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	2.0321	0.0203	0.0628	0.0135	-1.0	-1.0	0.0206	0.0869	0.2554	0.4502	-1.0	-1.0	0.4502	0.0232	0.504	0.0259	0.408	0.0118	0.4385
No log	2.0	102	1.3709	0.1018	0.1942	0.0862	-1.0	-1.0	0.1031	0.2309	0.4632	0.6693	-1.0	-1.0	0.6693	0.0563	0.624	0.0941	0.684	0.1549	0.7
No log	3.0	153	1.1863	0.116	0.1929	0.1326	-1.0	-1.0	0.1213	0.227	0.4793	0.7079	-1.0	-1.0	0.7079	0.0534	0.62	0.1232	0.7	0.1713	0.8038
No log	4.0	204	1.2220	0.1307	0.2643	0.1136	-1.0	-1.0	0.1307	0.2598	0.4965	0.6993	-1.0	-1.0	0.6993	0.1017	0.656	0.1091	0.692	0.1813	0.75
No log	5.0	255	1.1015	0.1603	0.265	0.1697	-1.0	-1.0	0.1607	0.2889	0.5493	0.737	-1.0	-1.0	0.737	0.1114	0.64	0.1785	0.744	0.191	0.8269
No log	6.0	306	1.0478	0.1911	0.3405	0.1919	-1.0	-1.0	0.1912	0.3308	0.5524	0.739	-1.0	-1.0	0.739	0.1955	0.696	0.1973	0.744	0.1804	0.7769
No log	7.0	357	1.0181	0.217	0.3965	0.2006	-1.0	-1.0	0.2189	0.3475	0.548	0.7318	-1.0	-1.0	0.7318	0.1779	0.636	0.2077	0.748	0.2654	0.8115
No log	8.0	408	0.9318	0.2536	0.4026	0.2947	-1.0	-1.0	0.2541	0.3476	0.5775	0.7677	-1.0	-1.0	0.7677	0.2199	0.7	0.302	0.78	0.239	0.8231
No log	9.0	459	0.9312	0.2406	0.3805	0.2651	-1.0	-1.0	0.2421	0.366	0.6131	0.7707	-1.0	-1.0	0.7707	0.2319	0.736	0.2441	0.776	0.2457	0.8
1.1932	10.0	510	0.9243	0.2712	0.4396	0.3171	-1.0	-1.0	0.2722	0.3716	0.6248	0.7628	-1.0	-1.0	0.7628	0.2455	0.716	0.3145	0.78	0.2536	0.7923
1.1932	11.0	561	0.8611	0.3478	0.5153	0.3949	-1.0	-1.0	0.348	0.3875	0.6398	0.7773	-1.0	-1.0	0.7773	0.2767	0.736	0.3658	0.796	0.4008	0.8
1.1932	12.0	612	0.8709	0.3874	0.5715	0.4718	-1.0	-1.0	0.388	0.3759	0.6516	0.7787	-1.0	-1.0	0.7787	0.3251	0.712	0.4328	0.828	0.4043	0.7962
1.1932	13.0	663	0.8699	0.4268	0.6194	0.4859	-1.0	-1.0	0.427	0.3993	0.6769	0.7801	-1.0	-1.0	0.7801	0.3644	0.748	0.4952	0.796	0.4207	0.7962
1.1932	14.0	714	0.8286	0.4442	0.6588	0.5266	-1.0	-1.0	0.4455	0.4038	0.698	0.7918	-1.0	-1.0	0.7918	0.3776	0.764	0.4947	0.8	0.4604	0.8115
1.1932	15.0	765	0.8631	0.4613	0.7092	0.557	-1.0	-1.0	0.462	0.3744	0.678	0.7625	-1.0	-1.0	0.7625	0.3586	0.736	0.4922	0.736	0.5331	0.8154
1.1932	16.0	816	0.7929	0.4876	0.6912	0.5874	-1.0	-1.0	0.4886	0.4024	0.6978	0.8035	-1.0	-1.0	0.8035	0.4266	0.788	0.4825	0.784	0.5538	0.8385
1.1932	17.0	867	0.7630	0.505	0.6978	0.5865	-1.0	-1.0	0.5055	0.4196	0.7164	0.8006	-1.0	-1.0	0.8006	0.4009	0.76	0.4995	0.788	0.6145	0.8538
1.1932	18.0	918	0.8162	0.491	0.7205	0.5904	-1.0	-1.0	0.4911	0.4051	0.7075	0.7733	-1.0	-1.0	0.7733	0.3758	0.736	0.5501	0.784	0.547	0.8
1.1932	19.0	969	0.8230	0.5191	0.7548	0.5996	-1.0	-1.0	0.5194	0.4231	0.6991	0.7755	-1.0	-1.0	0.7755	0.4261	0.704	0.5592	0.788	0.572	0.8346
0.7314	20.0	1020	0.8396	0.5136	0.759	0.5677	-1.0	-1.0	0.5138	0.4165	0.7019	0.7678	-1.0	-1.0	0.7678	0.392	0.696	0.5712	0.792	0.5775	0.8154
0.7314	21.0	1071	0.7980	0.5313	0.7738	0.6439	-1.0	-1.0	0.5317	0.4098	0.7123	0.7809	-1.0	-1.0	0.7809	0.4054	0.728	0.5846	0.78	0.6038	0.8346
0.7314	22.0	1122	0.8008	0.5211	0.7527	0.6053	-1.0	-1.0	0.5215	0.4257	0.7282	0.7861	-1.0	-1.0	0.7861	0.4065	0.724	0.5928	0.788	0.5641	0.8462
0.7314	23.0	1173	0.7929	0.5374	0.771	0.6468	-1.0	-1.0	0.5377	0.4123	0.7241	0.7886	-1.0	-1.0	0.7886	0.4085	0.724	0.601	0.784	0.6028	0.8577
0.7314	24.0	1224	0.7914	0.5611	0.8014	0.6858	-1.0	-1.0	0.5615	0.4137	0.7243	0.7861	-1.0	-1.0	0.7861	0.4398	0.716	0.6053	0.8	0.6384	0.8423
0.7314	25.0	1275	0.7709	0.5777	0.8186	0.7135	-1.0	-1.0	0.578	0.4283	0.7319	0.7873	-1.0	-1.0	0.7873	0.4435	0.712	0.6151	0.796	0.6744	0.8538
0.7314	26.0	1326	0.7812	0.5748	0.8099	0.6947	-1.0	-1.0	0.5752	0.431	0.7333	0.7952	-1.0	-1.0	0.7952	0.4179	0.712	0.6355	0.812	0.671	0.8615
0.7314	27.0	1377	0.7786	0.5705	0.8018	0.7131	-1.0	-1.0	0.5708	0.4204	0.7308	0.7887	-1.0	-1.0	0.7887	0.4166	0.72	0.6214	0.8	0.6734	0.8462
0.7314	28.0	1428	0.7775	0.5664	0.8014	0.7056	-1.0	-1.0	0.5667	0.4204	0.7188	0.7847	-1.0	-1.0	0.7847	0.4038	0.704	0.6284	0.8	0.6669	0.85
0.7314	29.0	1479	0.7776	0.5688	0.8023	0.7092	-1.0	-1.0	0.5692	0.4217	0.7214	0.786	-1.0	-1.0	0.786	0.4067	0.708	0.6347	0.8	0.665	0.85
0.5659	30.0	1530	0.7775	0.5687	0.8021	0.7092	-1.0	-1.0	0.5691	0.4217	0.7214	0.786	-1.0	-1.0	0.786	0.4067	0.708	0.6344	0.8	0.665	0.85

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

aliciapiedrafita/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

aliciapiedrafita/
yolo_finetuned_fruits