yolo_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: 0.7717
• Map: 0.5525
• Map 50: 0.7824
• Map 75: 0.6327
• Map Small: -1.0
• Map Medium: 0.6003
• Map Large: 0.5621
• Mar 1: 0.426
• Mar 10: 0.7222
• Mar 100: 0.7905
• Mar Small: -1.0
• Mar Medium: 0.7429
• Mar Large: 0.7989
• Map Banana: 0.3928
• Mar 100 Banana: 0.7625
• Map Orange: 0.6098
• Mar 100 Orange: 0.7833
• Map Apple: 0.655
• Mar 100 Apple: 0.8257

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_FUSED 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	1.9380	0.016	0.0435	0.0115	-1.0	0.016	0.0171	0.0793	0.2073	0.3354	-1.0	0.2714	0.3567	0.0218	0.22	0.0082	0.3405	0.0179	0.4457
No log	2.0	120	1.8526	0.0242	0.0722	0.0119	-1.0	0.0605	0.0238	0.1072	0.2713	0.41	-1.0	0.4143	0.412	0.0213	0.3975	0.0234	0.3095	0.0279	0.5229
No log	3.0	180	1.6196	0.0586	0.1588	0.0473	-1.0	0.1189	0.0544	0.1351	0.3404	0.5397	-1.0	0.4286	0.5608	0.0405	0.5025	0.0817	0.4881	0.0534	0.6286
No log	4.0	240	1.5929	0.0536	0.1218	0.0383	-1.0	0.1824	0.043	0.1477	0.3129	0.546	-1.0	0.4143	0.5657	0.0448	0.56	0.0875	0.5095	0.0285	0.5686
No log	5.0	300	1.3573	0.0819	0.1665	0.0657	-1.0	0.2122	0.0812	0.2358	0.4362	0.6319	-1.0	0.5	0.6544	0.0693	0.6125	0.1164	0.6405	0.0599	0.6429
No log	6.0	360	1.1932	0.1107	0.2086	0.1207	-1.0	0.313	0.0902	0.281	0.4692	0.6985	-1.0	0.6	0.7182	0.0946	0.6375	0.1486	0.6952	0.089	0.7629
No log	7.0	420	1.1793	0.123	0.2172	0.1204	-1.0	0.3366	0.0989	0.3102	0.5227	0.6961	-1.0	0.6286	0.711	0.1192	0.645	0.1521	0.7119	0.0978	0.7314
No log	8.0	480	1.0844	0.1883	0.3278	0.2089	-1.0	0.313	0.1801	0.3394	0.5727	0.7158	-1.0	0.6643	0.7289	0.179	0.6425	0.2632	0.7333	0.1227	0.7714
1.4482	9.0	540	0.9866	0.2772	0.4439	0.3057	-1.0	0.4752	0.2575	0.3569	0.6136	0.75	-1.0	0.7286	0.755	0.2406	0.72	0.3876	0.7786	0.2033	0.7514
1.4482	10.0	600	0.9111	0.341	0.5448	0.3872	-1.0	0.4848	0.3308	0.4055	0.6794	0.783	-1.0	0.7	0.7994	0.2931	0.7275	0.4297	0.7929	0.3003	0.8286
1.4482	11.0	660	0.9692	0.3717	0.5948	0.4359	-1.0	0.4423	0.3729	0.3859	0.6671	0.7472	-1.0	0.6357	0.7646	0.3221	0.7525	0.3522	0.7262	0.4407	0.7629
1.4482	12.0	720	0.9162	0.3884	0.6406	0.391	-1.0	0.5098	0.3837	0.3703	0.6892	0.7687	-1.0	0.6643	0.7868	0.3456	0.7475	0.3729	0.7357	0.4468	0.8229
1.4482	13.0	780	0.8469	0.4608	0.683	0.5301	-1.0	0.5791	0.4635	0.3809	0.6968	0.7851	-1.0	0.7643	0.7913	0.2782	0.7425	0.5874	0.8071	0.5167	0.8057
1.4482	14.0	840	0.8398	0.5071	0.742	0.5676	-1.0	0.578	0.5165	0.414	0.6978	0.7728	-1.0	0.7429	0.7804	0.3868	0.7375	0.5574	0.7524	0.577	0.8286
1.4482	15.0	900	0.8211	0.5192	0.7501	0.587	-1.0	0.6218	0.5208	0.4082	0.6898	0.7737	-1.0	0.7714	0.776	0.3558	0.75	0.6106	0.7881	0.5913	0.7829
1.4482	16.0	960	0.8128	0.5002	0.7351	0.5499	-1.0	0.5651	0.5126	0.4029	0.6966	0.7739	-1.0	0.7143	0.7843	0.3691	0.755	0.5311	0.7381	0.6004	0.8286
0.8192	17.0	1020	0.7783	0.5375	0.7587	0.6138	-1.0	0.5915	0.5475	0.4221	0.7278	0.7894	-1.0	0.7	0.8052	0.4066	0.755	0.5867	0.7762	0.6194	0.8371
0.8192	18.0	1080	0.7959	0.5329	0.7604	0.6026	-1.0	0.583	0.5442	0.4208	0.7044	0.7738	-1.0	0.7071	0.7875	0.4035	0.7225	0.5595	0.7762	0.6358	0.8229
0.8192	19.0	1140	0.8141	0.5335	0.7733	0.591	-1.0	0.5825	0.5436	0.4207	0.7054	0.7806	-1.0	0.7214	0.7924	0.4186	0.735	0.5833	0.7667	0.5987	0.84
0.8192	20.0	1200	0.7742	0.5476	0.7894	0.6143	-1.0	0.5959	0.5582	0.4247	0.7173	0.7937	-1.0	0.7429	0.8024	0.4134	0.77	0.6001	0.7881	0.6294	0.8229
0.8192	21.0	1260	0.8096	0.5421	0.7846	0.6333	-1.0	0.6011	0.5492	0.4156	0.7118	0.7878	-1.0	0.7286	0.7971	0.3994	0.77	0.5863	0.7762	0.6406	0.8171
0.8192	22.0	1320	0.7685	0.557	0.778	0.6319	-1.0	0.6363	0.5608	0.4274	0.7287	0.7948	-1.0	0.75	0.8036	0.3914	0.76	0.6228	0.7786	0.6566	0.8457
0.8192	23.0	1380	0.7776	0.5415	0.7635	0.613	-1.0	0.6435	0.5462	0.4183	0.7257	0.7884	-1.0	0.7429	0.7968	0.3848	0.7575	0.6026	0.7762	0.6373	0.8314
0.8192	24.0	1440	0.7899	0.5341	0.7694	0.607	-1.0	0.5907	0.5411	0.4173	0.7189	0.79	-1.0	0.7429	0.7994	0.3784	0.7525	0.6113	0.7833	0.6127	0.8343
0.6113	25.0	1500	0.7790	0.5484	0.7751	0.6214	-1.0	0.6085	0.5562	0.431	0.7232	0.7878	-1.0	0.7286	0.7979	0.3928	0.76	0.6103	0.7833	0.6419	0.82
0.6113	26.0	1560	0.7841	0.5517	0.7809	0.6315	-1.0	0.5902	0.5608	0.4307	0.7286	0.7926	-1.0	0.75	0.8005	0.3955	0.765	0.6239	0.7929	0.6357	0.82
0.6113	27.0	1620	0.7750	0.5509	0.7797	0.6291	-1.0	0.604	0.5605	0.4275	0.7207	0.7921	-1.0	0.7429	0.801	0.3952	0.765	0.6072	0.7857	0.6501	0.8257
0.6113	28.0	1680	0.7722	0.5538	0.7826	0.633	-1.0	0.6046	0.5628	0.426	0.7222	0.7907	-1.0	0.7429	0.7992	0.3945	0.7625	0.6102	0.781	0.6568	0.8286
0.6113	29.0	1740	0.7717	0.553	0.7834	0.6336	-1.0	0.6003	0.5626	0.426	0.7213	0.7905	-1.0	0.7429	0.7989	0.3942	0.7625	0.6096	0.7833	0.6552	0.8257
0.6113	30.0	1800	0.7717	0.5525	0.7824	0.6327	-1.0	0.6003	0.5621	0.426	0.7222	0.7905	-1.0	0.7429	0.7989	0.3928	0.7625	0.6098	0.7833	0.655	0.8257

Framework versions

• Transformers 4.57.6
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2