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	2.1986	0.0068	0.0254	0.0016	-1.0	0.0068	0.0079	0.0246	0.0997	0.2776	-1.0	0.24	0.283	0.0109	0.2575	0.0002	0.0095	0.0092	0.5657
No log	2.0	120	1.9727	0.0088	0.03	0.0036	-1.0	0.0201	0.0089	0.0521	0.1605	0.3185	-1.0	0.26	0.3186	0.0163	0.4325	0.0	0.0	0.0103	0.5229
No log	3.0	180	1.9117	0.0353	0.114	0.0137	-1.0	0.0335	0.0411	0.1015	0.2692	0.4279	-1.0	0.28	0.4458	0.0185	0.415	0.0278	0.3714	0.0596	0.4971
No log	4.0	240	1.6734	0.0659	0.1642	0.0544	-1.0	0.1162	0.0783	0.1647	0.3225	0.4596	-1.0	0.28	0.4787	0.0818	0.485	0.0324	0.2452	0.0836	0.6486
No log	5.0	300	1.3011	0.1225	0.2534	0.1145	-1.0	0.1155	0.156	0.2833	0.4893	0.5985	-1.0	0.42	0.6231	0.0858	0.5575	0.0939	0.5238	0.1879	0.7143
No log	6.0	360	1.2643	0.2057	0.356	0.2293	-1.0	0.2614	0.2286	0.3177	0.5069	0.6091	-1.0	0.4843	0.6289	0.1166	0.5425	0.1363	0.5333	0.3641	0.7514
No log	7.0	420	1.1581	0.281	0.4787	0.2868	-1.0	0.3952	0.2874	0.3263	0.577	0.6758	-1.0	0.54	0.6973	0.139	0.6025	0.2267	0.619	0.4773	0.8057
No log	8.0	480	1.1026	0.3086	0.524	0.3347	-1.0	0.2653	0.3326	0.3576	0.58	0.6648	-1.0	0.5943	0.6766	0.2161	0.615	0.2935	0.631	0.4162	0.7486
1.4697	9.0	540	1.0055	0.3516	0.5724	0.3781	-1.0	0.3764	0.3613	0.3457	0.6023	0.7044	-1.0	0.6629	0.7125	0.2457	0.645	0.3506	0.7024	0.4585	0.7657
1.4697	10.0	600	0.9545	0.4136	0.6261	0.4555	-1.0	0.3712	0.4388	0.3688	0.6483	0.73	-1.0	0.6671	0.7413	0.2924	0.68	0.4384	0.75	0.51	0.76
1.4697	11.0	660	0.9475	0.423	0.6493	0.4547	-1.0	0.5066	0.4345	0.3763	0.662	0.7468	-1.0	0.6429	0.7622	0.2579	0.71	0.456	0.7476	0.5551	0.7829
1.4697	12.0	720	0.9563	0.4131	0.6719	0.4431	-1.0	0.4135	0.4285	0.3598	0.6447	0.7194	-1.0	0.5957	0.7354	0.3076	0.71	0.4745	0.731	0.4573	0.7171
1.4697	13.0	780	0.8893	0.4472	0.6689	0.4985	-1.0	0.4739	0.4567	0.3983	0.6573	0.7334	-1.0	0.6443	0.7447	0.3567	0.735	0.4538	0.7595	0.5309	0.7057
1.4697	14.0	840	0.9049	0.4915	0.7427	0.5237	-1.0	0.415	0.5107	0.3922	0.6898	0.7536	-1.0	0.6529	0.7674	0.3643	0.7375	0.5229	0.7405	0.5872	0.7829
1.4697	15.0	900	0.8799	0.4884	0.7419	0.5376	-1.0	0.4822	0.5042	0.3963	0.6875	0.7565	-1.0	0.6614	0.7686	0.3481	0.7525	0.5076	0.7571	0.6095	0.76
1.4697	16.0	960	0.8778	0.5014	0.7714	0.5549	-1.0	0.5352	0.5127	0.4015	0.6808	0.744	-1.0	0.6329	0.7593	0.3398	0.725	0.5527	0.75	0.6116	0.7571
0.7568	17.0	1020	0.8810	0.5025	0.7664	0.5708	-1.0	0.506	0.5126	0.3919	0.6854	0.7424	-1.0	0.6743	0.7518	0.3768	0.7325	0.5336	0.7405	0.5973	0.7543
0.7568	18.0	1080	0.8716	0.4942	0.7505	0.5653	-1.0	0.4833	0.509	0.3965	0.6756	0.7391	-1.0	0.6357	0.7515	0.374	0.7525	0.5074	0.719	0.6011	0.7457
0.7568	19.0	1140	0.8007	0.5072	0.7516	0.5666	-1.0	0.4698	0.524	0.411	0.7079	0.757	-1.0	0.6486	0.7697	0.3868	0.7625	0.5498	0.7429	0.5849	0.7657
0.7568	20.0	1200	0.8122	0.5502	0.8115	0.594	-1.0	0.4834	0.575	0.4175	0.7223	0.7704	-1.0	0.6486	0.7855	0.436	0.765	0.6078	0.769	0.6067	0.7771
0.7568	21.0	1260	0.8067	0.5387	0.7907	0.5869	-1.0	0.505	0.5602	0.3976	0.72	0.7725	-1.0	0.6486	0.7874	0.3823	0.7775	0.6032	0.7857	0.6306	0.7543
0.7568	22.0	1320	0.8331	0.5408	0.7992	0.5769	-1.0	0.4986	0.5614	0.4017	0.71	0.7596	-1.0	0.6614	0.7726	0.4037	0.745	0.5779	0.7595	0.6408	0.7743
0.7568	23.0	1380	0.8336	0.5386	0.7938	0.5854	-1.0	0.4914	0.56	0.4017	0.713	0.7625	-1.0	0.6657	0.7751	0.3928	0.75	0.5954	0.769	0.6277	0.7686
0.7568	24.0	1440	0.8137	0.5391	0.7978	0.593	-1.0	0.4835	0.5612	0.4081	0.7134	0.7681	-1.0	0.6714	0.7807	0.3796	0.7625	0.6057	0.7762	0.6321	0.7657
0.5523	25.0	1500	0.8126	0.5518	0.8009	0.5998	-1.0	0.4901	0.5745	0.4082	0.7152	0.7745	-1.0	0.6757	0.7869	0.3933	0.7725	0.6199	0.7881	0.6423	0.7629
0.5523	26.0	1560	0.8205	0.5528	0.8115	0.6105	-1.0	0.4859	0.5733	0.4063	0.711	0.7727	-1.0	0.7	0.7819	0.4121	0.77	0.6125	0.7881	0.6338	0.76
0.5523	27.0	1620	0.8211	0.5503	0.8075	0.6082	-1.0	0.4756	0.5729	0.4081	0.7088	0.7748	-1.0	0.7	0.7843	0.4064	0.77	0.6134	0.7857	0.6312	0.7686
0.5523	28.0	1680	0.8223	0.5543	0.8091	0.6061	-1.0	0.4809	0.5771	0.4082	0.7081	0.7758	-1.0	0.6929	0.7862	0.4103	0.7725	0.6136	0.7833	0.639	0.7714
0.5523	29.0	1740	0.8171	0.5531	0.806	0.6037	-1.0	0.4803	0.5755	0.409	0.7106	0.774	-1.0	0.6829	0.7852	0.4113	0.775	0.6079	0.7786	0.6401	0.7686
0.5523	30.0	1800	0.8208	0.5539	0.8071	0.6043	-1.0	0.4804	0.5761	0.409	0.7106	0.7748	-1.0	0.6829	0.7861	0.4114	0.775	0.6102	0.781	0.6401	0.7686

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

yejimene/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yejimene/
yolo_finetuned_fruits