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	1.9443	0.0119	0.0459	0.0041	-1.0	-1.0	0.0123	0.0919	0.2188	0.2941	-1.0	-1.0	0.2941	0.0059	0.2458	0.008	0.0864	0.0219	0.55
No log	2.0	102	1.7202	0.0417	0.1218	0.0196	-1.0	-1.0	0.0428	0.097	0.2529	0.4061	-1.0	-1.0	0.4061	0.0208	0.5208	0.0576	0.1364	0.0467	0.5611
No log	3.0	153	1.6799	0.0678	0.1881	0.0277	-1.0	-1.0	0.0701	0.1469	0.3521	0.4684	-1.0	-1.0	0.4684	0.1057	0.5542	0.0496	0.3455	0.0483	0.5056
No log	4.0	204	1.6484	0.0564	0.147	0.0367	-1.0	-1.0	0.0564	0.1572	0.317	0.5527	-1.0	-1.0	0.5527	0.0317	0.5792	0.0845	0.4955	0.0529	0.5833
No log	5.0	255	1.3363	0.0932	0.2239	0.0612	-1.0	-1.0	0.0936	0.2441	0.4496	0.6466	-1.0	-1.0	0.6466	0.0607	0.6792	0.1239	0.5773	0.095	0.6833
No log	6.0	306	1.3289	0.1105	0.2221	0.0882	-1.0	-1.0	0.1117	0.2606	0.5598	0.6714	-1.0	-1.0	0.6714	0.086	0.6542	0.1146	0.6045	0.1308	0.7556
No log	7.0	357	1.1364	0.1642	0.2929	0.1786	-1.0	-1.0	0.1644	0.3322	0.5712	0.7046	-1.0	-1.0	0.7046	0.1434	0.6583	0.1797	0.7	0.1696	0.7556
No log	8.0	408	1.0330	0.186	0.301	0.207	-1.0	-1.0	0.1862	0.3302	0.5814	0.7411	-1.0	-1.0	0.7411	0.1548	0.6875	0.1781	0.7636	0.2251	0.7722
No log	9.0	459	1.0043	0.2741	0.4125	0.3051	-1.0	-1.0	0.2744	0.3831	0.6205	0.7471	-1.0	-1.0	0.7471	0.2052	0.6792	0.1992	0.7455	0.4178	0.8167
1.4201	10.0	510	0.9797	0.3217	0.4933	0.3881	-1.0	-1.0	0.3247	0.4298	0.6814	0.7416	-1.0	-1.0	0.7416	0.2604	0.7208	0.2853	0.7318	0.4195	0.7722
1.4201	11.0	561	0.9950	0.4103	0.6018	0.4659	-1.0	-1.0	0.4106	0.4502	0.6841	0.7165	-1.0	-1.0	0.7165	0.3125	0.6375	0.3432	0.7455	0.5751	0.7667
1.4201	12.0	612	0.8610	0.4386	0.5667	0.5361	-1.0	-1.0	0.4458	0.4996	0.7475	0.8052	-1.0	-1.0	0.8052	0.3483	0.7333	0.3092	0.8045	0.6582	0.8778
1.4201	13.0	663	0.7924	0.5139	0.7024	0.5937	-1.0	-1.0	0.5276	0.5042	0.7633	0.7989	-1.0	-1.0	0.7989	0.4	0.7458	0.4121	0.7955	0.7294	0.8556
1.4201	14.0	714	0.7932	0.5334	0.7221	0.6197	-1.0	-1.0	0.5335	0.4853	0.7604	0.8013	-1.0	-1.0	0.8013	0.3816	0.7333	0.4812	0.7818	0.7375	0.8889
1.4201	15.0	765	0.7750	0.5639	0.7899	0.6464	-1.0	-1.0	0.5644	0.4742	0.7447	0.7994	-1.0	-1.0	0.7994	0.4551	0.7583	0.5393	0.7955	0.6972	0.8444
1.4201	16.0	816	0.7809	0.6135	0.835	0.7392	-1.0	-1.0	0.614	0.4826	0.7484	0.7844	-1.0	-1.0	0.7844	0.4803	0.725	0.5639	0.7727	0.7962	0.8556
1.4201	17.0	867	0.7757	0.5946	0.7822	0.6991	-1.0	-1.0	0.5959	0.4917	0.7629	0.8098	-1.0	-1.0	0.8098	0.4574	0.7333	0.5845	0.8182	0.7419	0.8778
1.4201	18.0	918	0.7285	0.6335	0.8224	0.7602	-1.0	-1.0	0.6347	0.5104	0.781	0.8128	-1.0	-1.0	0.8128	0.4873	0.7458	0.6291	0.8091	0.7841	0.8833
1.4201	19.0	969	0.7418	0.6357	0.8493	0.7477	-1.0	-1.0	0.6369	0.4976	0.7826	0.8184	-1.0	-1.0	0.8184	0.4839	0.725	0.6089	0.8136	0.8143	0.9167
0.7846	20.0	1020	0.6951	0.6499	0.8243	0.764	-1.0	-1.0	0.6507	0.5485	0.7894	0.8175	-1.0	-1.0	0.8175	0.4957	0.7625	0.6431	0.7955	0.8108	0.8944
0.7846	21.0	1071	0.7040	0.6487	0.8309	0.7602	-1.0	-1.0	0.6504	0.5255	0.7774	0.8108	-1.0	-1.0	0.8108	0.5195	0.75	0.6144	0.8045	0.8121	0.8778
0.7846	22.0	1122	0.6864	0.6797	0.8618	0.7785	-1.0	-1.0	0.6807	0.5382	0.7935	0.8234	-1.0	-1.0	0.8234	0.5291	0.75	0.6608	0.8091	0.8491	0.9111
0.7846	23.0	1173	0.6792	0.671	0.8567	0.7831	-1.0	-1.0	0.6712	0.5518	0.7956	0.8292	-1.0	-1.0	0.8292	0.5409	0.775	0.654	0.8182	0.818	0.8944
0.7846	24.0	1224	0.6690	0.6743	0.8411	0.7854	-1.0	-1.0	0.6748	0.5388	0.7931	0.8207	-1.0	-1.0	0.8207	0.531	0.7458	0.6659	0.8273	0.8259	0.8889
0.7846	25.0	1275	0.6670	0.6928	0.872	0.7775	-1.0	-1.0	0.6934	0.5718	0.801	0.8268	-1.0	-1.0	0.8268	0.5671	0.7542	0.6759	0.8318	0.8355	0.8944
0.7846	26.0	1326	0.6690	0.6795	0.8553	0.7984	-1.0	-1.0	0.68	0.5559	0.792	0.8203	-1.0	-1.0	0.8203	0.5564	0.7458	0.6644	0.8318	0.8176	0.8833
0.7846	27.0	1377	0.6638	0.6885	0.8582	0.7796	-1.0	-1.0	0.689	0.5644	0.8061	0.8305	-1.0	-1.0	0.8305	0.5592	0.7542	0.6586	0.8318	0.8477	0.9056
0.7846	28.0	1428	0.6691	0.6808	0.8597	0.7789	-1.0	-1.0	0.6813	0.5607	0.8008	0.8234	-1.0	-1.0	0.8234	0.5591	0.7542	0.6574	0.8273	0.8261	0.8889
0.7846	29.0	1479	0.6674	0.6872	0.8594	0.78	-1.0	-1.0	0.6877	0.5662	0.8027	0.8285	-1.0	-1.0	0.8285	0.5608	0.7583	0.6577	0.8273	0.8432	0.9
0.5967	30.0	1530	0.6673	0.6873	0.8595	0.7801	-1.0	-1.0	0.6878	0.5662	0.8027	0.8285	-1.0	-1.0	0.8285	0.5609	0.7583	0.6577	0.8273	0.8432	0.9

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

miguelcale04/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

miguelcale04/
yolo_finetuned_fruits