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	2.1499	0.0136	0.0448	0.0056	-1.0	0.0109	0.0158	0.075	0.1852	0.3357	-1.0	0.1843	0.3553	0.013	0.37	0.0078	0.3143	0.0201	0.3229
No log	2.0	120	1.7782	0.0292	0.0773	0.0143	-1.0	0.0276	0.0386	0.1073	0.2172	0.3738	-1.0	0.1543	0.3976	0.0311	0.5425	0.0182	0.1476	0.0383	0.4314
No log	3.0	180	1.5906	0.0594	0.1414	0.0417	-1.0	0.1115	0.0605	0.152	0.3742	0.5341	-1.0	0.4	0.5537	0.0778	0.56	0.0529	0.5167	0.0476	0.5257
No log	4.0	240	1.5383	0.0861	0.202	0.0501	-1.0	0.2612	0.0865	0.151	0.3671	0.5304	-1.0	0.45	0.5377	0.1303	0.6025	0.0745	0.5	0.0535	0.4886
No log	5.0	300	1.1837	0.1558	0.2537	0.1816	-1.0	0.2695	0.1583	0.2698	0.4915	0.6304	-1.0	0.6171	0.6306	0.1721	0.665	0.1067	0.4976	0.1887	0.7286
No log	6.0	360	1.0734	0.157	0.2964	0.1582	-1.0	0.3468	0.187	0.2915	0.5425	0.6648	-1.0	0.6343	0.6689	0.2002	0.655	0.1633	0.6881	0.1074	0.6514
No log	7.0	420	1.0573	0.2775	0.4635	0.3247	-1.0	0.4621	0.2992	0.3344	0.5898	0.6521	-1.0	0.6143	0.6591	0.2421	0.6525	0.3061	0.581	0.2844	0.7229
No log	8.0	480	1.0384	0.2976	0.4884	0.3472	-1.0	0.3785	0.3332	0.349	0.5867	0.6615	-1.0	0.5629	0.6758	0.2774	0.655	0.2988	0.6095	0.3166	0.72
1.3795	9.0	540	1.0118	0.3836	0.6136	0.4243	-1.0	0.5103	0.4155	0.3625	0.6428	0.7234	-1.0	0.6757	0.7321	0.3059	0.7025	0.418	0.7048	0.4267	0.7629
1.3795	10.0	600	0.9245	0.435	0.6491	0.5092	-1.0	0.5728	0.4373	0.3755	0.6479	0.7627	-1.0	0.67	0.7771	0.3134	0.7225	0.4386	0.7571	0.5529	0.8086
1.3795	11.0	660	0.9402	0.4402	0.6789	0.4961	-1.0	0.5685	0.4575	0.3954	0.6632	0.7531	-1.0	0.6486	0.769	0.2956	0.7225	0.4795	0.7452	0.5453	0.7914
1.3795	12.0	720	0.9860	0.4799	0.732	0.5485	-1.0	0.5748	0.4896	0.3923	0.6661	0.7248	-1.0	0.64	0.7374	0.3637	0.6825	0.4651	0.7119	0.611	0.78
1.3795	13.0	780	0.9429	0.5169	0.7922	0.5961	-1.0	0.5773	0.5318	0.3917	0.6751	0.7439	-1.0	0.6871	0.7558	0.3606	0.6675	0.5592	0.7643	0.631	0.8
1.3795	14.0	840	0.8865	0.5173	0.758	0.5911	-1.0	0.6596	0.5182	0.4012	0.678	0.7499	-1.0	0.6986	0.7576	0.3531	0.705	0.5424	0.7619	0.6563	0.7829
1.3795	15.0	900	0.8419	0.5406	0.7763	0.6074	-1.0	0.5919	0.5512	0.4255	0.6973	0.7671	-1.0	0.7114	0.7778	0.4123	0.6975	0.5349	0.7952	0.6745	0.8086
1.3795	16.0	960	0.8329	0.5395	0.7552	0.6311	-1.0	0.5883	0.5466	0.4152	0.7104	0.757	-1.0	0.7	0.7684	0.4031	0.7	0.5438	0.7738	0.6716	0.7971
0.7998	17.0	1020	0.8817	0.534	0.7852	0.6453	-1.0	0.5942	0.5434	0.3962	0.6775	0.7507	-1.0	0.71	0.7613	0.4026	0.685	0.5503	0.7643	0.6492	0.8029
0.7998	18.0	1080	0.8657	0.5663	0.8226	0.6633	-1.0	0.6353	0.5746	0.4164	0.6948	0.7529	-1.0	0.7186	0.7613	0.415	0.685	0.5936	0.7595	0.6903	0.8143
0.7998	19.0	1140	0.8733	0.5511	0.8041	0.6633	-1.0	0.5608	0.5704	0.402	0.7012	0.7453	-1.0	0.6757	0.7573	0.4056	0.7025	0.5905	0.7619	0.6572	0.7714
0.7998	20.0	1200	0.8267	0.5838	0.8199	0.6795	-1.0	0.6184	0.5922	0.4153	0.7223	0.7688	-1.0	0.7086	0.779	0.4281	0.7075	0.6191	0.7905	0.7042	0.8086
0.7998	21.0	1260	0.8072	0.5746	0.8082	0.669	-1.0	0.6242	0.5837	0.424	0.7139	0.774	-1.0	0.7086	0.7843	0.417	0.7225	0.5945	0.7881	0.7124	0.8114
0.7998	22.0	1320	0.8209	0.5833	0.8172	0.6688	-1.0	0.6298	0.5924	0.4248	0.7034	0.7666	-1.0	0.7229	0.7737	0.4388	0.7175	0.6002	0.7738	0.7108	0.8086
0.7998	23.0	1380	0.8103	0.5882	0.8115	0.6759	-1.0	0.6302	0.5949	0.4237	0.7178	0.7796	-1.0	0.7571	0.7845	0.4453	0.725	0.6136	0.7881	0.7059	0.8257
0.7998	24.0	1440	0.8106	0.5867	0.8113	0.6811	-1.0	0.6585	0.5931	0.4273	0.7175	0.7777	-1.0	0.73	0.7851	0.4353	0.7275	0.6169	0.7857	0.7077	0.82
0.6151	25.0	1500	0.8246	0.5815	0.8161	0.6787	-1.0	0.6404	0.5954	0.424	0.7167	0.7696	-1.0	0.72	0.7772	0.4355	0.7175	0.615	0.7714	0.6941	0.82
0.6151	26.0	1560	0.8168	0.5812	0.8151	0.6754	-1.0	0.6353	0.5892	0.4254	0.7088	0.7707	-1.0	0.7229	0.778	0.4366	0.725	0.6096	0.7786	0.6972	0.8086
0.6151	27.0	1620	0.8339	0.5809	0.8164	0.6778	-1.0	0.6162	0.5896	0.4188	0.7077	0.7702	-1.0	0.7057	0.7798	0.4323	0.7225	0.6103	0.7738	0.7	0.8143
0.6151	28.0	1680	0.8239	0.5779	0.8163	0.6688	-1.0	0.617	0.5864	0.4218	0.7038	0.7647	-1.0	0.6786	0.7764	0.4304	0.715	0.6121	0.7762	0.6911	0.8029
0.6151	29.0	1740	0.8207	0.5819	0.8169	0.6689	-1.0	0.6283	0.5899	0.4235	0.7046	0.767	-1.0	0.6886	0.7778	0.4342	0.72	0.6167	0.781	0.6948	0.8
0.6151	30.0	1800	0.8209	0.5813	0.8161	0.6682	-1.0	0.6283	0.5888	0.4242	0.7055	0.7704	-1.0	0.6886	0.7816	0.4339	0.7225	0.6177	0.7857	0.6923	0.8029

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

mahernto/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mahernto/
yolo_finetuned_fruits