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mfly-auton/
suas-2025-rtdetr-finetuned-b8-lr3e-5

Name: suas-2025-rtdetr-finetuned-b8-lr3e-5
Availability: InStock

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suas-2025-rtdetr-finetuned-b8-lr3e-5

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: 6.4174
Map: 0.6051
Map 50: 0.7291
Map 75: 0.6597
Map Small: 0.5252
Map Medium: 0.6131
Map Large: 0.6723
Mar 1: 0.6006
Mar 10: 0.7415
Mar 100: 0.7959
Mar Small: 0.5831
Mar Medium: 0.8299
Mar Large: 0.948
Map Baseball-bat: 0.5841
Mar 100 Baseball-bat: 0.7219
Map Basketball: 0.4357
Mar 100 Basketball: 0.4955
Map Car: -1.0
Mar 100 Car: -1.0
Map Football: 0.4363
Mar 100 Football: 0.5014
Map Human: 0.9189
Mar 100 Human: 0.9512
Map Luggage: 0.5995
Mar 100 Luggage: 0.8773
Map Mattress: 0.7881
Mar 100 Mattress: 0.9906
Map Motorcycle: 0.9485
Mar 100 Motorcycle: 0.9752
Map Skis: 0.9582
Mar 100 Skis: 0.9782
Map Snowboard: 0.0257
Mar 100 Snowboard: 0.777
Map Soccer-ball: 0.6432
Mar 100 Soccer-ball: 0.7154
Map Stop-sign: 0.5994
Mar 100 Stop-sign: 0.9274
Map Tennis-racket: 0.0019
Mar 100 Tennis-racket: 0.5736
Map Umbrella: 0.8665
Mar 100 Umbrella: 0.9243
Map Volleyball: 0.6647
Mar 100 Volleyball: 0.7338

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: 3e-05
train_batch_size: 8
eval_batch_size: 32
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
17.7551	1.0	875	5.2510	0.6942	0.7838	0.7782	0.7254	0.6405	0.7363	0.7413	0.8942	0.9051	0.8089	0.8996	0.9496	0.6964	0.8173	0.7915	0.8415	-1.0	-1.0	0.7303	0.8192	0.8746	0.9281	0.6526	0.9372	0.9142	0.9711	0.8867	0.9311	0.8376	0.9594	0.3429	0.9742	0.7454	0.8686	0.581	0.9542	0.3918	0.944	0.4713	0.8414	0.8027	0.8846
6.6319	2.0	1750	5.6671	0.6915	0.7796	0.7715	0.6499	0.6468	0.8104	0.7323	0.879	0.892	0.726	0.9168	0.9841	0.6895	0.7787	0.5032	0.6076	-1.0	-1.0	0.7195	0.7926	0.9186	0.9593	0.6184	0.9288	0.895	0.9984	0.901	0.9643	0.9222	0.9634	0.2897	0.9699	0.7717	0.8578	0.6219	0.9584	0.2013	0.8995	0.8724	0.9458	0.7562	0.864
6.3092	3.0	2625	5.3786	0.6861	0.7797	0.7703	0.6794	0.6505	0.8039	0.7008	0.8593	0.8895	0.738	0.9294	0.9851	0.7854	0.863	0.5392	0.5953	-1.0	-1.0	0.6451	0.7011	0.9165	0.9458	0.6485	0.9354	0.9011	0.9986	0.9194	0.9592	0.9354	0.9827	0.147	0.9815	0.8089	0.8686	0.7102	0.9732	0.0527	0.8694	0.8651	0.9418	0.7312	0.838
5.8652	4.0	3500	5.8538	0.6465	0.749	0.7306	0.6014	0.6486	0.6801	0.6644	0.8092	0.8383	0.6448	0.8784	0.9506	0.6314	0.7432	0.4033	0.446	-1.0	-1.0	0.6013	0.6536	0.9291	0.9609	0.729	0.9151	0.64	0.9942	0.9361	0.9592	0.8382	0.9396	0.1472	0.893	0.7465	0.8059	0.803	0.952	0.0352	0.7725	0.8569	0.892	0.7539	0.8091
5.4748	5.0	4375	5.8922	0.6416	0.7467	0.7203	0.5884	0.6359	0.7155	0.6543	0.8104	0.8498	0.6337	0.895	0.965	0.6726	0.7857	0.3624	0.4249	-1.0	-1.0	0.5819	0.641	0.9266	0.9518	0.6374	0.9073	0.7673	0.9959	0.9372	0.9692	0.9629	0.9861	0.0824	0.9316	0.7745	0.8198	0.7208	0.9508	0.015	0.8404	0.7927	0.8863	0.7489	0.8061
5.352	6.0	5250	6.3142	0.6409	0.741	0.7178	0.6313	0.5958	0.6861	0.6395	0.7987	0.8411	0.6741	0.8595	0.9544	0.7148	0.8051	0.4792	0.5386	-1.0	-1.0	0.5879	0.6392	0.9101	0.9478	0.5811	0.8811	0.8213	0.9987	0.9139	0.96	0.9479	0.9767	0.052	0.9105	0.7854	0.8321	0.6801	0.9332	0.0209	0.6995	0.7664	0.8798	0.7112	0.7733
5.1405	7.0	6125	6.3704	0.6464	0.7579	0.7185	0.5862	0.6209	0.6971	0.6346	0.7737	0.8247	0.6346	0.865	0.9372	0.6828	0.7855	0.5464	0.5882	-1.0	-1.0	0.5058	0.5697	0.9172	0.9526	0.6302	0.9021	0.8037	0.9908	0.9339	0.9643	0.9615	0.9767	0.0249	0.6942	0.7298	0.7859	0.7449	0.9567	0.0025	0.713	0.8942	0.9397	0.6718	0.7272
4.9059	8.0	7000	6.4879	0.6238	0.7487	0.6799	0.5369	0.6248	0.6895	0.6205	0.7655	0.8153	0.5983	0.8634	0.9561	0.6155	0.7369	0.4736	0.5339	-1.0	-1.0	0.4614	0.5228	0.9159	0.9516	0.6436	0.8783	0.867	0.9951	0.9482	0.9714	0.9574	0.9767	0.0317	0.806	0.6742	0.7409	0.6387	0.9321	0.0037	0.7368	0.8625	0.9115	0.6398	0.7199
4.8446	9.0	7875	6.5165	0.6064	0.734	0.6583	0.5305	0.6055	0.6766	0.6057	0.7479	0.8021	0.5908	0.8441	0.9489	0.598	0.7249	0.4926	0.5547	-1.0	-1.0	0.4304	0.4976	0.9153	0.9478	0.5701	0.8526	0.7636	0.9867	0.9459	0.9684	0.9583	0.9762	0.0271	0.7862	0.6457	0.7131	0.6268	0.9338	0.0014	0.6518	0.8662	0.914	0.6484	0.7213
4.7656	10.0	8750	6.4174	0.6051	0.7291	0.6597	0.5252	0.6131	0.6723	0.6006	0.7415	0.7959	0.5831	0.8299	0.948	0.5841	0.7219	0.4357	0.4955	-1.0	-1.0	0.4363	0.5014	0.9189	0.9512	0.5995	0.8773	0.7881	0.9906	0.9485	0.9752	0.9582	0.9782	0.0257	0.777	0.6432	0.7154	0.5994	0.9274	0.0019	0.5736	0.8665	0.9243	0.6647	0.7338

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-b8-lr3e-5

suas-2025-rtdetr-finetuned-b8-lr3e-5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mfly-auton/
suas-2025-rtdetr-finetuned-b8-lr3e-5