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	1.7333	0.0186	0.0536	0.0085	-1.0	-1.0	0.0194	0.0662	0.2038	0.319	-1.0	-1.0	0.319	0.019	0.2625	0.0078	0.3	0.0292	0.3944
No log	2.0	102	1.6490	0.0495	0.1376	0.0318	-1.0	-1.0	0.0497	0.1013	0.2897	0.5444	-1.0	-1.0	0.5444	0.0448	0.5917	0.0421	0.4636	0.0614	0.5778
No log	3.0	153	1.6399	0.051	0.1343	0.0295	-1.0	-1.0	0.0511	0.1241	0.3021	0.5148	-1.0	-1.0	0.5148	0.0892	0.6167	0.0242	0.35	0.0396	0.5778
No log	4.0	204	1.4387	0.0574	0.1516	0.0302	-1.0	-1.0	0.0577	0.1492	0.3607	0.5925	-1.0	-1.0	0.5925	0.0659	0.6208	0.0489	0.4955	0.0576	0.6611
No log	5.0	255	1.2928	0.1027	0.2622	0.0664	-1.0	-1.0	0.1067	0.2915	0.5104	0.6533	-1.0	-1.0	0.6533	0.0915	0.6583	0.1162	0.6182	0.1004	0.6833
No log	6.0	306	0.9721	0.1321	0.219	0.1427	-1.0	-1.0	0.1322	0.3496	0.5701	0.7952	-1.0	-1.0	0.7952	0.0886	0.7875	0.1272	0.7591	0.1805	0.8389
No log	7.0	357	0.8879	0.3082	0.479	0.3185	-1.0	-1.0	0.3118	0.4522	0.6498	0.8076	-1.0	-1.0	0.8076	0.209	0.7708	0.2411	0.7909	0.4746	0.8611
No log	8.0	408	0.9419	0.3654	0.5822	0.4292	-1.0	-1.0	0.3654	0.4096	0.6574	0.7964	-1.0	-1.0	0.7964	0.2803	0.7917	0.252	0.7364	0.5638	0.8611
No log	9.0	459	0.8468	0.4617	0.6579	0.5501	-1.0	-1.0	0.4638	0.45	0.7032	0.8094	-1.0	-1.0	0.8094	0.2891	0.7833	0.4003	0.7727	0.6957	0.8722
1.2934	10.0	510	0.8371	0.5433	0.8082	0.6506	-1.0	-1.0	0.5453	0.4889	0.7308	0.8007	-1.0	-1.0	0.8007	0.4069	0.7875	0.4483	0.7591	0.7749	0.8556
1.2934	11.0	561	0.7655	0.5904	0.7991	0.6724	-1.0	-1.0	0.5908	0.508	0.7638	0.8129	-1.0	-1.0	0.8129	0.4696	0.775	0.4453	0.7636	0.8563	0.9
1.2934	12.0	612	0.7287	0.5909	0.8129	0.6732	-1.0	-1.0	0.5914	0.4816	0.7579	0.8179	-1.0	-1.0	0.8179	0.4641	0.7875	0.4882	0.7773	0.8204	0.8889
1.2934	13.0	663	0.7399	0.6292	0.8546	0.7209	-1.0	-1.0	0.6299	0.5231	0.7582	0.8075	-1.0	-1.0	0.8075	0.5115	0.7583	0.5281	0.7864	0.8479	0.8778
1.2934	14.0	714	0.7132	0.6067	0.8471	0.6886	-1.0	-1.0	0.6072	0.4899	0.7779	0.8323	-1.0	-1.0	0.8323	0.5036	0.8167	0.512	0.8136	0.8045	0.8667
1.2934	15.0	765	0.7087	0.6116	0.8645	0.7153	-1.0	-1.0	0.6121	0.4995	0.7819	0.8377	-1.0	-1.0	0.8377	0.5177	0.7958	0.515	0.8227	0.8022	0.8944
1.2934	16.0	816	0.7246	0.6205	0.8496	0.7287	-1.0	-1.0	0.6208	0.5088	0.771	0.8211	-1.0	-1.0	0.8211	0.547	0.7875	0.5268	0.8091	0.7877	0.8667
1.2934	17.0	867	0.6635	0.6394	0.8431	0.7411	-1.0	-1.0	0.6405	0.4978	0.8009	0.8339	-1.0	-1.0	0.8339	0.5405	0.8083	0.5633	0.8045	0.8145	0.8889
1.2934	18.0	918	0.6802	0.649	0.8378	0.7631	-1.0	-1.0	0.6501	0.5045	0.795	0.8336	-1.0	-1.0	0.8336	0.5489	0.7958	0.5626	0.8273	0.8354	0.8778
1.2934	19.0	969	0.6650	0.6595	0.8498	0.7548	-1.0	-1.0	0.6613	0.5255	0.8132	0.8492	-1.0	-1.0	0.8492	0.5492	0.8167	0.6052	0.8364	0.8241	0.8944
0.6894	20.0	1020	0.6824	0.6709	0.8788	0.7539	-1.0	-1.0	0.6728	0.5234	0.7864	0.8196	-1.0	-1.0	0.8196	0.5644	0.775	0.6372	0.8227	0.8111	0.8611
0.6894	21.0	1071	0.6730	0.6848	0.8794	0.7989	-1.0	-1.0	0.6863	0.5329	0.8075	0.8423	-1.0	-1.0	0.8423	0.582	0.8042	0.6365	0.8227	0.8361	0.9
0.6894	22.0	1122	0.6708	0.6815	0.8849	0.7873	-1.0	-1.0	0.6831	0.5399	0.7981	0.8392	-1.0	-1.0	0.8392	0.5841	0.7833	0.6397	0.8455	0.8206	0.8889
0.6894	23.0	1173	0.6636	0.7036	0.898	0.8071	-1.0	-1.0	0.7047	0.5519	0.8019	0.8402	-1.0	-1.0	0.8402	0.6156	0.8	0.6509	0.8318	0.8441	0.8889
0.6894	24.0	1224	0.6509	0.7047	0.9031	0.8151	-1.0	-1.0	0.706	0.5617	0.7985	0.8455	-1.0	-1.0	0.8455	0.6195	0.8	0.6494	0.8364	0.8452	0.9
0.6894	25.0	1275	0.6586	0.6939	0.8932	0.8028	-1.0	-1.0	0.6957	0.5406	0.783	0.8383	-1.0	-1.0	0.8383	0.6099	0.7958	0.6335	0.8136	0.8382	0.9056
0.6894	26.0	1326	0.6561	0.7031	0.8937	0.8061	-1.0	-1.0	0.7048	0.5453	0.7881	0.8384	-1.0	-1.0	0.8384	0.6149	0.7833	0.6499	0.8318	0.8444	0.9
0.6894	27.0	1377	0.6595	0.7029	0.8955	0.799	-1.0	-1.0	0.7046	0.5453	0.7894	0.8416	-1.0	-1.0	0.8416	0.6176	0.7875	0.6483	0.8318	0.8427	0.9056
0.6894	28.0	1428	0.6579	0.7054	0.8927	0.8051	-1.0	-1.0	0.707	0.5457	0.7926	0.8426	-1.0	-1.0	0.8426	0.6176	0.7958	0.6454	0.8318	0.8533	0.9
0.6894	29.0	1479	0.6567	0.7023	0.8934	0.8059	-1.0	-1.0	0.7038	0.5453	0.7907	0.8392	-1.0	-1.0	0.8392	0.6176	0.7958	0.6441	0.8273	0.8452	0.8944
0.5335	30.0	1530	0.6563	0.7023	0.8934	0.8059	-1.0	-1.0	0.7038	0.5453	0.7907	0.8392	-1.0	-1.0	0.8392	0.6177	0.7958	0.6441	0.8273	0.8452	0.8944

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

DanielArgaiz/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DanielArgaiz/
yolo_finetuned_fruits