You can deploy this model and start earning money today!

mfly-auton/
suas-2025-rtdetr-finetuned-e10-b32-lr1e-5

Name: suas-2025-rtdetr-finetuned-e10-b32-lr1e-5
Availability: InStock

generated_from_trainer

Views

No views yet

suas-2025-rtdetr-finetuned-e10-b32-lr1e-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: 5.7755
Map: 0.8259
Map 50: 0.9053
Map 75: 0.8962
Map Small: 0.7396
Map Medium: 0.8978
Map Large: 0.8904
Mar 1: 0.7897
Mar 10: 0.9079
Mar 100: 0.911
Mar Small: 0.8045
Mar Medium: 0.9449
Mar Large: 0.974
Map Baseball-bat: 0.8535
Mar 100 Baseball-bat: 0.9223
Map Basketball: 0.8181
Mar 100 Basketball: 0.9213
Map Car: -1.0
Mar 100 Car: -1.0
Map Football: 0.4419
Mar 100 Football: 0.516
Map Human: 0.8513
Mar 100 Human: 0.9473
Map Luggage: 0.8768
Mar 100 Luggage: 0.9398
Map Mattress: 0.5281
Mar 100 Mattress: 0.9959
Map Motorcycle: 0.9311
Mar 100 Motorcycle: 0.9635
Map Skis: 0.9687
Mar 100 Skis: 0.9975
Map Snowboard: 0.9779
Mar 100 Snowboard: 0.9953
Map Soccer-ball: 0.8253
Mar 100 Soccer-ball: 0.8679
Map Stop-sign: 0.9582
Mar 100 Stop-sign: 0.9838
Map Tennis-racket: 0.8887
Mar 100 Tennis-racket: 0.9275
Map Umbrella: 0.8417
Mar 100 Umbrella: 0.9128
Map Volleyball: 0.8015
Mar 100 Volleyball: 0.8632

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: 32
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
31.6046	1.0	219	12.5434	0.3553	0.4457	0.3805	0.2717	0.4559	0.4479	0.5615	0.7432	0.7718	0.5413	0.873	0.9496	0.6878	0.9023	0.0337	0.5289	-1.0	-1.0	0.1746	0.4829	0.4718	0.8344	0.1939	0.8512	0.139	0.9643	0.5451	0.9288	0.9133	0.9891	0.4051	0.9817	0.3034	0.4481	0.3931	0.9455	0.3464	0.615	0.2395	0.8765	0.1274	0.4561
11.2376	2.0	438	8.7086	0.7492	0.8518	0.8382	0.6809	0.8229	0.7802	0.7521	0.8824	0.8898	0.7682	0.9257	0.9652	0.8225	0.9069	0.7335	0.8417	-1.0	-1.0	0.5246	0.6187	0.8272	0.9075	0.8351	0.91	0.2917	0.9978	0.8814	0.9449	0.9763	0.9916	0.9795	0.9963	0.7605	0.8211	0.9176	0.9746	0.8631	0.9088	0.4335	0.9109	0.6428	0.7265
7.5378	3.0	657	7.5895	0.7926	0.8861	0.874	0.7165	0.8732	0.8452	0.7772	0.8976	0.9033	0.7944	0.9368	0.9808	0.8325	0.9096	0.7675	0.8908	-1.0	-1.0	0.5341	0.6171	0.8536	0.9323	0.8621	0.9288	0.3232	0.9995	0.9099	0.9534	0.9589	0.9851	0.9779	0.9968	0.7949	0.8422	0.9557	0.9799	0.874	0.9187	0.7466	0.908	0.7061	0.7833
6.7821	4.0	876	6.9548	0.7906	0.8773	0.8662	0.6944	0.8658	0.8692	0.7707	0.8882	0.8931	0.763	0.9347	0.9677	0.8386	0.9153	0.738	0.8742	-1.0	-1.0	0.4264	0.5058	0.83	0.9254	0.8657	0.9297	0.4204	0.9995	0.9153	0.9537	0.955	0.9891	0.9727	0.9924	0.8047	0.8542	0.9495	0.9869	0.8817	0.9254	0.7954	0.8976	0.6753	0.7549
6.4517	5.0	1095	6.2876	0.8217	0.9052	0.8965	0.7345	0.8867	0.884	0.7885	0.9066	0.9107	0.8055	0.9448	0.9705	0.8384	0.92	0.7994	0.9156	-1.0	-1.0	0.4948	0.5703	0.8404	0.9363	0.8765	0.9391	0.5335	0.9946	0.927	0.9651	0.9504	0.995	0.9766	0.9947	0.8183	0.8602	0.946	0.9813	0.8881	0.9259	0.8387	0.9205	0.7759	0.8306
6.1454	6.0	1314	5.8688	0.8355	0.9186	0.9086	0.7416	0.8939	0.9026	0.7975	0.91	0.9135	0.8094	0.9477	0.973	0.8577	0.9237	0.7904	0.9206	-1.0	-1.0	0.4838	0.5638	0.8723	0.9423	0.8782	0.9378	0.6114	0.9982	0.9296	0.9638	0.9826	0.9965	0.9772	0.9953	0.8218	0.8638	0.9541	0.9866	0.887	0.9285	0.851	0.9228	0.7993	0.8458
6.0567	7.0	1533	5.9641	0.8278	0.9084	0.9005	0.7434	0.8944	0.89	0.7917	0.9098	0.9135	0.8091	0.9467	0.9854	0.8531	0.9224	0.7819	0.9187	-1.0	-1.0	0.4727	0.5437	0.8434	0.9498	0.8774	0.9406	0.5438	0.9946	0.9232	0.963	0.9791	0.998	0.9771	0.994	0.8241	0.8707	0.9609	0.9785	0.8909	0.9264	0.8533	0.9254	0.8084	0.863
5.9579	8.0	1752	5.7184	0.8339	0.9141	0.9056	0.7448	0.8939	0.9004	0.7947	0.9099	0.9136	0.8088	0.9453	0.9871	0.8529	0.9222	0.8083	0.9175	-1.0	-1.0	0.4795	0.5478	0.8608	0.9444	0.8795	0.9406	0.5759	0.998	0.9325	0.9641	0.9711	0.9975	0.9816	0.9964	0.8218	0.8661	0.9655	0.9872	0.8942	0.928	0.8474	0.9251	0.8034	0.8551
5.9419	9.0	1971	5.7328	0.8305	0.9091	0.9018	0.7434	0.8955	0.8956	0.7924	0.9098	0.9136	0.8101	0.9462	0.9868	0.858	0.9253	0.8223	0.9197	-1.0	-1.0	0.4625	0.5377	0.8502	0.9471	0.8804	0.9409	0.5395	0.9939	0.93	0.9619	0.9787	0.9975	0.979	0.9959	0.8296	0.8704	0.9624	0.988	0.8852	0.9269	0.8393	0.9164	0.8101	0.8684
5.9176	10.0	2190	5.7755	0.8259	0.9053	0.8962	0.7396	0.8978	0.8904	0.7897	0.9079	0.911	0.8045	0.9449	0.974	0.8535	0.9223	0.8181	0.9213	-1.0	-1.0	0.4419	0.516	0.8513	0.9473	0.8768	0.9398	0.5281	0.9959	0.9311	0.9635	0.9687	0.9975	0.9779	0.9953	0.8253	0.8679	0.9582	0.9838	0.8887	0.9275	0.8417	0.9128	0.8015	0.8632

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-e10-b32-lr1e-5

suas-2025-rtdetr-finetuned-e10-b32-lr1e-5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mfly-auton/
suas-2025-rtdetr-finetuned-e10-b32-lr1e-5