law-game-evidence-replacement-finetune

This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.5533
• Map: 0.9339
• Map 50: 0.9616
• Map 75: 0.9575
• Map Small: 0.5574
• Map Medium: 0.9423
• Map Large: 0.9699
• Mar 1: 0.6597
• Mar 10: 0.9522
• Mar 100: 0.9722
• Mar Small: 0.7411
• Mar Medium: 0.9806
• Mar Large: 0.9908
• Map Evidence: -1.0
• Mar 100 Evidence: -1.0
• Map Ambulance: 0.9802
• Mar 100 Ambulance: 0.9899
• Map Artificial Target: 0.9245
• Mar 100 Artificial Target: 0.9611
• Map Cartridge: 0.9759
• Mar 100 Cartridge: 0.9937
• Map Gun: 0.9225
• Mar 100 Gun: 0.9542
• Map Knife: 0.8562
• Mar 100 Knife: 0.9404
• Map Police: 0.9495
• Mar 100 Police: 0.999
• Map Traffic Cone: 0.9285
• Mar 100 Traffic Cone: 0.9673

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 25

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 Evidence	Mar 100 Evidence	Map Ambulance	Mar 100 Ambulance	Map Artificial Target	Mar 100 Artificial Target	Map Cartridge	Mar 100 Cartridge	Map Gun	Mar 100 Gun	Map Knife	Mar 100 Knife	Map Police	Mar 100 Police	Map Traffic Cone	Mar 100 Traffic Cone
No log	1.0	183	17.1925	0.553	0.61	0.584	0.1918	0.3235	0.6555	0.5467	0.8763	0.8964	0.3142	0.8206	0.9705	-1.0	-1.0	0.9057	0.9848	0.5233	0.7299	0.9125	0.9647	0.1841	0.9194	0.6003	0.8687	0.518	0.9286	0.2268	0.8789
No log	2.0	366	7.1301	0.7763	0.8536	0.8146	0.2855	0.6006	0.875	0.6198	0.9116	0.9359	0.62	0.876	0.9781	-1.0	-1.0	0.9418	0.9707	0.7052	0.8648	0.9529	0.9733	0.5436	0.9667	0.7831	0.9172	0.8516	0.9398	0.656	0.9191
37.3669	3.0	549	5.7075	0.848	0.9115	0.8936	0.3256	0.7543	0.9317	0.6317	0.9274	0.9486	0.6783	0.9289	0.9849	-1.0	-1.0	0.9687	0.9879	0.7575	0.8761	0.9619	0.9822	0.8187	0.9653	0.8076	0.9172	0.9181	0.9827	0.7032	0.9287
37.3669	4.0	732	5.8395	0.8232	0.8809	0.8653	0.3221	0.7104	0.8994	0.642	0.9362	0.9536	0.688	0.9333	0.9884	-1.0	-1.0	0.9718	0.9899	0.8061	0.8878	0.9676	0.9854	0.8731	0.9778	0.7678	0.9162	0.6454	0.9867	0.7303	0.9317
37.3669	5.0	915	5.2081	0.8722	0.924	0.9156	0.3818	0.7789	0.951	0.6457	0.9406	0.9593	0.6963	0.9663	0.9887	-1.0	-1.0	0.976	0.9899	0.8077	0.9071	0.973	0.9869	0.7967	0.9611	0.8391	0.9313	0.8822	0.9908	0.8309	0.9482
4.4127	6.0	1098	5.4515	0.8848	0.9339	0.9262	0.5118	0.8295	0.9572	0.6538	0.9446	0.9624	0.6997	0.9621	0.9903	-1.0	-1.0	0.9686	0.9889	0.7937	0.9057	0.9784	0.9886	0.8982	0.9722	0.8491	0.9434	0.8521	0.9888	0.8534	0.9495
4.4127	7.0	1281	4.9756	0.9019	0.9468	0.9396	0.5037	0.8805	0.9631	0.6476	0.9443	0.9666	0.703	0.9692	0.9932	-1.0	-1.0	0.9754	0.9889	0.821	0.9129	0.9753	0.9907	0.9017	0.9833	0.8054	0.9414	0.9414	0.9949	0.8933	0.9541
4.4127	8.0	1464	4.4998	0.9119	0.9554	0.9432	0.5098	0.9047	0.9619	0.6482	0.9436	0.9641	0.7091	0.9786	0.9876	-1.0	-1.0	0.9726	0.9899	0.869	0.9248	0.9738	0.9902	0.8528	0.9556	0.8667	0.9384	0.9631	0.9939	0.8853	0.9561
3.3287	9.0	1647	4.5378	0.9107	0.9472	0.9424	0.5347	0.91	0.9625	0.6551	0.9498	0.9694	0.7243	0.984	0.9908	-1.0	-1.0	0.9802	0.9899	0.8691	0.9281	0.9785	0.9939	0.9128	0.9736	0.8646	0.9424	0.8889	0.9929	0.8805	0.965
3.3287	10.0	1830	5.0033	0.8831	0.9264	0.9202	0.5206	0.8887	0.9369	0.6497	0.9456	0.9661	0.7104	0.9714	0.9895	-1.0	-1.0	0.9404	0.9899	0.8686	0.929	0.9741	0.9917	0.92	0.9722	0.8131	0.9323	0.7768	0.9908	0.889	0.9568
2.8465	11.0	2013	4.1896	0.9183	0.9522	0.9491	0.4507	0.8926	0.9704	0.6595	0.9497	0.9676	0.7033	0.9677	0.9884	-1.0	-1.0	0.9786	0.9899	0.8902	0.9333	0.9745	0.993	0.9182	0.9583	0.8633	0.9424	0.9004	0.9929	0.9031	0.963
2.8465	12.0	2196	4.3806	0.9118	0.9486	0.9445	0.5313	0.8959	0.9574	0.6545	0.9487	0.9701	0.688	0.9741	0.9929	-1.0	-1.0	0.9791	0.9899	0.8856	0.935	0.9736	0.9924	0.9151	0.975	0.8429	0.9384	0.8852	0.998	0.9008	0.9617
2.8465	13.0	2379	4.3575	0.9131	0.9471	0.9419	0.5419	0.9126	0.9643	0.6576	0.9531	0.9717	0.7239	0.9875	0.9909	-1.0	-1.0	0.9677	0.9899	0.8731	0.9358	0.9774	0.9951	0.9226	0.9708	0.8794	0.9545	0.8601	0.9939	0.9114	0.9617
2.5085	14.0	2562	4.0609	0.9277	0.9619	0.9539	0.5802	0.9195	0.9659	0.6566	0.9518	0.9703	0.7168	0.9791	0.9913	-1.0	-1.0	0.9697	0.9899	0.902	0.9451	0.9819	0.9958	0.9273	0.9667	0.8392	0.9374	0.954	0.9939	0.9199	0.9634
2.5085	15.0	2745	4.2034	0.9284	0.961	0.9559	0.541	0.9483	0.9743	0.6606	0.9502	0.9695	0.7169	0.9792	0.9902	-1.0	-1.0	0.979	0.9899	0.9022	0.9469	0.9781	0.9947	0.9251	0.9611	0.8495	0.9404	0.9481	0.9918	0.9167	0.9614
2.5085	16.0	2928	4.1849	0.9283	0.9599	0.9559	0.5591	0.9323	0.9644	0.6575	0.9493	0.9697	0.7267	0.9795	0.988	-1.0	-1.0	0.9716	0.9899	0.9033	0.948	0.9754	0.9949	0.9108	0.9583	0.8469	0.9404	0.9675	0.9929	0.9222	0.9634
2.2183	17.0	3111	4.0696	0.9222	0.9556	0.9503	0.5517	0.9288	0.9634	0.6572	0.9523	0.9726	0.7348	0.9863	0.992	-1.0	-1.0	0.9707	0.9899	0.9052	0.9496	0.9784	0.9949	0.9309	0.9694	0.8074	0.9465	0.9525	0.9959	0.91	0.962
2.2183	18.0	3294	4.3283	0.9126	0.9461	0.9414	0.5422	0.9246	0.9498	0.6564	0.9502	0.9698	0.7138	0.9805	0.9896	-1.0	-1.0	0.9723	0.9899	0.901	0.9483	0.9815	0.9952	0.9204	0.9528	0.7964	0.9394	0.9043	0.9969	0.9125	0.966
2.2183	19.0	3477	3.7839	0.9209	0.9518	0.9477	0.5608	0.9475	0.9583	0.6562	0.9512	0.9701	0.7414	0.9806	0.9885	-1.0	-1.0	0.9566	0.9899	0.9131	0.9531	0.9779	0.9949	0.9132	0.9486	0.833	0.9404	0.9407	0.9959	0.9117	0.9677
2.009	20.0	3660	3.7275	0.9287	0.958	0.9542	0.5558	0.9078	0.9681	0.6586	0.951	0.9709	0.7422	0.979	0.9892	-1.0	-1.0	0.9802	0.9899	0.9239	0.96	0.9761	0.9944	0.9222	0.9514	0.8345	0.9364	0.9389	0.998	0.9248	0.966
2.009	21.0	3843	3.8496	0.93	0.9592	0.9554	0.5552	0.9187	0.9664	0.6581	0.9508	0.9708	0.7373	0.9788	0.9904	-1.0	-1.0	0.9802	0.9899	0.9148	0.9565	0.9778	0.9949	0.9227	0.9556	0.853	0.9364	0.9443	1.0	0.9167	0.9624
1.8494	22.0	4026	3.6452	0.9309	0.9592	0.9551	0.5561	0.929	0.9664	0.6595	0.9495	0.9709	0.723	0.9801	0.9902	-1.0	-1.0	0.9802	0.9899	0.9176	0.9593	0.9764	0.9935	0.9212	0.95	0.8459	0.9374	0.9471	0.999	0.928	0.9673
1.8494	23.0	4209	3.6352	0.9299	0.9587	0.9546	0.5524	0.9155	0.9681	0.659	0.9509	0.9708	0.7217	0.98	0.9902	-1.0	-1.0	0.9802	0.9899	0.9175	0.9589	0.9756	0.9935	0.9217	0.9514	0.8458	0.9364	0.9448	0.999	0.9236	0.9667
1.8494	24.0	4392	3.6526	0.9298	0.9577	0.9535	0.5572	0.9119	0.9666	0.6593	0.9518	0.972	0.725	0.9794	0.9911	-1.0	-1.0	0.9802	0.9899	0.9229	0.9609	0.976	0.9934	0.9217	0.9542	0.8493	0.9394	0.936	1.0	0.9226	0.9663
1.7025	25.0	4575	3.5533	0.9339	0.9616	0.9575	0.5574	0.9423	0.9699	0.6597	0.9522	0.9722	0.7411	0.9806	0.9908	-1.0	-1.0	0.9802	0.9899	0.9245	0.9611	0.9759	0.9937	0.9225	0.9542	0.8562	0.9404	0.9495	0.999	0.9285	0.9673

Framework versions

• Transformers 4.44.0.dev0
• Pytorch 2.3.1+cu121
• Tokenizers 0.19.1