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	2.2302	0.0141	0.0584	0.0027	-1.0	-1.0	0.0153	0.0454	0.126	0.2718	-1.0	-1.0	0.2718	0.0374	0.3208	0.0011	0.15	0.0039	0.3444
No log	2.0	102	1.9048	0.0201	0.0698	0.0083	-1.0	-1.0	0.0205	0.1021	0.2365	0.3931	-1.0	-1.0	0.3931	0.0405	0.5042	0.006	0.2864	0.0139	0.3889
No log	3.0	153	1.6323	0.0314	0.0812	0.0146	-1.0	-1.0	0.0317	0.1415	0.2969	0.5111	-1.0	-1.0	0.5111	0.0503	0.5458	0.015	0.4318	0.029	0.5556
No log	4.0	204	1.6976	0.0686	0.1624	0.0568	-1.0	-1.0	0.0687	0.1777	0.3383	0.491	-1.0	-1.0	0.491	0.0592	0.4875	0.031	0.3909	0.1157	0.5944
No log	5.0	255	1.6158	0.076	0.1713	0.0354	-1.0	-1.0	0.0762	0.264	0.4497	0.5819	-1.0	-1.0	0.5819	0.056	0.425	0.0683	0.5818	0.1036	0.7389
No log	6.0	306	1.1660	0.1196	0.2208	0.1133	-1.0	-1.0	0.1221	0.3064	0.4898	0.6761	-1.0	-1.0	0.6761	0.082	0.6708	0.1309	0.6409	0.1458	0.7167
No log	7.0	357	1.0355	0.1931	0.2978	0.2512	-1.0	-1.0	0.2112	0.3695	0.5988	0.7598	-1.0	-1.0	0.7598	0.0944	0.6667	0.243	0.7682	0.2418	0.8444
No log	8.0	408	0.9254	0.292	0.4369	0.3472	-1.0	-1.0	0.2989	0.4327	0.6872	0.778	-1.0	-1.0	0.778	0.1515	0.6917	0.3921	0.8091	0.3324	0.8333
No log	9.0	459	0.8142	0.4707	0.6772	0.5396	-1.0	-1.0	0.4737	0.4425	0.7067	0.7951	-1.0	-1.0	0.7951	0.2882	0.7333	0.4655	0.7909	0.6584	0.8611
1.3607	10.0	510	0.7984	0.5236	0.7512	0.5636	-1.0	-1.0	0.5242	0.4591	0.7305	0.784	-1.0	-1.0	0.784	0.3803	0.7542	0.521	0.7591	0.6694	0.8389
1.3607	11.0	561	0.7821	0.5435	0.7624	0.5828	-1.0	-1.0	0.5439	0.4667	0.7561	0.8287	-1.0	-1.0	0.8287	0.3664	0.7708	0.5029	0.8318	0.7612	0.8833
1.3607	12.0	612	0.7901	0.572	0.8172	0.6475	-1.0	-1.0	0.5736	0.4775	0.7312	0.8255	-1.0	-1.0	0.8255	0.4291	0.7917	0.539	0.8182	0.7479	0.8667
1.3607	13.0	663	0.7657	0.6074	0.8164	0.7294	-1.0	-1.0	0.6077	0.5185	0.7651	0.8199	-1.0	-1.0	0.8199	0.4331	0.7583	0.5815	0.8182	0.8075	0.8833
1.3607	14.0	714	0.7556	0.6428	0.8577	0.7333	-1.0	-1.0	0.6431	0.5254	0.7738	0.8337	-1.0	-1.0	0.8337	0.4679	0.7667	0.6465	0.8455	0.814	0.8889
1.3607	15.0	765	0.7345	0.6256	0.8452	0.7251	-1.0	-1.0	0.6261	0.5074	0.7741	0.8251	-1.0	-1.0	0.8251	0.4651	0.7833	0.6114	0.8364	0.8002	0.8556
1.3607	16.0	816	0.7337	0.6343	0.8617	0.7506	-1.0	-1.0	0.6347	0.5081	0.7529	0.8121	-1.0	-1.0	0.8121	0.4875	0.7625	0.6222	0.8182	0.7933	0.8556
1.3607	17.0	867	0.7308	0.6479	0.8715	0.739	-1.0	-1.0	0.6484	0.5075	0.7607	0.8172	-1.0	-1.0	0.8172	0.4851	0.75	0.6506	0.8182	0.8081	0.8833
1.3607	18.0	918	0.6891	0.6631	0.8679	0.7967	-1.0	-1.0	0.6641	0.5269	0.7801	0.8353	-1.0	-1.0	0.8353	0.5082	0.7917	0.69	0.8364	0.7911	0.8778
1.3607	19.0	969	0.6671	0.6707	0.8721	0.7828	-1.0	-1.0	0.6713	0.531	0.7848	0.8353	-1.0	-1.0	0.8353	0.5051	0.7625	0.668	0.8545	0.839	0.8889
0.6892	20.0	1020	0.6919	0.6709	0.8809	0.8003	-1.0	-1.0	0.6712	0.5264	0.784	0.8322	-1.0	-1.0	0.8322	0.4947	0.7833	0.672	0.8409	0.846	0.8722
0.6892	21.0	1071	0.6716	0.6722	0.8749	0.7809	-1.0	-1.0	0.6726	0.5371	0.7872	0.8404	-1.0	-1.0	0.8404	0.4916	0.7833	0.6887	0.8545	0.8362	0.8833
0.6892	22.0	1122	0.6739	0.6693	0.8653	0.7537	-1.0	-1.0	0.6698	0.5357	0.7807	0.831	-1.0	-1.0	0.831	0.479	0.7708	0.698	0.85	0.8307	0.8722
0.6892	23.0	1173	0.6744	0.6631	0.86	0.7715	-1.0	-1.0	0.6634	0.5272	0.7937	0.8443	-1.0	-1.0	0.8443	0.4862	0.8042	0.6729	0.8455	0.8302	0.8833
0.6892	24.0	1224	0.6850	0.6774	0.8756	0.7857	-1.0	-1.0	0.6778	0.5332	0.7886	0.8383	-1.0	-1.0	0.8383	0.5058	0.775	0.6886	0.8455	0.8379	0.8944
0.6892	25.0	1275	0.6679	0.6816	0.8823	0.7956	-1.0	-1.0	0.682	0.5341	0.7975	0.8452	-1.0	-1.0	0.8452	0.5254	0.7958	0.6823	0.8455	0.8371	0.8944
0.6892	26.0	1326	0.6535	0.6855	0.8803	0.797	-1.0	-1.0	0.6858	0.5322	0.813	0.8592	-1.0	-1.0	0.8592	0.5183	0.7958	0.697	0.8818	0.8411	0.9
0.6892	27.0	1377	0.6573	0.677	0.881	0.7902	-1.0	-1.0	0.6774	0.5285	0.8016	0.85	-1.0	-1.0	0.85	0.5137	0.7875	0.6883	0.8682	0.8291	0.8944
0.6892	28.0	1428	0.6575	0.6809	0.8781	0.796	-1.0	-1.0	0.6812	0.5304	0.8034	0.8508	-1.0	-1.0	0.8508	0.5142	0.7833	0.6932	0.8636	0.8353	0.9056
0.6892	29.0	1479	0.6578	0.6829	0.8781	0.7971	-1.0	-1.0	0.6832	0.5289	0.8037	0.8553	-1.0	-1.0	0.8553	0.5145	0.7875	0.6926	0.8727	0.8415	0.9056
0.5342	30.0	1530	0.6581	0.6827	0.8779	0.797	-1.0	-1.0	0.6831	0.5289	0.8037	0.8553	-1.0	-1.0	0.8553	0.5144	0.7875	0.6923	0.8727	0.8415	0.9056

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

joarnaiz/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

joarnaiz/
yolo_finetuned_fruits