Map Large: 0.783

Mar 1: 0.1695

Mar 10: 0.6195

Mar 100: 0.6862

Mar Small: 0.5362

Mar Medium: 0.6977

Mar Large: 0.8372

Map Car: 0.6938

Mar 100 Car: 0.769

Map Pedestrian: 0.4569

Mar 100 Pedestrian: 0.6034

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001

train_batch_size: 8

eval_batch_size: 8

seed: 42

optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments

lr_scheduler_type: cosine

num_epochs: 50

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 Car	Mar 100 Car	Map Pedestrian	Mar 100 Pedestrian
1.5904	1.0	743	1.2276	0.1644	0.3572	0.1419	0.0253	0.1267	0.3984	0.08	0.2466	0.3977	0.1364	0.4022	0.7009	0.277	0.4472	0.0518	0.3482
1.3176	2.0	1486	1.1301	0.2261	0.5107	0.1714	0.0479	0.1929	0.5	0.1024	0.3066	0.4201	0.201	0.4196	0.6825	0.3095	0.4709	0.1426	0.3694
1.1187	3.0	2229	1.0565	0.2699	0.5705	0.2147	0.0416	0.2365	0.5833	0.1142	0.332	0.4328	0.1875	0.4434	0.7003	0.3674	0.4924	0.1725	0.3733
1.0835	4.0	2972	1.0326	0.2851	0.5903	0.2363	0.0572	0.2557	0.6048	0.116	0.3481	0.4474	0.2339	0.4447	0.7149	0.3781	0.5115	0.1921	0.3832
1.0681	5.0	3715	1.0095	0.3017	0.5998	0.2728	0.0538	0.2878	0.6051	0.1205	0.3625	0.4664	0.2237	0.4787	0.7298	0.4003	0.5288	0.2031	0.404
1.042	6.0	4458	1.0315	0.271	0.6062	0.1981	0.0628	0.2597	0.5656	0.1073	0.3465	0.4393	0.2258	0.4469	0.6762	0.3601	0.4929	0.182	0.3857
1.0051	7.0	5201	0.9796	0.2875	0.6189	0.2331	0.0695	0.2789	0.5719	0.1122	0.367	0.473	0.2403	0.4912	0.7076	0.3936	0.5423	0.1814	0.4036
0.9798	8.0	5944	0.9567	0.3178	0.6257	0.2915	0.0823	0.3041	0.6211	0.124	0.379	0.485	0.2552	0.5059	0.7105	0.4214	0.5633	0.2142	0.4068
0.9814	9.0	6687	1.0122	0.2799	0.579	0.239	0.0493	0.2585	0.5948	0.1124	0.3488	0.4607	0.2195	0.4725	0.7226	0.3877	0.5279	0.1721	0.3934
1.0057	10.0	7430	0.9464	0.3156	0.6365	0.2797	0.081	0.3083	0.6042	0.1201	0.3814	0.4929	0.275	0.5011	0.736	0.4326	0.5579	0.1986	0.428
0.9267	11.0	8173	0.9137	0.3364	0.6634	0.2955	0.118	0.326	0.6291	0.1276	0.3995	0.5073	0.3059	0.5147	0.7301	0.4369	0.5728	0.2359	0.4417
0.8938	12.0	8916	0.8808	0.3622	0.6811	0.3348	0.1147	0.3497	0.6639	0.1358	0.4187	0.5212	0.2956	0.536	0.7572	0.4784	0.5986	0.246	0.4438
0.8575	13.0	9659	0.8632	0.3614	0.6888	0.34	0.1005	0.3647	0.6565	0.1305	0.4254	0.5258	0.2989	0.5449	0.7523	0.4629	0.5893	0.2599	0.4624
0.8442	14.0	10402	0.8544	0.3746	0.6922	0.3628	0.1129	0.3759	0.6419	0.1356	0.4284	0.5352	0.3325	0.5545	0.7302	0.4878	0.6007	0.2614	0.4696
0.8213	15.0	11145	0.8441	0.3792	0.7034	0.3561	0.1321	0.3742	0.6534	0.1365	0.4317	0.5378	0.324	0.56	0.7363	0.5039	0.6128	0.2544	0.4628
0.8115	16.0	11888	0.8210	0.3888	0.709	0.3807	0.128	0.392	0.6675	0.1399	0.4413	0.5468	0.3479	0.5616	0.7491	0.5037	0.6212	0.2739	0.4723
0.8093	17.0	12631	0.8127	0.385	0.7092	0.3751	0.1383	0.3858	0.6626	0.1381	0.4399	0.5493	0.3673	0.556	0.7529	0.5141	0.6257	0.2559	0.4729
0.7893	18.0	13374	0.8204	0.3901	0.7194	0.3675	0.1295	0.3842	0.6851	0.1378	0.4404	0.5416	0.3486	0.5481	0.7602	0.5048	0.6092	0.2755	0.474
0.7459	19.0	14117	0.7880	0.4058	0.729	0.4085	0.1582	0.4043	0.6709	0.1407	0.46	0.5627	0.3686	0.5794	0.7528	0.53	0.6421	0.2816	0.4832
0.7383	20.0	14860	0.7477	0.4264	0.7551	0.4264	0.1563	0.4328	0.7092	0.1441	0.48	0.5888	0.3989	0.6004	0.788	0.538	0.6577	0.3149	0.5199
0.7207	21.0	15603	0.7688	0.4188	0.7599	0.4087	0.1591	0.419	0.7057	0.1419	0.4691	0.5641	0.3832	0.5686	0.772	0.5306	0.6361	0.307	0.492
0.7127	22.0	16346	0.7450	0.4368	0.7622	0.4379	0.1733	0.4368	0.7099	0.1483	0.4828	0.5834	0.3873	0.5959	0.7875	0.5601	0.6532	0.3136	0.5136
0.698	23.0	17089	0.7429	0.4401	0.7626	0.4515	0.1739	0.4456	0.7194	0.1498	0.4841	0.5901	0.3878	0.6079	0.7878	0.5536	0.6524	0.3266	0.5278
0.6836	24.0	17832	0.7642	0.422	0.7565	0.4217	0.154	0.428	0.7066	0.1433	0.47	0.5743	0.3941	0.5791	0.7801	0.5423	0.6404	0.3017	0.5081
0.6684	25.0	18575	0.7016	0.4599	0.7889	0.4717	0.1873	0.4706	0.7334	0.1497	0.5093	0.6116	0.4221	0.6271	0.8006	0.5767	0.6837	0.3431	0.5395
0.6471	26.0	19318	0.6890	0.4724	0.791	0.4869	0.2053	0.4825	0.7304	0.1552	0.516	0.6068	0.4245	0.6179	0.7994	0.6004	0.6895	0.3443	0.5241
0.6259	27.0	20061	0.6788	0.4726	0.7816	0.4914	0.2182	0.481	0.7202	0.1555	0.5212	0.6199	0.4374	0.6387	0.7919	0.6137	0.7058	0.3316	0.5339
0.6038	28.0	20804	0.6664	0.4844	0.8057	0.5206	0.221	0.4985	0.7352	0.1539	0.5336	0.6236	0.4423	0.6382	0.8047	0.6049	0.6987	0.3639	0.5486
0.5963	29.0	21547	0.6562	0.4952	0.8143	0.5265	0.2398	0.5087	0.7436	0.1549	0.54	0.6299	0.4495	0.6445	0.8094	0.6177	0.7083	0.3728	0.5514
0.5821	30.0	22290	0.6533	0.5019	0.8238	0.5289	0.2385	0.5151	0.7553	0.1579	0.5441	0.6328	0.4478	0.6471	0.8184	0.6045	0.6965	0.3992	0.5691
0.5642	31.0	23033	0.6434	0.506	0.8291	0.532	0.2451	0.5199	0.7421	0.1575	0.5505	0.6342	0.4598	0.6482	0.8086	0.6226	0.7103	0.3893	0.558
0.5547	32.0	23776	0.6382	0.5041	0.8261	0.5454	0.2484	0.5193	0.7529	0.1566	0.551	0.6357	0.4587	0.6488	0.8154	0.6221	0.7123	0.3861	0.5591
0.536	33.0	24519	0.6175	0.5188	0.8382	0.5556	0.2732	0.5318	0.753	0.1607	0.565	0.644	0.4731	0.659	0.8112	0.6352	0.7259	0.4024	0.5621
0.5231	34.0	25262	0.6037	0.531	0.8421	0.5703	0.2879	0.5412	0.765	0.1636	0.5774	0.6578	0.4848	0.6731	0.827	0.6485	0.7353	0.4134	0.5803
0.5042	35.0	26005	0.5947	0.5373	0.846	0.5869	0.3039	0.5495	0.7621	0.1622	0.583	0.6614	0.507	0.6715	0.8218	0.6552	0.7398	0.4194	0.5829
0.4956	36.0	26748	0.5955	0.5379	0.8496	0.5835	0.2997	0.5484	0.7641	0.1651	0.5818	0.6599	0.498	0.6726	0.8222	0.6606	0.7391	0.4152	0.5808
0.4838	37.0	27491	0.5849	0.549	0.8514	0.591	0.3076	0.5609	0.7749	0.1669	0.5937	0.6703	0.5038	0.6858	0.8311	0.6618	0.7439	0.4362	0.5967
0.4586	38.0	28234	0.5708	0.5568	0.8591	0.6044	0.3296	0.5658	0.7782	0.1678	0.6007	0.6776	0.5205	0.6896	0.8356	0.6762	0.7563	0.4374	0.5989
0.455	39.0	28977	0.5749	0.5525	0.8581	0.6	0.3221	0.5597	0.7801	0.1669	0.5965	0.6695	0.5138	0.6786	0.834	0.6722	0.7511	0.4327	0.5879
0.4426	40.0	29720	0.5670	0.5605	0.8626	0.6086	0.3276	0.573	0.7795	0.1678	0.6052	0.6765	0.5188	0.6894	0.8331	0.6771	0.7574	0.4439	0.5957
0.4337	41.0	30463	0.5652	0.5621	0.8631	0.6117	0.3368	0.5715	0.7822	0.1677	0.6074	0.6774	0.5267	0.686	0.8367	0.6783	0.7547	0.4459	0.6001
0.4181	42.0	31206	0.5612	0.5625	0.8635	0.6096	0.3284	0.5754	0.782	0.1677	0.6074	0.678	0.5179	0.6906	0.8381	0.6785	0.7569	0.4466	0.5992
0.4198	43.0	31949	0.5575	0.5692	0.8651	0.6197	0.3385	0.5803	0.7836	0.1698	0.6139	0.6811	0.5308	0.6905	0.8384	0.6867	0.7632	0.4516	0.599
0.4058	44.0	32692	0.5524	0.5706	0.8675	0.6212	0.3436	0.5816	0.7814	0.1692	0.6154	0.6847	0.5345	0.6962	0.8357	0.6871	0.7652	0.4541	0.6042
0.4026	45.0	33435	0.5508	0.5722	0.8664	0.6223	0.3448	0.5838	0.7817	0.1686	0.618	0.6842	0.5337	0.6959	0.8356	0.6902	0.767	0.4541	0.6015
0.398	46.0	34178	0.5498	0.5733	0.8701	0.6257	0.348	0.5843	0.7826	0.169	0.6185	0.6856	0.5347	0.6974	0.8371	0.6898	0.7665	0.4569	0.6048
0.3954	47.0	34921	0.5471	0.5749	0.8687	0.6261	0.3515	0.5856	0.784	0.1695	0.6195	0.6866	0.5355	0.6977	0.8395	0.6925	0.7683	0.4573	0.6049
0.3967	48.0	35664	0.5491	0.5736	0.87	0.6228	0.3481	0.5848	0.7816	0.169	0.6181	0.6848	0.5329	0.697	0.8362	0.6918	0.7674	0.4554	0.6022
0.3874	49.0	36407	0.5468	0.5756	0.8689	0.6253	0.3525	0.5857	0.783	0.1697	0.62	0.6863	0.5366	0.6977	0.8372	0.694	0.7691	0.4571	0.6035
0.3861	50.0	37150	0.5468	0.5754	0.8688	0.625	0.3516	0.5857	0.783	0.1695	0.6195	0.6862	0.5362	0.6977	0.8372	0.6938	0.769	0.4569	0.6034

Framework versions

Transformers 4.49.0

Pytorch 2.5.1+cu121

Datasets 3.3.2

Tokenizers 0.21.0

toukapy/detr_finetuned_kitti_mots

detr_finetuned_kitti_mots

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

detr_finetuned_kitti_mots

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions