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	2.3094	0.0032	0.0108	0.0012	-1.0	0.0006	0.0049	0.0514	0.121	0.2462	-1.0	0.0167	0.2808	0.0054	0.3351	0.0011	0.048	0.0031	0.3556
No log	2.0	102	1.8096	0.0269	0.1103	0.0103	-1.0	0.0042	0.0305	0.1002	0.195	0.4115	-1.0	0.075	0.4555	0.0301	0.5243	0.0345	0.188	0.016	0.5222
No log	3.0	153	1.8119	0.03	0.099	0.0095	-1.0	0.005	0.0323	0.0649	0.2336	0.3939	-1.0	0.1	0.4319	0.0489	0.4865	0.0316	0.184	0.0096	0.5111
No log	4.0	204	1.7983	0.0501	0.1704	0.0171	-1.0	0.0828	0.0512	0.1062	0.3072	0.4305	-1.0	0.1	0.471	0.0511	0.4541	0.0492	0.404	0.05	0.4333
No log	5.0	255	1.2361	0.1463	0.2325	0.1539	-1.0	0.2326	0.1641	0.3315	0.5618	0.6525	-1.0	0.5	0.676	0.097	0.6486	0.1378	0.62	0.204	0.6889
No log	6.0	306	1.0533	0.1713	0.2628	0.1951	-1.0	0.2405	0.1796	0.3315	0.6258	0.7348	-1.0	0.6	0.754	0.1014	0.7054	0.1565	0.688	0.256	0.8111
No log	7.0	357	1.0764	0.2575	0.4103	0.2844	-1.0	0.2974	0.2843	0.3723	0.611	0.7068	-1.0	0.475	0.7347	0.1864	0.673	0.2672	0.692	0.3189	0.7556
No log	8.0	408	1.0170	0.3077	0.4714	0.348	-1.0	0.3119	0.3328	0.3789	0.5997	0.6755	-1.0	0.4667	0.6959	0.2122	0.6811	0.2794	0.612	0.4316	0.7333
No log	9.0	459	1.0347	0.3821	0.5846	0.4213	-1.0	0.3855	0.418	0.4039	0.6315	0.6732	-1.0	0.425	0.7022	0.2739	0.6351	0.3215	0.64	0.551	0.7444
1.2730	10.0	510	1.1369	0.3592	0.5588	0.3891	-1.0	0.2356	0.3974	0.415	0.6019	0.6432	-1.0	0.3	0.6907	0.2487	0.6135	0.3243	0.616	0.5046	0.7
1.2730	11.0	561	1.1140	0.3659	0.5944	0.4177	-1.0	0.3266	0.4087	0.3804	0.6027	0.6548	-1.0	0.3667	0.6923	0.2352	0.6243	0.3796	0.64	0.4831	0.7
1.2730	12.0	612	1.0854	0.3937	0.616	0.4196	-1.0	0.2572	0.4359	0.3976	0.6143	0.6596	-1.0	0.3667	0.7034	0.2747	0.6108	0.4439	0.668	0.4625	0.7
1.2730	13.0	663	0.9304	0.4262	0.6495	0.4878	-1.0	0.3474	0.4593	0.4394	0.675	0.7342	-1.0	0.525	0.7656	0.3153	0.7	0.4455	0.736	0.5179	0.7667
1.2730	14.0	714	1.0531	0.4829	0.7622	0.5441	-1.0	0.4128	0.5146	0.4108	0.6526	0.6979	-1.0	0.55	0.7225	0.3256	0.6351	0.5017	0.692	0.6213	0.7667
1.2730	15.0	765	1.0054	0.5187	0.7912	0.6041	-1.0	0.4332	0.5588	0.4571	0.6767	0.7041	-1.0	0.4833	0.7394	0.4085	0.6486	0.5486	0.708	0.599	0.7556
1.2730	16.0	816	0.9023	0.5074	0.7819	0.5852	-1.0	0.4293	0.5384	0.4318	0.6843	0.7135	-1.0	0.4917	0.7458	0.4281	0.673	0.5234	0.712	0.5708	0.7556
1.2730	17.0	867	0.9242	0.5574	0.8299	0.647	-1.0	0.4564	0.5883	0.4601	0.7117	0.7478	-1.0	0.5083	0.7855	0.4472	0.6973	0.5308	0.724	0.6941	0.8222
1.2730	18.0	918	0.8697	0.5834	0.8647	0.667	-1.0	0.529	0.6092	0.4684	0.7229	0.7624	-1.0	0.6167	0.789	0.473	0.7	0.5921	0.776	0.685	0.8111
1.2730	19.0	969	0.8877	0.5763	0.8644	0.6543	-1.0	0.4926	0.6069	0.4809	0.7078	0.7406	-1.0	0.5417	0.7715	0.4796	0.7081	0.5583	0.736	0.6912	0.7778
0.6854	20.0	1020	0.8976	0.5687	0.8245	0.6662	-1.0	0.4684	0.5931	0.4709	0.7159	0.7415	-1.0	0.6167	0.7635	0.487	0.7027	0.567	0.744	0.6523	0.7778
0.6854	21.0	1071	0.8716	0.5861	0.8264	0.6596	-1.0	0.4524	0.6182	0.4812	0.7256	0.7691	-1.0	0.625	0.7923	0.4731	0.7243	0.5766	0.772	0.7086	0.8111
0.6854	22.0	1122	0.8568	0.5734	0.8242	0.6524	-1.0	0.4153	0.6095	0.4776	0.725	0.7574	-1.0	0.575	0.7886	0.4585	0.7081	0.5549	0.764	0.7067	0.8
0.6854	23.0	1173	0.8485	0.5918	0.8382	0.6733	-1.0	0.4939	0.6183	0.485	0.7348	0.775	-1.0	0.65	0.7954	0.4658	0.7324	0.5559	0.748	0.7538	0.8444
0.6854	24.0	1224	0.8602	0.5811	0.8391	0.6776	-1.0	0.4766	0.6058	0.4845	0.727	0.7647	-1.0	0.6167	0.7904	0.4797	0.727	0.5576	0.756	0.706	0.8111
0.6854	25.0	1275	0.8444	0.5971	0.8582	0.6964	-1.0	0.4917	0.6249	0.4619	0.7292	0.7696	-1.0	0.6167	0.7955	0.4686	0.7378	0.5725	0.76	0.7502	0.8111
0.6854	26.0	1326	0.8446	0.5935	0.8578	0.6823	-1.0	0.5159	0.6238	0.4776	0.7283	0.7707	-1.0	0.6	0.7991	0.4625	0.7297	0.5817	0.76	0.7362	0.8222
0.6854	27.0	1377	0.8695	0.5911	0.8536	0.66	-1.0	0.4866	0.6163	0.4652	0.7185	0.7617	-1.0	0.6	0.7894	0.4641	0.7189	0.5699	0.744	0.7394	0.8222
0.6854	28.0	1428	0.8621	0.5928	0.8536	0.6787	-1.0	0.4866	0.6159	0.4652	0.7207	0.7657	-1.0	0.6	0.7936	0.4751	0.727	0.5742	0.748	0.7289	0.8222
0.6854	29.0	1479	0.8613	0.5917	0.856	0.6635	-1.0	0.4866	0.6146	0.4678	0.7189	0.7648	-1.0	0.6	0.7927	0.4769	0.7243	0.5705	0.748	0.7276	0.8222
0.5120	30.0	1530	0.8611	0.5862	0.8559	0.6628	-1.0	0.4866	0.6073	0.4641	0.7152	0.7611	-1.0	0.6	0.788	0.4777	0.7243	0.5692	0.748	0.7117	0.8111

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

joheras/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

joheras/
yolo_finetuned_fruits