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mfly-auton/
suas-2025-rtdetr-finetuned-b16-lr1e-4

Name: suas-2025-rtdetr-finetuned-b16-lr1e-4
Availability: InStock

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suas-2025-rtdetr-finetuned-b16-lr1e-4

This model is a fine-tuned version of PekingU/rtdetr_r50vd on the mfly-auton/suas-2025-synthetic-data dataset. It achieves the following results on the evaluation set:

Loss: 7.4789
Map: 0.636
Map 50: 0.7833
Map 75: 0.6825
Map Small: 0.5292
Map Medium: 0.6761
Map Large: 0.6243
Mar 1: 0.6914
Mar 10: 0.8319
Mar 100: 0.8459
Mar Small: 0.6526
Mar Medium: 0.861
Mar Large: 0.9511
Map Baseball-bat: 0.7327
Mar 100 Baseball-bat: 0.8479
Map Basketball: 0.7378
Mar 100 Basketball: 0.8453
Map Car: -1.0
Mar 100 Car: -1.0
Map Football: 0.515
Mar 100 Football: 0.5874
Map Human: 0.2845
Mar 100 Human: 0.6493
Map Luggage: 0.3927
Mar 100 Luggage: 0.9199
Map Mattress: 0.4605
Mar 100 Mattress: 0.9731
Map Motorcycle: 0.8921
Mar 100 Motorcycle: 0.9379
Map Skis: 0.9356
Mar 100 Skis: 0.9772
Map Snowboard: 0.4858
Mar 100 Snowboard: 0.9648
Map Soccer-ball: 0.291
Mar 100 Soccer-ball: 0.5365
Map Stop-sign: 0.8721
Mar 100 Stop-sign: 0.9777
Map Tennis-racket: 0.8872
Mar 100 Tennis-racket: 0.9275
Map Umbrella: 0.7926
Mar 100 Umbrella: 0.945
Map Volleyball: 0.6243
Mar 100 Volleyball: 0.7525

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: 16
eval_batch_size: 16
seed: 1337
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10.0
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 Baseball-bat	Mar 100 Baseball-bat	Map Basketball	Mar 100 Basketball	Map Car	Mar 100 Car	Map Football	Mar 100 Football	Map Human	Mar 100 Human	Map Luggage	Mar 100 Luggage	Map Mattress	Mar 100 Mattress	Map Motorcycle	Mar 100 Motorcycle	Map Skis	Mar 100 Skis	Map Snowboard	Mar 100 Snowboard	Map Soccer-ball	Mar 100 Soccer-ball	Map Stop-sign	Mar 100 Stop-sign	Map Tennis-racket	Mar 100 Tennis-racket	Map Umbrella	Mar 100 Umbrella	Map Volleyball	Mar 100 Volleyball
14.9473	1.0	438	6.0393	0.6929	0.8006	0.7826	0.6924	0.6693	0.7428	0.7205	0.8507	0.8584	0.7888	0.8574	0.8979	0.7927	0.8492	0.8387	0.9263	-1.0	-1.0	0.5419	0.8203	0.2032	0.5136	0.4715	0.931	0.6218	0.9887	0.866	0.9199	0.8182	0.8782	0.5796	0.9515	0.6169	0.6753	0.9283	0.9732	0.814	0.8668	0.8116	0.8802	0.7961	0.8431
6.8045	2.0	876	5.2885	0.7468	0.8307	0.8234	0.7611	0.7249	0.6791	0.7565	0.8986	0.9052	0.8475	0.877	0.9424	0.8403	0.8959	0.8533	0.9358	-1.0	-1.0	0.7497	0.8638	0.1319	0.532	0.4374	0.9456	0.5892	0.9958	0.9306	0.9599	0.9652	0.9802	0.6246	0.995	0.7541	0.8175	0.9788	0.9958	0.8972	0.9316	0.9091	0.9701	0.7936	0.8532
6.122	3.0	1314	6.0592	0.7128	0.8135	0.7987	0.6996	0.7187	0.665	0.739	0.8748	0.8891	0.8104	0.8731	0.9347	0.8141	0.8864	0.8214	0.9197	-1.0	-1.0	0.7119	0.8346	0.1712	0.5716	0.3367	0.9129	0.4596	0.9784	0.9166	0.9472	0.9445	0.9718	0.6822	0.9922	0.6055	0.7355	0.9341	0.9659	0.8819	0.9207	0.9083	0.9698	0.7906	0.8409
5.7542	4.0	1752	6.7229	0.7089	0.8239	0.7984	0.6721	0.7153	0.6452	0.752	0.8732	0.8817	0.7591	0.8769	0.9587	0.7857	0.8574	0.835	0.9052	-1.0	-1.0	0.6519	0.7566	0.3228	0.6242	0.5068	0.9147	0.4086	0.9842	0.9254	0.9588	0.9363	0.9639	0.6089	0.9904	0.6176	0.7185	0.8906	0.9765	0.8719	0.9197	0.8104	0.9656	0.7532	0.8083
5.4197	5.0	2190	6.9705	0.6705	0.7896	0.7393	0.6309	0.6775	0.6347	0.7021	0.8427	0.8567	0.7156	0.8715	0.9375	0.8044	0.874	0.8447	0.8896	-1.0	-1.0	0.6092	0.6991	0.1786	0.5444	0.3152	0.8589	0.3916	0.946	0.943	0.9708	0.957	0.9782	0.521	0.9759	0.4336	0.5923	0.8949	0.9757	0.8747	0.9171	0.8842	0.9704	0.7354	0.8017
5.1598	6.0	2628	6.7201	0.6509	0.7806	0.7067	0.5621	0.6931	0.6332	0.6927	0.826	0.839	0.6904	0.8633	0.8993	0.7365	0.8412	0.8438	0.9021	-1.0	-1.0	0.5378	0.639	0.1533	0.4137	0.412	0.91	0.3546	0.9008	0.9302	0.967	0.9724	0.9881	0.5606	0.9739	0.3666	0.5918	0.8779	0.9642	0.843	0.8969	0.8855	0.9633	0.6379	0.7946
5.1626	7.0	3066	7.2974	0.644	0.7767	0.6936	0.5262	0.6787	0.6513	0.7043	0.8445	0.8587	0.6746	0.8942	0.9601	0.7639	0.8663	0.7068	0.8149	-1.0	-1.0	0.5452	0.6602	0.3086	0.6825	0.41	0.9299	0.4634	0.9747	0.9327	0.9679	0.9505	0.9797	0.4214	0.9677	0.2936	0.5483	0.852	0.9818	0.8823	0.9181	0.8924	0.9678	0.5933	0.7615
5.15	8.0	3504	7.2201	0.6572	0.7908	0.7113	0.5559	0.6962	0.6276	0.7048	0.8396	0.8534	0.6795	0.8702	0.9327	0.7574	0.8526	0.7803	0.8718	-1.0	-1.0	0.5741	0.6547	0.2742	0.5961	0.418	0.9233	0.4893	0.9697	0.91	0.9479	0.9345	0.9807	0.5642	0.9692	0.3294	0.5602	0.8803	0.9821	0.8794	0.9264	0.7936	0.9476	0.616	0.7659
4.9521	9.0	3942	7.5060	0.6374	0.7847	0.6838	0.5132	0.6921	0.6331	0.6868	0.8276	0.8419	0.6487	0.8619	0.9341	0.7388	0.8431	0.715	0.8273	-1.0	-1.0	0.5194	0.592	0.2577	0.624	0.3945	0.9234	0.4599	0.966	0.9031	0.942	0.9235	0.9752	0.5532	0.9694	0.2822	0.5368	0.9131	0.9855	0.8769	0.9244	0.8091	0.9438	0.5766	0.7336
4.8252	10.0	4380	7.4789	0.636	0.7833	0.6825	0.5292	0.6761	0.6243	0.6914	0.8319	0.8459	0.6526	0.861	0.9511	0.7327	0.8479	0.7378	0.8453	-1.0	-1.0	0.515	0.5874	0.2845	0.6493	0.3927	0.9199	0.4605	0.9731	0.8921	0.9379	0.9356	0.9772	0.4858	0.9648	0.291	0.5365	0.8721	0.9777	0.8872	0.9275	0.7926	0.945	0.6243	0.7525

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.21.0

mfly-auton/suas-2025-rtdetr-finetuned-b16-lr1e-4

suas-2025-rtdetr-finetuned-b16-lr1e-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mfly-auton/
suas-2025-rtdetr-finetuned-b16-lr1e-4