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	1.2065	0.3349	0.594	0.3191	-1.0	0.2027	0.3477	0.2886	0.5674	0.6597	-1.0	0.45	0.6626	0.2604	0.6435	0.3192	0.6156	0.4252	0.72
No log	2.0	102	1.1052	0.3702	0.6747	0.3714	-1.0	0.1184	0.3947	0.2991	0.6003	0.691	-1.0	0.525	0.6947	0.2997	0.6652	0.3983	0.7344	0.4126	0.6733
No log	3.0	153	1.1502	0.3963	0.675	0.3912	-1.0	0.2477	0.4055	0.3323	0.5815	0.6637	-1.0	0.45	0.6692	0.258	0.5957	0.4331	0.6687	0.4977	0.7267
No log	4.0	204	1.1329	0.4307	0.7103	0.4618	-1.0	0.2527	0.4348	0.3348	0.5913	0.6825	-1.0	0.5375	0.6828	0.2881	0.587	0.4102	0.6938	0.5939	0.7667
No log	5.0	255	1.0829	0.4477	0.7623	0.4572	-1.0	0.2504	0.4579	0.3419	0.6191	0.6856	-1.0	0.5125	0.6881	0.328	0.6391	0.4276	0.6844	0.5875	0.7333
No log	6.0	306	1.0990	0.4651	0.7719	0.4787	-1.0	0.2534	0.4716	0.3628	0.6225	0.6816	-1.0	0.6125	0.6753	0.3228	0.5913	0.516	0.7469	0.5564	0.7067
No log	7.0	357	1.0293	0.5028	0.8035	0.5774	-1.0	0.2769	0.5155	0.3768	0.6413	0.7096	-1.0	0.575	0.7099	0.3685	0.6478	0.5339	0.7344	0.6061	0.7467
No log	8.0	408	1.1512	0.4855	0.8179	0.5257	-1.0	0.3376	0.4945	0.3582	0.6062	0.657	-1.0	0.575	0.6527	0.361	0.5609	0.5136	0.6969	0.5819	0.7133
No log	9.0	459	1.0125	0.5294	0.8323	0.5958	-1.0	0.4663	0.5302	0.3944	0.6407	0.7181	-1.0	0.6625	0.7129	0.4293	0.6348	0.6066	0.7594	0.5522	0.76
0.8149	10.0	510	1.0078	0.5278	0.8242	0.558	-1.0	0.4623	0.5251	0.3868	0.6542	0.6989	-1.0	0.6	0.6958	0.3978	0.613	0.5908	0.7437	0.5947	0.74
0.8149	11.0	561	1.0651	0.5228	0.8039	0.5865	-1.0	0.3568	0.5329	0.3913	0.653	0.7063	-1.0	0.5125	0.7052	0.3783	0.613	0.5365	0.7125	0.6534	0.7933
0.8149	12.0	612	1.0468	0.506	0.8224	0.5397	-1.0	0.3883	0.5105	0.3963	0.6266	0.6819	-1.0	0.525	0.6774	0.372	0.587	0.5545	0.7188	0.5916	0.74
0.8149	13.0	663	1.0665	0.5167	0.8234	0.5827	-1.0	0.3814	0.5207	0.3826	0.6189	0.6833	-1.0	0.525	0.6822	0.3615	0.5957	0.5741	0.7344	0.6144	0.72
0.8149	14.0	714	1.0126	0.5511	0.8445	0.6159	-1.0	0.3544	0.5627	0.3989	0.6601	0.7002	-1.0	0.525	0.698	0.4236	0.5826	0.5738	0.7312	0.6559	0.7867
0.8149	15.0	765	1.0911	0.5235	0.8415	0.5407	-1.0	0.3178	0.5349	0.3737	0.6239	0.6972	-1.0	0.4875	0.7	0.3667	0.6217	0.575	0.7031	0.6288	0.7667
0.8149	16.0	816	1.0059	0.535	0.8198	0.5755	-1.0	0.4028	0.5413	0.3968	0.6614	0.7229	-1.0	0.525	0.7244	0.3597	0.6261	0.5902	0.7625	0.6551	0.78
0.8149	17.0	867	1.0205	0.5342	0.8214	0.5542	-1.0	0.3467	0.5393	0.3777	0.6545	0.7103	-1.0	0.525	0.7077	0.3537	0.6043	0.6042	0.7531	0.6446	0.7733
0.8149	18.0	918	1.0311	0.525	0.8154	0.5435	-1.0	0.3299	0.5333	0.3925	0.6466	0.6968	-1.0	0.4875	0.6984	0.3188	0.5957	0.5909	0.7281	0.6653	0.7667
0.8149	19.0	969	1.0238	0.5436	0.8155	0.5654	-1.0	0.2763	0.56	0.4013	0.6464	0.7054	-1.0	0.475	0.713	0.353	0.6217	0.6146	0.7344	0.6633	0.76
0.5986	20.0	1020	1.0287	0.5613	0.8492	0.6369	-1.0	0.4372	0.5614	0.4042	0.6538	0.6989	-1.0	0.525	0.6977	0.411	0.5957	0.6098	0.7344	0.663	0.7667
0.5986	21.0	1071	1.0119	0.567	0.8502	0.6284	-1.0	0.3637	0.5709	0.4045	0.6609	0.6994	-1.0	0.5	0.6995	0.4064	0.6	0.6156	0.725	0.6789	0.7733
0.5986	22.0	1122	1.0043	0.5646	0.8461	0.6066	-1.0	0.4164	0.5638	0.4007	0.671	0.7039	-1.0	0.525	0.7035	0.4145	0.6043	0.608	0.7406	0.6714	0.7667
0.5986	23.0	1173	0.9863	0.5664	0.8467	0.586	-1.0	0.411	0.5649	0.4021	0.6735	0.7164	-1.0	0.525	0.717	0.3922	0.613	0.6233	0.7563	0.6837	0.78
0.5986	24.0	1224	0.9919	0.5633	0.8461	0.5847	-1.0	0.413	0.5611	0.3992	0.6778	0.7191	-1.0	0.525	0.7198	0.3823	0.6174	0.6245	0.7531	0.683	0.7867
0.5986	25.0	1275	1.0022	0.5616	0.8493	0.5873	-1.0	0.4422	0.5596	0.3982	0.6823	0.723	-1.0	0.525	0.7244	0.3773	0.6261	0.6178	0.7563	0.6896	0.7867
0.5986	26.0	1326	1.0008	0.5559	0.8421	0.5884	-1.0	0.4122	0.5573	0.4025	0.6728	0.7162	-1.0	0.525	0.717	0.3673	0.613	0.6219	0.7688	0.6786	0.7667
0.5986	27.0	1377	0.9994	0.5565	0.8475	0.5919	-1.0	0.4123	0.5582	0.3959	0.6807	0.7153	-1.0	0.525	0.7158	0.3724	0.613	0.6246	0.7594	0.6726	0.7733
0.5986	28.0	1428	1.0028	0.5545	0.8454	0.5858	-1.0	0.4002	0.5563	0.3945	0.6713	0.7066	-1.0	0.525	0.7061	0.3718	0.6	0.6224	0.7531	0.6695	0.7667
0.5986	29.0	1479	1.0025	0.5525	0.8453	0.585	-1.0	0.4002	0.5542	0.3959	0.6775	0.7095	-1.0	0.525	0.7096	0.372	0.6087	0.6211	0.7531	0.6644	0.7667
0.4673	30.0	1530	1.0035	0.5521	0.8457	0.585	-1.0	0.4002	0.5544	0.3959	0.6775	0.7095	-1.0	0.525	0.7096	0.3711	0.6087	0.6208	0.7531	0.6644	0.7667

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Elisaa44/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Elisaa44/
yolo_finetuned_fruits