detr_finetuned_fruits

This model is a fine-tuned version of hustvl/yolos-tiny on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8626
Map: 0.5447
Map 50: 0.8282
Map 75: 0.5821
Map Small: -1.0
Map Medium: 0.4675
Map Large: 0.5734
Mar 1: 0.4327
Mar 10: 0.7017
Mar 100: 0.7589
Mar Small: -1.0
Mar Medium: 0.6514
Mar Large: 0.7795
Map Banana: 0.4399
Mar 100 Banana: 0.72
Map Orange: 0.541
Mar 100 Orange: 0.7738
Map Apple: 0.6532
Mar 100 Apple: 0.7829

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: 4
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: 30

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 Banana	Mar 100 Banana	Map Orange	Mar 100 Orange	Map Apple	Mar 100 Apple
No log	1.0	60	2.2905	0.008	0.0222	0.006	-1.0	0.0061	0.012	0.0871	0.1937	0.303	-1.0	0.2429	0.3256	0.0066	0.15	0.0042	0.4048	0.0133	0.3543
No log	2.0	120	1.9265	0.0202	0.0629	0.0071	-1.0	0.119	0.023	0.091	0.236	0.396	-1.0	0.3243	0.4106	0.0238	0.415	0.0256	0.4071	0.0111	0.3657
No log	3.0	180	1.8221	0.0309	0.0731	0.0214	-1.0	0.082	0.035	0.0877	0.241	0.4251	-1.0	0.4071	0.4275	0.0504	0.49	0.0302	0.4738	0.0121	0.3114
No log	4.0	240	1.7172	0.0253	0.0655	0.0111	-1.0	0.0988	0.0251	0.1424	0.258	0.4915	-1.0	0.4371	0.502	0.0303	0.5225	0.0273	0.4548	0.0183	0.4971
No log	5.0	300	1.5541	0.0472	0.1085	0.0305	-1.0	0.0639	0.0526	0.1869	0.3652	0.5653	-1.0	0.4014	0.5933	0.0326	0.535	0.0777	0.6095	0.0313	0.5514
No log	6.0	360	1.5159	0.0501	0.1145	0.0436	-1.0	0.0694	0.0556	0.2009	0.3976	0.5542	-1.0	0.38	0.5799	0.0659	0.5725	0.0527	0.5071	0.0318	0.5829
No log	7.0	420	1.4185	0.0775	0.1777	0.0662	-1.0	0.2007	0.0751	0.2078	0.4237	0.5944	-1.0	0.5071	0.6137	0.0647	0.585	0.1071	0.5952	0.0608	0.6029
No log	8.0	480	1.2902	0.0965	0.189	0.077	-1.0	0.1555	0.1161	0.2715	0.4469	0.64	-1.0	0.5186	0.66	0.0726	0.62	0.1498	0.6286	0.0673	0.6714
1.5459	9.0	540	1.2497	0.1052	0.2137	0.1115	-1.0	0.2298	0.1295	0.294	0.4625	0.6662	-1.0	0.4914	0.6987	0.0749	0.6025	0.1614	0.6905	0.0794	0.7057
1.5459	10.0	600	1.0677	0.141	0.2485	0.1427	-1.0	0.2822	0.1552	0.3656	0.5481	0.7142	-1.0	0.6257	0.7329	0.0819	0.6475	0.2168	0.7238	0.1242	0.7714
1.5459	11.0	660	1.0572	0.1813	0.3134	0.1988	-1.0	0.2859	0.2008	0.3533	0.5777	0.7017	-1.0	0.5886	0.72	0.1098	0.665	0.2983	0.7143	0.136	0.7257
1.5459	12.0	720	1.0403	0.247	0.4247	0.2529	-1.0	0.3598	0.2663	0.348	0.5748	0.7021	-1.0	0.6286	0.7157	0.1359	0.67	0.3934	0.7333	0.2115	0.7029
1.5459	13.0	780	0.9933	0.3205	0.5352	0.3708	-1.0	0.3999	0.3373	0.3908	0.6208	0.7248	-1.0	0.6086	0.7447	0.1991	0.68	0.3998	0.7429	0.3626	0.7514
1.5459	14.0	840	1.0158	0.3865	0.6502	0.4208	-1.0	0.3726	0.4172	0.3843	0.6447	0.7184	-1.0	0.5557	0.7445	0.2549	0.6875	0.4506	0.7333	0.454	0.7343
1.5459	15.0	900	0.9649	0.4519	0.6973	0.4866	-1.0	0.4641	0.4712	0.395	0.6727	0.7373	-1.0	0.6357	0.7575	0.2713	0.67	0.5052	0.7619	0.5792	0.78
1.5459	16.0	960	0.9148	0.491	0.7552	0.5358	-1.0	0.4674	0.5169	0.4167	0.6903	0.7571	-1.0	0.6686	0.7776	0.3438	0.69	0.5616	0.7786	0.5676	0.8029
0.864	17.0	1020	0.8861	0.5232	0.7871	0.571	-1.0	0.5199	0.5463	0.4387	0.6948	0.7541	-1.0	0.68	0.771	0.4007	0.7	0.5659	0.7595	0.6029	0.8029
0.864	18.0	1080	0.8914	0.5014	0.7661	0.5433	-1.0	0.4449	0.5276	0.4245	0.6954	0.7655	-1.0	0.6286	0.79	0.4006	0.715	0.4992	0.7643	0.6043	0.8171
0.864	19.0	1140	0.8886	0.5223	0.7763	0.5611	-1.0	0.4595	0.5492	0.4201	0.6893	0.7473	-1.0	0.6143	0.7716	0.4002	0.69	0.5387	0.769	0.6279	0.7829
0.864	20.0	1200	0.8973	0.5239	0.8057	0.5726	-1.0	0.4437	0.5531	0.4317	0.6917	0.7535	-1.0	0.6371	0.7758	0.4343	0.7125	0.5406	0.7738	0.5966	0.7743
0.864	21.0	1260	0.8740	0.5355	0.8126	0.5889	-1.0	0.4869	0.5605	0.4162	0.7055	0.7633	-1.0	0.6314	0.7856	0.4039	0.7375	0.5735	0.7667	0.6292	0.7857
0.864	22.0	1320	0.8917	0.5212	0.7944	0.5517	-1.0	0.4609	0.549	0.423	0.6872	0.7421	-1.0	0.61	0.7657	0.4232	0.7	0.5315	0.769	0.609	0.7571
0.864	23.0	1380	0.8508	0.5508	0.8362	0.6164	-1.0	0.4879	0.5786	0.4278	0.6983	0.753	-1.0	0.6614	0.7723	0.4453	0.71	0.5576	0.769	0.6494	0.78
0.864	24.0	1440	0.8769	0.5586	0.8358	0.6156	-1.0	0.4846	0.5886	0.4471	0.7105	0.765	-1.0	0.6586	0.787	0.4598	0.705	0.5588	0.7786	0.6572	0.8114
0.638	25.0	1500	0.8670	0.5394	0.8271	0.5786	-1.0	0.4681	0.5667	0.425	0.7004	0.7563	-1.0	0.6514	0.7771	0.4333	0.7075	0.5426	0.7786	0.6422	0.7829
0.638	26.0	1560	0.8487	0.5557	0.8355	0.6103	-1.0	0.4903	0.5829	0.4353	0.709	0.7612	-1.0	0.6586	0.7812	0.4483	0.715	0.559	0.7857	0.6596	0.7829
0.638	27.0	1620	0.8585	0.5484	0.8267	0.5888	-1.0	0.4735	0.5755	0.4318	0.7106	0.7646	-1.0	0.6586	0.7848	0.4431	0.7225	0.5435	0.7857	0.6587	0.7857
0.638	28.0	1680	0.8668	0.5479	0.8262	0.5865	-1.0	0.471	0.5762	0.4318	0.7051	0.763	-1.0	0.6586	0.7831	0.4414	0.72	0.5465	0.7833	0.6556	0.7857
0.638	29.0	1740	0.8631	0.5459	0.8282	0.5962	-1.0	0.4737	0.5737	0.4319	0.7011	0.7598	-1.0	0.6586	0.7795	0.4394	0.72	0.5405	0.7738	0.6579	0.7857
0.638	30.0	1800	0.8626	0.5447	0.8282	0.5821	-1.0	0.4675	0.5734	0.4327	0.7017	0.7589	-1.0	0.6514	0.7795	0.4399	0.72	0.541	0.7738	0.6532	0.7829

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

joheras/detr_finetuned_fruits

detr_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种