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	1.9168	0.011	0.0267	0.0081	-1.0	0.0161	0.0148	0.0442	0.1565	0.3206	-1.0	0.1	0.3485	0.0136	0.4275	0.0	0.0	0.0193	0.5343
No log	2.0	120	1.5461	0.0412	0.0965	0.0315	-1.0	0.1493	0.0399	0.1579	0.273	0.449	-1.0	0.3786	0.4512	0.0342	0.57	0.0194	0.1143	0.07	0.6629
No log	3.0	180	1.2702	0.0734	0.1671	0.0639	-1.0	0.1017	0.076	0.2463	0.387	0.5636	-1.0	0.4357	0.5795	0.0961	0.615	0.0443	0.3214	0.0799	0.7543
No log	4.0	240	1.2423	0.0813	0.1613	0.0758	-1.0	0.2779	0.0743	0.2702	0.4558	0.6172	-1.0	0.5357	0.6285	0.0907	0.6125	0.0486	0.4905	0.1046	0.7486
No log	5.0	300	1.2186	0.1002	0.1958	0.0928	-1.0	0.2011	0.0965	0.2551	0.4875	0.595	-1.0	0.4643	0.612	0.0919	0.6275	0.0925	0.4262	0.1163	0.7314
No log	6.0	360	1.0360	0.1936	0.3298	0.2149	-1.0	0.3235	0.1842	0.3295	0.5887	0.6991	-1.0	0.5857	0.7157	0.1859	0.705	0.1827	0.581	0.2124	0.8114
No log	7.0	420	1.0435	0.3418	0.5496	0.3768	-1.0	0.4263	0.3516	0.3779	0.6207	0.7223	-1.0	0.6429	0.7387	0.2188	0.6575	0.3071	0.6952	0.4995	0.8143
No log	8.0	480	0.9763	0.3733	0.5963	0.4304	-1.0	0.4576	0.384	0.3477	0.6131	0.7203	-1.0	0.6643	0.7299	0.2579	0.6875	0.3948	0.7619	0.4673	0.7114
1.2819	9.0	540	0.9729	0.4048	0.6491	0.4533	-1.0	0.4535	0.4208	0.3606	0.6365	0.7297	-1.0	0.6786	0.7392	0.317	0.6925	0.4198	0.7595	0.4778	0.7371
1.2819	10.0	600	0.9867	0.4522	0.7245	0.5204	-1.0	0.5405	0.4528	0.3633	0.6438	0.733	-1.0	0.65	0.7462	0.3247	0.71	0.471	0.7405	0.561	0.7486
1.2819	11.0	660	0.8974	0.4976	0.7352	0.561	-1.0	0.6327	0.4956	0.394	0.6717	0.7571	-1.0	0.7214	0.7652	0.3346	0.705	0.5363	0.7833	0.622	0.7829
1.2819	12.0	720	0.9062	0.5042	0.8019	0.566	-1.0	0.5781	0.5131	0.3774	0.6703	0.7594	-1.0	0.7071	0.7702	0.3796	0.715	0.5031	0.769	0.6301	0.7943
1.2819	13.0	780	0.8927	0.5136	0.79	0.5867	-1.0	0.6444	0.514	0.3724	0.6983	0.7641	-1.0	0.7286	0.7736	0.3486	0.7	0.5547	0.781	0.6374	0.8114
1.2819	14.0	840	0.9009	0.507	0.7814	0.5461	-1.0	0.5907	0.5186	0.3919	0.691	0.7621	-1.0	0.7	0.7754	0.3471	0.7025	0.5375	0.7667	0.6364	0.8171
1.2819	15.0	900	0.8588	0.5349	0.7915	0.607	-1.0	0.5704	0.5479	0.404	0.6791	0.7604	-1.0	0.7143	0.7677	0.3818	0.7425	0.5783	0.7643	0.6445	0.7743
1.2819	16.0	960	0.8809	0.5314	0.8154	0.599	-1.0	0.5413	0.5484	0.4085	0.6689	0.7546	-1.0	0.7143	0.7613	0.4064	0.7225	0.5545	0.7643	0.6334	0.7771
0.7	17.0	1020	0.8626	0.5402	0.823	0.601	-1.0	0.5705	0.5557	0.4038	0.6979	0.767	-1.0	0.7214	0.7739	0.4157	0.7525	0.5728	0.7857	0.632	0.7629
0.7	18.0	1080	0.8723	0.5431	0.8142	0.615	-1.0	0.5579	0.556	0.3902	0.6911	0.7657	-1.0	0.7357	0.7717	0.4201	0.73	0.5923	0.7786	0.617	0.7886
0.7	19.0	1140	0.8407	0.558	0.8205	0.6471	-1.0	0.5592	0.5833	0.4172	0.7085	0.7793	-1.0	0.7286	0.7905	0.4215	0.725	0.5807	0.7786	0.6719	0.8343
0.7	20.0	1200	0.8675	0.5656	0.8479	0.6415	-1.0	0.5875	0.5785	0.4039	0.697	0.7698	-1.0	0.75	0.7743	0.4318	0.735	0.6069	0.7857	0.6579	0.7886
0.7	21.0	1260	0.8636	0.5601	0.8313	0.6211	-1.0	0.6281	0.5637	0.4085	0.6962	0.7611	-1.0	0.7357	0.7662	0.4335	0.73	0.607	0.7905	0.6399	0.7629
0.7	22.0	1320	0.8463	0.567	0.827	0.6541	-1.0	0.6092	0.5758	0.4168	0.7023	0.7797	-1.0	0.7357	0.7888	0.4327	0.73	0.6211	0.8119	0.6472	0.7971
0.7	23.0	1380	0.8397	0.5704	0.8411	0.6472	-1.0	0.6288	0.579	0.4068	0.7036	0.7723	-1.0	0.7357	0.7804	0.4259	0.7225	0.6243	0.8	0.661	0.7943
0.7	24.0	1440	0.8512	0.5627	0.829	0.6446	-1.0	0.6038	0.5722	0.404	0.7019	0.7693	-1.0	0.75	0.7748	0.4261	0.725	0.6236	0.7857	0.6383	0.7971
0.5208	25.0	1500	0.8526	0.5704	0.843	0.6505	-1.0	0.6087	0.584	0.412	0.7165	0.7754	-1.0	0.7429	0.7837	0.4288	0.7175	0.6141	0.7857	0.6684	0.8229
0.5208	26.0	1560	0.8459	0.5683	0.8447	0.6421	-1.0	0.6012	0.5795	0.4175	0.7115	0.7681	-1.0	0.7429	0.7748	0.4402	0.72	0.6091	0.7786	0.6555	0.8057
0.5208	27.0	1620	0.8259	0.5724	0.847	0.6408	-1.0	0.6195	0.5806	0.4146	0.7185	0.7724	-1.0	0.75	0.7789	0.4418	0.7225	0.6132	0.7833	0.6621	0.8114
0.5208	28.0	1680	0.8257	0.5728	0.8404	0.6418	-1.0	0.6191	0.5812	0.4127	0.718	0.7744	-1.0	0.7429	0.7818	0.4424	0.7275	0.616	0.7929	0.66	0.8029
0.5208	29.0	1740	0.8261	0.5689	0.837	0.6377	-1.0	0.6193	0.5761	0.4035	0.708	0.7661	-1.0	0.75	0.7707	0.4415	0.725	0.6176	0.7905	0.6476	0.7829
0.5208	30.0	1800	0.8251	0.5689	0.837	0.6378	-1.0	0.6185	0.5762	0.4035	0.7088	0.7653	-1.0	0.7429	0.7707	0.4416	0.725	0.6177	0.7881	0.6474	0.7829

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Unax14/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Unax14/
yolo_finetuned_fruits