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	1.8431	0.0284	0.0658	0.0262	-1.0	-1.0	0.0298	0.0324	0.1882	0.4421	-1.0	-1.0	0.4421	0.0124	0.5542	0.024	0.35	0.0489	0.4222
No log	2.0	102	1.7374	0.0326	0.076	0.0255	-1.0	-1.0	0.0332	0.0694	0.2329	0.4395	-1.0	-1.0	0.4395	0.0661	0.5417	0.0125	0.3045	0.0191	0.4722
No log	3.0	153	1.6816	0.04	0.0948	0.028	-1.0	-1.0	0.0402	0.1349	0.2661	0.4966	-1.0	-1.0	0.4966	0.069	0.5667	0.0212	0.4455	0.0298	0.4778
No log	4.0	204	1.7031	0.0391	0.0858	0.0339	-1.0	-1.0	0.0392	0.116	0.2778	0.5347	-1.0	-1.0	0.5347	0.0658	0.5667	0.0239	0.4818	0.0276	0.5556
No log	5.0	255	1.5714	0.1005	0.2657	0.0606	-1.0	-1.0	0.1008	0.2056	0.3856	0.5269	-1.0	-1.0	0.5269	0.0763	0.55	0.1142	0.4864	0.1109	0.5444
No log	6.0	306	1.3019	0.1148	0.2774	0.0689	-1.0	-1.0	0.117	0.1945	0.4478	0.6452	-1.0	-1.0	0.6452	0.1209	0.6792	0.115	0.6455	0.1084	0.6111
No log	7.0	357	1.1840	0.1716	0.2911	0.1964	-1.0	-1.0	0.1802	0.3438	0.5866	0.7077	-1.0	-1.0	0.7077	0.118	0.6625	0.2324	0.7273	0.1646	0.7333
No log	8.0	408	1.1440	0.3462	0.5556	0.4163	-1.0	-1.0	0.3463	0.4018	0.6585	0.7264	-1.0	-1.0	0.7264	0.314	0.6792	0.4235	0.7	0.3011	0.8
No log	9.0	459	1.0075	0.4983	0.7346	0.5666	-1.0	-1.0	0.4988	0.4668	0.7413	0.7823	-1.0	-1.0	0.7823	0.3922	0.6833	0.5339	0.8136	0.5689	0.85
1.3814	10.0	510	0.8870	0.5764	0.8102	0.6616	-1.0	-1.0	0.577	0.491	0.7359	0.7897	-1.0	-1.0	0.7897	0.4599	0.7167	0.6132	0.8136	0.6561	0.8389
1.3814	11.0	561	0.8131	0.5733	0.8275	0.7065	-1.0	-1.0	0.5743	0.4693	0.7253	0.7918	-1.0	-1.0	0.7918	0.4448	0.775	0.5464	0.7727	0.7287	0.8278
1.3814	12.0	612	0.7591	0.6024	0.8316	0.6969	-1.0	-1.0	0.6032	0.4914	0.7539	0.8032	-1.0	-1.0	0.8032	0.4466	0.7875	0.6405	0.8	0.7201	0.8222
1.3814	13.0	663	0.8094	0.6017	0.8265	0.7062	-1.0	-1.0	0.6023	0.4996	0.7565	0.8044	-1.0	-1.0	0.8044	0.3917	0.7208	0.6315	0.8091	0.782	0.8833
1.3814	14.0	714	0.7015	0.627	0.8395	0.7252	-1.0	-1.0	0.6274	0.4969	0.7758	0.8039	-1.0	-1.0	0.8039	0.4684	0.7542	0.6133	0.7909	0.7993	0.8667
1.3814	15.0	765	0.7873	0.6263	0.8671	0.7407	-1.0	-1.0	0.6268	0.4849	0.7629	0.8067	-1.0	-1.0	0.8067	0.4687	0.75	0.6192	0.8091	0.7911	0.8611
1.3814	16.0	816	0.7589	0.6119	0.8464	0.7386	-1.0	-1.0	0.6124	0.5114	0.761	0.8022	-1.0	-1.0	0.8022	0.4426	0.7208	0.6095	0.8136	0.7836	0.8722
1.3814	17.0	867	0.6998	0.6468	0.8509	0.7564	-1.0	-1.0	0.6476	0.5167	0.7749	0.8404	-1.0	-1.0	0.8404	0.4802	0.7667	0.6573	0.8545	0.803	0.9
1.3814	18.0	918	0.6883	0.6287	0.8471	0.7281	-1.0	-1.0	0.6297	0.5065	0.8012	0.8343	-1.0	-1.0	0.8343	0.4577	0.775	0.6415	0.85	0.7868	0.8778
1.3814	19.0	969	0.6679	0.6299	0.8304	0.7658	-1.0	-1.0	0.6312	0.5175	0.8003	0.8407	-1.0	-1.0	0.8407	0.4904	0.7958	0.6199	0.8318	0.7793	0.8944
0.7067	20.0	1020	0.6620	0.6307	0.832	0.7763	-1.0	-1.0	0.6315	0.5096	0.772	0.8312	-1.0	-1.0	0.8312	0.4779	0.8042	0.6464	0.8227	0.7679	0.8667
0.7067	21.0	1071	0.6664	0.6424	0.8445	0.7735	-1.0	-1.0	0.6432	0.5143	0.7631	0.8421	-1.0	-1.0	0.8421	0.4694	0.8042	0.6395	0.85	0.8182	0.8722
0.7067	22.0	1122	0.6489	0.6445	0.8523	0.7496	-1.0	-1.0	0.6451	0.5273	0.8027	0.8478	-1.0	-1.0	0.8478	0.479	0.8	0.6641	0.8545	0.7902	0.8889
0.7067	23.0	1173	0.6428	0.6603	0.8561	0.7906	-1.0	-1.0	0.661	0.5312	0.792	0.8512	-1.0	-1.0	0.8512	0.4974	0.8167	0.6671	0.8591	0.8163	0.8778
0.7067	24.0	1224	0.6540	0.6572	0.848	0.7817	-1.0	-1.0	0.6578	0.5426	0.7894	0.8504	-1.0	-1.0	0.8504	0.4965	0.8042	0.6599	0.8636	0.8151	0.8833
0.7067	25.0	1275	0.6384	0.6606	0.8493	0.7954	-1.0	-1.0	0.6611	0.5463	0.8005	0.846	-1.0	-1.0	0.846	0.4852	0.8	0.6779	0.8545	0.8186	0.8833
0.7067	26.0	1326	0.6450	0.6673	0.8494	0.7982	-1.0	-1.0	0.6679	0.5511	0.805	0.8464	-1.0	-1.0	0.8464	0.4835	0.7958	0.6839	0.8545	0.8347	0.8889
0.7067	27.0	1377	0.6478	0.6594	0.8543	0.7998	-1.0	-1.0	0.66	0.5465	0.8059	0.8461	-1.0	-1.0	0.8461	0.4701	0.7958	0.6873	0.8591	0.8207	0.8833
0.7067	28.0	1428	0.6457	0.6626	0.8557	0.8053	-1.0	-1.0	0.6633	0.5486	0.8123	0.8527	-1.0	-1.0	0.8527	0.4723	0.8	0.6827	0.8636	0.833	0.8944
0.7067	29.0	1479	0.6435	0.6647	0.8575	0.8068	-1.0	-1.0	0.6654	0.5486	0.8152	0.8541	-1.0	-1.0	0.8541	0.4757	0.8042	0.6854	0.8636	0.833	0.8944
0.5613	30.0	1530	0.6435	0.6656	0.8584	0.8078	-1.0	-1.0	0.6662	0.5486	0.8152	0.8541	-1.0	-1.0	0.8541	0.4776	0.8042	0.6854	0.8636	0.8338	0.8944

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

aribanez/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

aribanez/
yolo_finetuned_fruits