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	1.9158	0.0195	0.0636	0.0063	-1.0	0.027	0.0198	0.0473	0.1897	0.3615	-1.0	0.2183	0.3761	0.04	0.385	0.0011	0.0881	0.0174	0.6114
No log	2.0	120	2.0371	0.0268	0.078	0.012	-1.0	0.0374	0.0262	0.0794	0.2079	0.3617	-1.0	0.2	0.3771	0.039	0.4075	0.0053	0.1119	0.0361	0.5657
No log	3.0	180	1.4488	0.0432	0.1226	0.0229	-1.0	0.0661	0.0401	0.1905	0.3398	0.5017	-1.0	0.2583	0.5219	0.069	0.625	0.0333	0.2714	0.0272	0.6086
No log	4.0	240	1.2716	0.0733	0.1622	0.0681	-1.0	0.1318	0.0715	0.242	0.4183	0.6169	-1.0	0.3967	0.6399	0.0732	0.6425	0.0524	0.4452	0.0943	0.7629
No log	5.0	300	1.1472	0.1133	0.2136	0.1094	-1.0	0.1777	0.1156	0.2851	0.48	0.6252	-1.0	0.405	0.6456	0.1031	0.7	0.1103	0.4071	0.1265	0.7686
No log	6.0	360	1.1300	0.1191	0.2449	0.1125	-1.0	0.1469	0.1307	0.2699	0.4715	0.6694	-1.0	0.4333	0.6956	0.0991	0.67	0.1202	0.6095	0.1379	0.7286
No log	7.0	420	1.0298	0.1813	0.3059	0.1929	-1.0	0.2227	0.2027	0.3479	0.5318	0.6733	-1.0	0.45	0.6983	0.1167	0.67	0.2302	0.55	0.197	0.8
No log	8.0	480	1.0293	0.2451	0.4266	0.2668	-1.0	0.2595	0.2725	0.3298	0.5753	0.7069	-1.0	0.5267	0.7266	0.1943	0.7125	0.3182	0.6738	0.2229	0.7343
1.2845	9.0	540	1.0215	0.3761	0.6485	0.3915	-1.0	0.4188	0.3996	0.3375	0.6398	0.7336	-1.0	0.6383	0.7473	0.2155	0.6775	0.428	0.7405	0.4848	0.7829
1.2845	10.0	600	0.9666	0.4652	0.7264	0.5036	-1.0	0.3976	0.497	0.3696	0.6634	0.7315	-1.0	0.5817	0.7512	0.3139	0.6775	0.4658	0.7286	0.616	0.7886
1.2845	11.0	660	0.9365	0.4826	0.7627	0.5587	-1.0	0.4124	0.5147	0.3787	0.6606	0.7163	-1.0	0.505	0.7414	0.3238	0.6875	0.4915	0.7071	0.6327	0.7543
1.2845	12.0	720	0.9472	0.4644	0.7652	0.5261	-1.0	0.3875	0.5056	0.3719	0.6594	0.7294	-1.0	0.5717	0.7484	0.3286	0.7025	0.4983	0.7143	0.5663	0.7714
1.2845	13.0	780	0.9087	0.4966	0.764	0.5557	-1.0	0.4804	0.5252	0.3921	0.679	0.7517	-1.0	0.6483	0.7656	0.3481	0.71	0.5176	0.7595	0.624	0.7857
1.2845	14.0	840	0.8610	0.5198	0.7753	0.5692	-1.0	0.4833	0.5606	0.4232	0.7004	0.7477	-1.0	0.6633	0.7606	0.409	0.685	0.5204	0.7667	0.63	0.7914
1.2845	15.0	900	0.8564	0.5518	0.8086	0.6727	-1.0	0.5648	0.5817	0.411	0.6983	0.7569	-1.0	0.645	0.7717	0.4321	0.715	0.5533	0.7643	0.6701	0.7914
1.2845	16.0	960	0.8996	0.5348	0.8088	0.6341	-1.0	0.4901	0.5621	0.4183	0.6793	0.745	-1.0	0.6383	0.7595	0.4119	0.6975	0.5284	0.7405	0.6642	0.7971
0.8009	17.0	1020	0.8437	0.5527	0.8203	0.6544	-1.0	0.4749	0.5871	0.4243	0.6989	0.7535	-1.0	0.6067	0.7722	0.4025	0.71	0.5727	0.7476	0.683	0.8029
0.8009	18.0	1080	0.8433	0.5625	0.8238	0.6682	-1.0	0.4952	0.5982	0.4334	0.6974	0.7577	-1.0	0.5983	0.7777	0.407	0.7175	0.5929	0.7643	0.6876	0.7914
0.8009	19.0	1140	0.8158	0.5855	0.8359	0.6588	-1.0	0.5315	0.614	0.4387	0.7157	0.7715	-1.0	0.6267	0.7896	0.4249	0.735	0.6071	0.7738	0.7245	0.8057
0.8009	20.0	1200	0.7977	0.586	0.8433	0.6602	-1.0	0.5306	0.6157	0.4415	0.7192	0.7753	-1.0	0.6433	0.7929	0.4119	0.7225	0.6322	0.7833	0.7138	0.82
0.8009	21.0	1260	0.8195	0.5916	0.8465	0.6581	-1.0	0.5731	0.6166	0.442	0.7196	0.7795	-1.0	0.6733	0.7941	0.4367	0.73	0.616	0.7857	0.7222	0.8229
0.8009	22.0	1320	0.7861	0.5915	0.8481	0.6645	-1.0	0.5399	0.619	0.4396	0.7219	0.7785	-1.0	0.6583	0.7943	0.4391	0.735	0.6303	0.7976	0.7052	0.8029
0.8009	23.0	1380	0.8101	0.5835	0.848	0.6618	-1.0	0.5154	0.6151	0.4409	0.7128	0.7804	-1.0	0.64	0.7979	0.4427	0.7475	0.614	0.7881	0.6937	0.8057
0.8009	24.0	1440	0.7936	0.5912	0.8462	0.6779	-1.0	0.5577	0.6196	0.4438	0.7217	0.7819	-1.0	0.6733	0.7962	0.4422	0.7425	0.6164	0.7833	0.715	0.82
0.5971	25.0	1500	0.7935	0.5879	0.8557	0.6645	-1.0	0.4766	0.6217	0.441	0.7235	0.7775	-1.0	0.6683	0.7921	0.4412	0.7325	0.6318	0.8	0.6907	0.8
0.5971	26.0	1560	0.7936	0.5867	0.854	0.6559	-1.0	0.4773	0.6209	0.4406	0.719	0.7754	-1.0	0.6417	0.7922	0.4459	0.74	0.6115	0.7833	0.7028	0.8029
0.5971	27.0	1620	0.7856	0.5904	0.8561	0.6682	-1.0	0.5188	0.6238	0.4441	0.7217	0.7748	-1.0	0.6417	0.7919	0.4463	0.7325	0.6143	0.7833	0.7105	0.8086
0.5971	28.0	1680	0.7838	0.5918	0.8561	0.6678	-1.0	0.4937	0.6265	0.4448	0.7231	0.7746	-1.0	0.6417	0.7919	0.4458	0.7275	0.6251	0.7905	0.7047	0.8057
0.5971	29.0	1740	0.7819	0.592	0.8569	0.6674	-1.0	0.4967	0.6266	0.4457	0.724	0.7738	-1.0	0.6417	0.791	0.4476	0.7275	0.6235	0.7881	0.7047	0.8057
0.5971	30.0	1800	0.7819	0.5883	0.8521	0.6633	-1.0	0.4917	0.6223	0.4441	0.7224	0.7722	-1.0	0.6417	0.7892	0.4472	0.7275	0.6126	0.7833	0.7051	0.8057

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

GabrielMI/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

GabrielMI/
yolo_finetuned_fruits