detr_finetuned_kitti_mots-bright

toukapy/detr_finetuned_kitti_mots-bright

detr_finetuned_kitti_mots-bright is a object detection model from toukapy. 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: conditional_detr
Access: Public
Created: Mar 8, 2025
Updated: Mar 8, 2025
SHA: 8202370b

Base models

microsoft/conditional-detr-resnet-50

Model card

From the Hugging Face model README

detr_finetuned_kitti_mots-bright

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: 0.6703
Map: 0.4803
Map 50: 0.7799
Map 75: 0.494
Map Small: 0.2239
Map Medium: 0.4794
Map Large: 0.7442
Mar 1: 0.1586
Mar 10: 0.5224
Mar 100: 0.6196
Mar Small: 0.4422
Mar Medium: 0.6281
Mar Large: 0.8123
Map Car: 0.6182
Mar 100 Car: 0.7081
Map Pedestrian: 0.3423
Mar 100 Pedestrian: 0.5311

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: 1e-05
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: 60

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
0.9436	1.0	743	0.8850	0.3518	0.6491	0.3381	0.1025	0.3437	0.6351	0.1336	0.4063	0.5187	0.3121	0.5288	0.7428	0.4742	0.5947	0.2293	0.4428
0.8864	2.0	1486	0.8514	0.3714	0.6685	0.3602	0.1196	0.3636	0.6536	0.1384	0.4226	0.5403	0.3347	0.5536	0.7549	0.5002	0.6203	0.2426	0.4603
0.8577	3.0	2229	0.8346	0.3815	0.6809	0.3741	0.1251	0.3727	0.6703	0.1428	0.4299	0.5424	0.3401	0.5521	0.7616	0.5075	0.6206	0.2555	0.4642
0.8454	4.0	2972	0.8225	0.3872	0.6848	0.3845	0.1313	0.3804	0.671	0.142	0.4376	0.5496	0.3441	0.5631	0.763	0.5173	0.6291	0.2571	0.47
0.8258	5.0	3715	0.8161	0.3929	0.6901	0.3893	0.1383	0.3859	0.6733	0.1434	0.4402	0.5486	0.3469	0.561	0.7605	0.5246	0.6332	0.2613	0.464
0.8186	6.0	4458	0.8088	0.3963	0.6967	0.3967	0.1404	0.3882	0.6784	0.1441	0.4437	0.5516	0.3549	0.5603	0.7673	0.5232	0.6351	0.2695	0.4681
0.8058	7.0	5201	0.7959	0.4054	0.7096	0.4078	0.1448	0.4036	0.6818	0.1444	0.4565	0.5626	0.3653	0.5749	0.7706	0.5338	0.6433	0.2771	0.4819
0.7957	8.0	5944	0.7860	0.4115	0.7072	0.4119	0.1455	0.4088	0.6917	0.1469	0.4561	0.5666	0.3603	0.5817	0.7772	0.543	0.6507	0.28	0.4825
0.7842	9.0	6687	0.7766	0.4155	0.7136	0.4165	0.1508	0.4105	0.6974	0.1479	0.459	0.5711	0.3734	0.5819	0.7826	0.547	0.6543	0.2841	0.4879
0.7814	10.0	7430	0.7803	0.4128	0.7122	0.4133	0.1481	0.41	0.6995	0.1467	0.4578	0.5646	0.3611	0.577	0.7794	0.5464	0.6516	0.2793	0.4776
0.771	11.0	8173	0.7707	0.4145	0.7134	0.4199	0.1524	0.4116	0.696	0.1459	0.4624	0.5691	0.3691	0.5823	0.7773	0.5474	0.6548	0.2816	0.4834
0.7674	12.0	8916	0.7643	0.4228	0.7207	0.4258	0.1581	0.4223	0.6966	0.1477	0.4668	0.5762	0.3753	0.5912	0.7799	0.5566	0.6607	0.289	0.4916
0.7584	13.0	9659	0.7559	0.4246	0.7265	0.4316	0.1629	0.4213	0.7056	0.1472	0.4698	0.5772	0.3817	0.5872	0.7875	0.5574	0.6611	0.2919	0.4933
0.7516	14.0	10402	0.7555	0.4269	0.7277	0.4334	0.1615	0.4246	0.7072	0.1481	0.4716	0.5787	0.3765	0.5922	0.7879	0.5619	0.6641	0.2919	0.4933
0.7456	15.0	11145	0.7448	0.4328	0.7333	0.4432	0.1679	0.4316	0.7122	0.1507	0.4785	0.5819	0.3883	0.5916	0.7915	0.5686	0.6714	0.297	0.4923
0.7404	16.0	11888	0.7475	0.4336	0.7359	0.4363	0.1668	0.4304	0.7181	0.1501	0.4798	0.579	0.3815	0.5885	0.7936	0.5668	0.6671	0.3005	0.4909
0.7404	17.0	12631	0.7393	0.4361	0.7386	0.4473	0.1752	0.4337	0.7128	0.1509	0.4821	0.583	0.3997	0.5905	0.787	0.5745	0.6745	0.2978	0.4915
0.7279	18.0	13374	0.7331	0.4384	0.7406	0.4417	0.1771	0.4376	0.7105	0.1508	0.4833	0.5879	0.3965	0.5984	0.7926	0.578	0.6775	0.2988	0.4982
0.722	19.0	14117	0.7329	0.4407	0.742	0.4443	0.1834	0.4393	0.7138	0.1505	0.4866	0.5898	0.4042	0.5995	0.7887	0.5748	0.6751	0.3067	0.5044
0.7184	20.0	14860	0.7240	0.4484	0.748	0.4557	0.1872	0.4476	0.7186	0.153	0.4915	0.5943	0.4045	0.6062	0.7926	0.5846	0.6819	0.3121	0.5066
0.7177	21.0	15603	0.7266	0.4447	0.75	0.4517	0.1856	0.4419	0.7154	0.1515	0.4883	0.5893	0.4061	0.5988	0.7866	0.58	0.6777	0.3095	0.5009
0.7077	22.0	16346	0.7172	0.4496	0.752	0.4618	0.1861	0.4486	0.7199	0.1524	0.4921	0.5935	0.4065	0.6031	0.7946	0.5856	0.6812	0.3137	0.5057
0.7073	23.0	17089	0.7199	0.4471	0.7489	0.4598	0.1882	0.4443	0.7203	0.1518	0.4898	0.5944	0.4094	0.6039	0.7936	0.5819	0.6807	0.3123	0.5081
0.7043	24.0	17832	0.7139	0.4525	0.7506	0.4618	0.1893	0.4508	0.7258	0.1542	0.4964	0.5994	0.4122	0.6084	0.8026	0.589	0.6827	0.316	0.516
0.6988	25.0	18575	0.7132	0.4527	0.7543	0.4627	0.19	0.4498	0.7296	0.1538	0.4957	0.5967	0.4039	0.6064	0.805	0.591	0.6854	0.3144	0.5081
0.6957	26.0	19318	0.7092	0.4545	0.7561	0.4626	0.1934	0.4516	0.7304	0.1539	0.4973	0.5984	0.4111	0.6069	0.8027	0.5887	0.6838	0.3203	0.513
0.6864	27.0	20061	0.7065	0.4559	0.7552	0.4667	0.1973	0.4536	0.7279	0.1542	0.4987	0.5998	0.4103	0.6117	0.7982	0.5941	0.6895	0.3178	0.5101
0.684	28.0	20804	0.7045	0.458	0.7582	0.4746	0.1966	0.4572	0.7311	0.1545	0.4997	0.6022	0.415	0.6116	0.8053	0.594	0.6893	0.322	0.5152
0.681	29.0	21547	0.7040	0.4574	0.7603	0.4715	0.1971	0.4563	0.7296	0.1536	0.4988	0.5987	0.4136	0.6073	0.8004	0.591	0.6872	0.3239	0.5102
0.6769	30.0	22290	0.7023	0.4585	0.7613	0.4703	0.2004	0.4565	0.7335	0.1539	0.5012	0.6019	0.4214	0.6084	0.8038	0.5922	0.6902	0.3247	0.5136
0.6774	31.0	23033	0.6974	0.4607	0.7646	0.4775	0.2032	0.4594	0.7304	0.1543	0.502	0.6048	0.4317	0.6094	0.8032	0.5963	0.6924	0.3251	0.5173
0.6678	32.0	23776	0.6914	0.4654	0.7623	0.4756	0.2076	0.4642	0.7337	0.1559	0.5067	0.6088	0.4287	0.6175	0.8047	0.6021	0.6976	0.3287	0.5201
0.6733	33.0	24519	0.6896	0.4664	0.767	0.4805	0.212	0.4653	0.7326	0.1552	0.5086	0.6078	0.4246	0.6166	0.8067	0.6038	0.6979	0.329	0.5177
0.6656	34.0	25262	0.6878	0.4687	0.769	0.4857	0.2133	0.4682	0.7353	0.1558	0.5112	0.6078	0.4241	0.6173	0.8055	0.6048	0.6975	0.3326	0.5181
0.6599	35.0	26005	0.6848	0.4716	0.7718	0.492	0.2121	0.4717	0.7364	0.156	0.5135	0.6121	0.4292	0.6218	0.8082	0.6081	0.7002	0.3351	0.524
0.6646	36.0	26748	0.6857	0.4709	0.7721	0.487	0.2129	0.4711	0.7369	0.1565	0.5137	0.6109	0.4316	0.6184	0.8092	0.6073	0.7001	0.3344	0.5217
0.6568	37.0	27491	0.6867	0.4707	0.7729	0.4843	0.2147	0.4694	0.7393	0.1564	0.5117	0.6102	0.4252	0.6195	0.8094	0.6065	0.6985	0.3349	0.5219
0.6493	38.0	28234	0.6830	0.4713	0.771	0.4835	0.2121	0.4734	0.7357	0.1573	0.5131	0.6118	0.4277	0.622	0.8083	0.6081	0.7002	0.3345	0.5234
0.6567	39.0	28977	0.6813	0.4724	0.771	0.4841	0.2117	0.4729	0.7396	0.1573	0.515	0.6135	0.4351	0.6213	0.8097	0.6098	0.701	0.3351	0.526
0.6532	40.0	29720	0.6797	0.4743	0.7751	0.4848	0.2137	0.4761	0.7369	0.1573	0.516	0.6149	0.4354	0.6243	0.8077	0.6101	0.7019	0.3384	0.5279
0.6475	41.0	30463	0.6769	0.4755	0.773	0.4903	0.219	0.4742	0.7397	0.1572	0.5193	0.6169	0.4418	0.6248	0.8088	0.6125	0.7044	0.3384	0.5295
0.6432	42.0	31206	0.6779	0.4762	0.7757	0.4926	0.2171	0.4777	0.739	0.158	0.5184	0.6168	0.4384	0.6262	0.8079	0.6122	0.703	0.3403	0.5305
0.6482	43.0	31949	0.6762	0.4759	0.7756	0.4897	0.218	0.4755	0.74	0.1579	0.5169	0.6141	0.4329	0.624	0.8071	0.6132	0.7042	0.3385	0.524
0.6427	44.0	32692	0.6744	0.4771	0.776	0.49	0.2167	0.4766	0.7445	0.1591	0.5195	0.6159	0.4333	0.6258	0.8112	0.616	0.7064	0.3382	0.5254
0.6409	45.0	33435	0.6758	0.4767	0.777	0.4882	0.2189	0.4762	0.7426	0.1581	0.5181	0.6155	0.437	0.6239	0.8099	0.6141	0.7046	0.3393	0.5264
0.6361	46.0	34178	0.6748	0.4758	0.7762	0.4888	0.2178	0.4744	0.7448	0.1577	0.5177	0.6139	0.4299	0.6234	0.8116	0.6135	0.704	0.338	0.5238
0.6383	47.0	34921	0.6757	0.475	0.7788	0.4883	0.217	0.4751	0.7424	0.158	0.5184	0.6139	0.4278	0.6244	0.8116	0.6115	0.7031	0.3384	0.5247
0.6421	48.0	35664	0.6717	0.4793	0.7796	0.4909	0.2217	0.4788	0.7447	0.1589	0.5208	0.6186	0.4413	0.627	0.8114	0.6161	0.7071	0.3426	0.5301
0.6357	49.0	36407	0.6712	0.4789	0.7787	0.4916	0.2215	0.4789	0.7425	0.1592	0.5219	0.6188	0.4403	0.6279	0.8114	0.6161	0.7069	0.3418	0.5308
0.6322	50.0	37150	0.6715	0.4792	0.7795	0.4922	0.2219	0.4792	0.7436	0.1587	0.5223	0.6188	0.4368	0.629	0.8124	0.6174	0.7074	0.3409	0.5302
0.6324	51.0	37893	0.6729	0.478	0.7787	0.4906	0.2206	0.4772	0.7447	0.1585	0.5202	0.6171	0.4379	0.6254	0.8126	0.6153	0.7048	0.3407	0.5293
0.6402	52.0	38636	0.6707	0.4806	0.7792	0.4978	0.2222	0.4795	0.747	0.1592	0.5221	0.6196	0.4419	0.6278	0.8135	0.6174	0.7076	0.3438	0.5317
0.6328	53.0	39379	0.6716	0.4796	0.7794	0.4964	0.2231	0.4789	0.7445	0.1587	0.5212	0.6184	0.4405	0.6269	0.812	0.6173	0.707	0.342	0.5299
0.6349	54.0	40122	0.6715	0.4795	0.7796	0.4941	0.223	0.4782	0.7453	0.1587	0.5216	0.6186	0.4399	0.6268	0.8135	0.6165	0.7066	0.3425	0.5305
0.6293	55.0	40865	0.6705	0.4798	0.779	0.4921	0.2232	0.479	0.7445	0.159	0.5222	0.6192	0.4408	0.628	0.8123	0.6177	0.7073	0.3419	0.5311
0.6324	56.0	41608	0.6705	0.4804	0.78	0.4939	0.2238	0.48	0.7446	0.1588	0.5222	0.6198	0.4418	0.6285	0.8127	0.618	0.7079	0.3428	0.5318
0.6293	57.0	42351	0.6702	0.4803	0.7796	0.4947	0.2235	0.4792	0.7452	0.159	0.5228	0.6197	0.4415	0.6283	0.813	0.6178	0.708	0.3428	0.5314
0.6353	58.0	43094	0.6701	0.4804	0.7798	0.4943	0.224	0.4795	0.7444	0.1588	0.5223	0.6198	0.4422	0.6284	0.8128	0.6183	0.7082	0.3424	0.5315
0.6323	59.0	43837	0.6703	0.4803	0.78	0.4935	0.2238	0.4794	0.7443	0.1586	0.5223	0.6196	0.4419	0.6282	0.8124	0.6183	0.7082	0.3423	0.5309
0.6384	60.0	44580	0.6703	0.4803	0.7799	0.494	0.2239	0.4794	0.7442	0.1586	0.5224	0.6196	0.4422	0.6281	0.8123	0.6182	0.7081	0.3423	0.5311

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.3.2
Tokenizers 0.21.0

Task: Object Detection
Library: transformers
Type: conditional_detr
License: apache-2.0
Created: Mar 8, 2025
Updated: Mar 8, 2025