yolo_finetuned_fruits

This model is a fine-tuned version of hustvl/yolos-tiny on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8611
Map: 0.5862
Map 50: 0.8559
Map 75: 0.6628
Map Small: -1.0
Map Medium: 0.4866
Map Large: 0.6073
Mar 1: 0.4641
Mar 10: 0.7152
Mar 100: 0.7611
Mar Small: -1.0
Mar Medium: 0.6
Mar Large: 0.788
Map Banana: 0.4777
Mar 100 Banana: 0.7243
Map Orange: 0.5692
Mar 100 Orange: 0.748
Map Apple: 0.7117
Mar 100 Apple: 0.8111

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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

为此模型做种