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	51	2.1113	0.038	0.1264	0.0254	-1.0	0.0152	0.0397	0.118	0.233	0.3606	-1.0	0.3	0.3503	0.0105	0.4217	0.0032	0.1	0.1003	0.56
No log	2.0	102	1.9366	0.0804	0.1992	0.0599	-1.0	0.0576	0.081	0.1306	0.3402	0.4382	-1.0	0.3	0.4355	0.0459	0.487	0.0672	0.2344	0.1282	0.5933
No log	3.0	153	1.7885	0.0841	0.1986	0.0721	-1.0	0.1156	0.0828	0.1999	0.3487	0.5154	-1.0	0.275	0.5201	0.0602	0.487	0.0742	0.4125	0.118	0.6467
No log	4.0	204	1.6456	0.0818	0.2278	0.0405	-1.0	0.1556	0.0783	0.1699	0.3756	0.5287	-1.0	0.35	0.5273	0.1239	0.5	0.0511	0.4594	0.0702	0.6267
No log	5.0	255	1.4538	0.1061	0.2066	0.0885	-1.0	0.2048	0.1046	0.2642	0.4234	0.596	-1.0	0.45	0.5988	0.0709	0.5391	0.1278	0.6156	0.1195	0.6333
No log	6.0	306	1.3033	0.1226	0.2457	0.1008	-1.0	0.2186	0.1238	0.2775	0.4809	0.6454	-1.0	0.325	0.6635	0.0607	0.5522	0.1506	0.6906	0.1566	0.6933
No log	7.0	357	1.2163	0.1845	0.3681	0.1844	-1.0	0.2351	0.1864	0.3169	0.524	0.6629	-1.0	0.525	0.6619	0.1636	0.6087	0.2205	0.7	0.1695	0.68
No log	8.0	408	1.1261	0.2555	0.4617	0.2608	-1.0	0.2281	0.2538	0.3458	0.559	0.6897	-1.0	0.4625	0.6974	0.211	0.6478	0.2179	0.6812	0.3376	0.74
No log	9.0	459	1.1689	0.2369	0.4665	0.1768	-1.0	0.2948	0.2341	0.325	0.54	0.6701	-1.0	0.5375	0.6763	0.1366	0.6391	0.2389	0.6844	0.3352	0.6867
1.2827	10.0	510	1.0391	0.2843	0.4593	0.299	-1.0	0.3678	0.2793	0.3545	0.6148	0.7217	-1.0	0.6125	0.7227	0.2561	0.7087	0.21	0.7031	0.3867	0.7533
1.2827	11.0	561	1.1143	0.3464	0.5596	0.3491	-1.0	0.2567	0.3484	0.3542	0.5879	0.6894	-1.0	0.4625	0.7007	0.2226	0.6304	0.2816	0.6844	0.535	0.7533
1.2827	12.0	612	1.0410	0.4142	0.6463	0.4997	-1.0	0.2565	0.426	0.374	0.6478	0.7071	-1.0	0.4625	0.7185	0.337	0.6696	0.3585	0.6719	0.5473	0.78
1.2827	13.0	663	1.0514	0.4352	0.7038	0.4321	-1.0	0.3149	0.4424	0.3793	0.6192	0.6992	-1.0	0.525	0.702	0.3228	0.6696	0.4402	0.6812	0.5424	0.7467
1.2827	14.0	714	1.0254	0.459	0.7539	0.4564	-1.0	0.309	0.4619	0.3642	0.6436	0.7082	-1.0	0.5	0.7144	0.3437	0.6696	0.4134	0.675	0.6198	0.78
1.2827	15.0	765	1.0033	0.4827	0.7599	0.4897	-1.0	0.3646	0.486	0.3869	0.6537	0.732	-1.0	0.6	0.7299	0.353	0.6957	0.5115	0.7469	0.5835	0.7533
1.2827	16.0	816	1.0524	0.4788	0.7562	0.4848	-1.0	0.3124	0.4874	0.3726	0.6208	0.6875	-1.0	0.55	0.6901	0.3749	0.6478	0.4786	0.6812	0.5831	0.7333
1.2827	17.0	867	0.9603	0.5241	0.7913	0.5728	-1.0	0.3757	0.5295	0.3972	0.6586	0.7357	-1.0	0.5625	0.7377	0.3772	0.6826	0.555	0.7312	0.6399	0.7933
1.2827	18.0	918	1.0032	0.5148	0.7938	0.5793	-1.0	0.3829	0.5192	0.3938	0.664	0.7311	-1.0	0.5625	0.7358	0.4001	0.6913	0.5227	0.7219	0.6218	0.78
1.2827	19.0	969	0.9784	0.5131	0.7778	0.5701	-1.0	0.3772	0.5172	0.3868	0.6578	0.7229	-1.0	0.5625	0.7258	0.3854	0.6609	0.5034	0.7344	0.6504	0.7733
0.7025	20.0	1020	0.9793	0.5199	0.7867	0.5552	-1.0	0.3706	0.5231	0.4052	0.6594	0.7171	-1.0	0.5625	0.7212	0.3748	0.6435	0.5489	0.7344	0.6361	0.7733
0.7025	21.0	1071	0.9631	0.5451	0.8064	0.6305	-1.0	0.3781	0.5488	0.4036	0.6729	0.7185	-1.0	0.575	0.7188	0.4038	0.6478	0.5675	0.7344	0.6639	0.7733
0.7025	22.0	1122	0.9492	0.5393	0.8131	0.5715	-1.0	0.3817	0.5416	0.3952	0.6795	0.7267	-1.0	0.5875	0.7271	0.4043	0.6783	0.5624	0.7219	0.6513	0.78
0.7025	23.0	1173	0.9811	0.554	0.8336	0.6016	-1.0	0.3974	0.5566	0.401	0.6734	0.7319	-1.0	0.5875	0.7342	0.4171	0.6652	0.5681	0.7437	0.6767	0.7867
0.7025	24.0	1224	0.9875	0.5297	0.8036	0.5667	-1.0	0.3648	0.5323	0.3938	0.6736	0.7253	-1.0	0.575	0.7285	0.3693	0.6522	0.5519	0.7437	0.668	0.78
0.7025	25.0	1275	0.9913	0.5454	0.827	0.5871	-1.0	0.3784	0.5485	0.3956	0.6725	0.7298	-1.0	0.575	0.7337	0.4081	0.6652	0.5546	0.7375	0.6734	0.7867
0.7025	26.0	1326	0.9613	0.548	0.8265	0.592	-1.0	0.4083	0.5493	0.3956	0.675	0.7301	-1.0	0.575	0.732	0.4166	0.6696	0.5536	0.7406	0.6737	0.78
0.7025	27.0	1377	0.9535	0.5463	0.8271	0.5968	-1.0	0.4049	0.5483	0.3934	0.6851	0.7327	-1.0	0.575	0.7364	0.4161	0.6609	0.5554	0.7437	0.6674	0.7933
0.7025	28.0	1428	0.9766	0.5498	0.8281	0.5912	-1.0	0.3799	0.5535	0.3956	0.675	0.7302	-1.0	0.5875	0.7326	0.4215	0.6696	0.5514	0.7344	0.6763	0.7867
0.7025	29.0	1479	0.9742	0.5507	0.828	0.5919	-1.0	0.3874	0.5538	0.3956	0.6764	0.7302	-1.0	0.5875	0.7326	0.4236	0.6696	0.5524	0.7344	0.6762	0.7867
0.5377	30.0	1530	0.9733	0.5513	0.8285	0.5921	-1.0	0.3882	0.5543	0.3956	0.6764	0.7327	-1.0	0.6125	0.7344	0.4244	0.6739	0.5532	0.7375	0.6762	0.7867

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

advecino/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

advecino/
yolo_finetuned_fruits