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	1.3832	0.0662	0.1157	0.0675	-1.0	0.0739	0.0823	0.2406	0.4375	0.5766	-1.0	0.3429	0.6114	0.0589	0.585	0.0233	0.5476	0.1164	0.5971
No log	2.0	120	1.1698	0.1353	0.2296	0.1411	-1.0	0.084	0.153	0.2702	0.4985	0.5976	-1.0	0.3	0.6393	0.1204	0.64	0.0371	0.45	0.2482	0.7029
No log	3.0	180	1.1113	0.2256	0.3806	0.2196	-1.0	0.2253	0.2425	0.2869	0.541	0.6777	-1.0	0.4429	0.7111	0.2027	0.6825	0.0814	0.619	0.3926	0.7314
No log	4.0	240	1.1347	0.2893	0.4969	0.3288	-1.0	0.227	0.3081	0.31	0.5639	0.6804	-1.0	0.5	0.708	0.2585	0.6575	0.1758	0.6524	0.4336	0.7314
No log	5.0	300	1.2339	0.3139	0.5879	0.2967	-1.0	0.331	0.3322	0.3008	0.5406	0.632	-1.0	0.5429	0.6512	0.206	0.575	0.2292	0.681	0.5065	0.64
No log	6.0	360	0.9967	0.4014	0.6469	0.4216	-1.0	0.3962	0.4191	0.358	0.6481	0.7231	-1.0	0.6286	0.742	0.3186	0.6625	0.2708	0.7238	0.6147	0.7829
No log	7.0	420	1.0597	0.4142	0.7448	0.4478	-1.0	0.3237	0.4436	0.3425	0.6121	0.6731	-1.0	0.5143	0.6985	0.3237	0.6575	0.3926	0.6762	0.5264	0.6857
No log	8.0	480	0.8963	0.4543	0.7499	0.457	-1.0	0.4688	0.4784	0.3545	0.6702	0.7344	-1.0	0.6357	0.75	0.3374	0.72	0.4997	0.7548	0.526	0.7286
1.1464	9.0	540	0.9669	0.4473	0.706	0.4926	-1.0	0.3025	0.4892	0.369	0.6374	0.719	-1.0	0.5643	0.7461	0.3263	0.6775	0.4506	0.731	0.5649	0.7486
1.1464	10.0	600	0.9448	0.4642	0.7241	0.5078	-1.0	0.4185	0.4969	0.3656	0.6285	0.7131	-1.0	0.5714	0.7363	0.3286	0.6875	0.4589	0.7262	0.6051	0.7257
1.1464	11.0	660	0.9464	0.4645	0.7321	0.4897	-1.0	0.393	0.502	0.381	0.6472	0.7229	-1.0	0.6071	0.7425	0.3356	0.6925	0.4614	0.7476	0.5964	0.7286
1.1464	12.0	720	0.9143	0.4816	0.7601	0.5366	-1.0	0.4266	0.5129	0.37	0.6644	0.7434	-1.0	0.6143	0.7642	0.3459	0.7225	0.4752	0.7333	0.6236	0.7743
1.1464	13.0	780	0.8523	0.5186	0.7851	0.5524	-1.0	0.4882	0.5457	0.3976	0.6733	0.7352	-1.0	0.6071	0.7542	0.3849	0.7475	0.5316	0.7238	0.6394	0.7343
1.1464	14.0	840	0.8937	0.5077	0.7907	0.557	-1.0	0.4944	0.5348	0.3906	0.6622	0.748	-1.0	0.6571	0.7649	0.3544	0.7125	0.5471	0.7571	0.6215	0.7743
1.1464	15.0	900	0.8502	0.52	0.8012	0.5662	-1.0	0.4128	0.5524	0.4075	0.669	0.7367	-1.0	0.5857	0.7619	0.3692	0.715	0.5478	0.7381	0.6432	0.7571
1.1464	16.0	960	0.8644	0.515	0.8117	0.5603	-1.0	0.4659	0.5399	0.3743	0.6587	0.7319	-1.0	0.5857	0.7537	0.3634	0.7325	0.5928	0.769	0.5889	0.6943
0.6981	17.0	1020	0.8121	0.5252	0.8011	0.5394	-1.0	0.4677	0.5524	0.3924	0.6975	0.7589	-1.0	0.6571	0.7767	0.347	0.73	0.5675	0.7667	0.6611	0.78
0.6981	18.0	1080	0.8345	0.5364	0.8268	0.5691	-1.0	0.5232	0.559	0.3959	0.6849	0.7559	-1.0	0.6571	0.7713	0.3633	0.7425	0.58	0.7595	0.6659	0.7657
0.6981	19.0	1140	0.8186	0.531	0.8115	0.5705	-1.0	0.4991	0.5533	0.3908	0.6815	0.748	-1.0	0.65	0.7623	0.3856	0.7525	0.5816	0.7571	0.6257	0.7343
0.6981	20.0	1200	0.7999	0.562	0.8515	0.598	-1.0	0.4755	0.5915	0.4071	0.7006	0.7647	-1.0	0.6143	0.7881	0.4081	0.755	0.6069	0.7762	0.6711	0.7629
0.6981	21.0	1260	0.8050	0.5545	0.8284	0.6088	-1.0	0.4877	0.5829	0.3939	0.704	0.7691	-1.0	0.6429	0.7885	0.4096	0.7625	0.608	0.7619	0.6459	0.7829
0.6981	22.0	1320	0.8181	0.5503	0.813	0.573	-1.0	0.4993	0.5756	0.4033	0.7077	0.77	-1.0	0.6714	0.787	0.4089	0.7375	0.5731	0.7667	0.6689	0.8057
0.6981	23.0	1380	0.8230	0.553	0.8332	0.5737	-1.0	0.4984	0.5758	0.4016	0.7059	0.7706	-1.0	0.6571	0.7882	0.405	0.7575	0.5733	0.7571	0.6807	0.7971
0.6981	24.0	1440	0.8177	0.5512	0.8339	0.5859	-1.0	0.5113	0.5758	0.402	0.701	0.7675	-1.0	0.6357	0.7882	0.4096	0.7525	0.5716	0.7643	0.6724	0.7857
0.5444	25.0	1500	0.8300	0.558	0.8348	0.5836	-1.0	0.4993	0.5835	0.4049	0.7054	0.7706	-1.0	0.65	0.7902	0.4095	0.75	0.5775	0.7619	0.6868	0.8
0.5444	26.0	1560	0.8121	0.5618	0.8348	0.5814	-1.0	0.5067	0.5873	0.4102	0.7154	0.7776	-1.0	0.6714	0.7954	0.4142	0.7575	0.5854	0.7667	0.6859	0.8086
0.5444	27.0	1620	0.8138	0.5582	0.8328	0.5901	-1.0	0.5006	0.5824	0.4064	0.7073	0.7702	-1.0	0.6571	0.7883	0.4105	0.755	0.5879	0.7643	0.6762	0.7914
0.5444	28.0	1680	0.8084	0.5597	0.842	0.5932	-1.0	0.4993	0.5837	0.4052	0.704	0.7677	-1.0	0.6571	0.7854	0.4212	0.7525	0.5829	0.7619	0.6748	0.7886
0.5444	29.0	1740	0.8162	0.5581	0.8424	0.593	-1.0	0.4995	0.5829	0.4052	0.7065	0.7719	-1.0	0.6571	0.79	0.4187	0.76	0.5811	0.7643	0.6745	0.7914
0.5444	30.0	1800	0.8182	0.5572	0.8422	0.5925	-1.0	0.4995	0.5815	0.406	0.7081	0.771	-1.0	0.6571	0.7893	0.4184	0.755	0.5804	0.7667	0.6727	0.7914

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

aiarenm/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

aiarenm/
yolo_finetuned_fruits