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: 0.6527
• Map: 0.6866
• Map 50: 0.9268
• Map 75: 0.8064
• Map Small: -1.0
• Map Medium: 0.3032
• Map Large: 0.7269
• Mar 1: 0.6841
• Mar 10: 0.8114
• Mar 100: 0.8591
• Mar Small: -1.0
• Mar Medium: 0.625
• Mar Large: 0.8825
• Map Raccoon: 0.6866
• Mar 100 Raccoon: 0.8591

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 Raccoon	Mar 100 Raccoon
No log	1.0	40	1.3397	0.1653	0.3096	0.1634	-1.0	0.2203	0.1727	0.4068	0.5932	0.6432	-1.0	0.5	0.6575	0.1653	0.6432
No log	2.0	80	0.9567	0.206	0.3313	0.251	-1.0	0.2506	0.2184	0.4523	0.6432	0.7545	-1.0	0.525	0.7775	0.206	0.7545
No log	3.0	120	0.8981	0.3407	0.5033	0.3996	-1.0	0.2111	0.3644	0.5386	0.6773	0.7795	-1.0	0.575	0.8	0.3407	0.7795
No log	4.0	160	0.8561	0.4313	0.6765	0.5453	-1.0	0.337	0.4552	0.4955	0.7114	0.7955	-1.0	0.55	0.82	0.4313	0.7955
No log	5.0	200	0.8243	0.4946	0.7259	0.5872	-1.0	0.3095	0.5205	0.5727	0.7455	0.7955	-1.0	0.625	0.8125	0.4946	0.7955
No log	6.0	240	0.6946	0.6341	0.8614	0.7492	-1.0	0.437	0.6609	0.6159	0.7909	0.8364	-1.0	0.725	0.8475	0.6341	0.8364
No log	7.0	280	0.7659	0.568	0.8338	0.6552	-1.0	0.3524	0.5952	0.5909	0.7568	0.8568	-1.0	0.675	0.875	0.568	0.8568
No log	8.0	320	0.7453	0.6063	0.8747	0.6842	-1.0	0.3703	0.6374	0.6136	0.7818	0.8295	-1.0	0.6	0.8525	0.6063	0.8295
No log	9.0	360	0.8076	0.6337	0.9109	0.7728	-1.0	0.3426	0.6675	0.6273	0.75	0.7932	-1.0	0.575	0.815	0.6337	0.7932
No log	10.0	400	0.8037	0.6431	0.8962	0.741	-1.0	0.3238	0.6793	0.6477	0.7727	0.8295	-1.0	0.5	0.8625	0.6431	0.8295
No log	11.0	440	0.7562	0.6629	0.9247	0.8388	-1.0	0.3437	0.7	0.6432	0.7909	0.8341	-1.0	0.6	0.8575	0.6629	0.8341
No log	12.0	480	0.6658	0.6829	0.916	0.8145	-1.0	0.4476	0.7113	0.6773	0.8114	0.8591	-1.0	0.7	0.875	0.6829	0.8591
0.7837	13.0	520	0.7645	0.6514	0.9123	0.7653	-1.0	0.398	0.682	0.6386	0.7977	0.8545	-1.0	0.625	0.8775	0.6514	0.8545
0.7837	14.0	560	0.6700	0.6795	0.9287	0.8161	-1.0	0.4489	0.7083	0.675	0.7955	0.8523	-1.0	0.75	0.8625	0.6795	0.8523
0.7837	15.0	600	0.7249	0.6661	0.9175	0.7974	-1.0	0.33	0.705	0.6636	0.7932	0.8386	-1.0	0.575	0.865	0.6661	0.8386
0.7837	16.0	640	0.6520	0.6822	0.9226	0.778	-1.0	0.3635	0.7216	0.6795	0.8182	0.8591	-1.0	0.675	0.8775	0.6822	0.8591
0.7837	17.0	680	0.6738	0.6627	0.9069	0.7566	-1.0	0.3809	0.7031	0.6727	0.8068	0.8477	-1.0	0.625	0.87	0.6627	0.8477
0.7837	18.0	720	0.6502	0.6883	0.925	0.7847	-1.0	0.3661	0.7262	0.6932	0.8091	0.85	-1.0	0.625	0.8725	0.6883	0.85
0.7837	19.0	760	0.7292	0.6509	0.9187	0.7946	-1.0	0.2743	0.6892	0.6636	0.7818	0.8318	-1.0	0.6	0.855	0.6509	0.8318
0.7837	20.0	800	0.6524	0.6792	0.9149	0.7753	-1.0	0.2601	0.7217	0.6909	0.8114	0.8545	-1.0	0.575	0.8825	0.6792	0.8545
0.7837	21.0	840	0.6343	0.671	0.9163	0.7899	-1.0	0.2777	0.7105	0.6886	0.8182	0.8614	-1.0	0.625	0.885	0.671	0.8614
0.7837	22.0	880	0.6931	0.6654	0.9226	0.7917	-1.0	0.3492	0.6981	0.6659	0.7886	0.8364	-1.0	0.675	0.8525	0.6654	0.8364
0.7837	23.0	920	0.6532	0.6747	0.9264	0.8198	-1.0	0.3292	0.7138	0.6795	0.7955	0.8432	-1.0	0.65	0.8625	0.6747	0.8432
0.7837	24.0	960	0.6431	0.6765	0.9232	0.8213	-1.0	0.2775	0.7188	0.6795	0.8136	0.8614	-1.0	0.625	0.885	0.6765	0.8614
0.4689	25.0	1000	0.6492	0.689	0.9264	0.8033	-1.0	0.3039	0.7294	0.6818	0.8091	0.8568	-1.0	0.65	0.8775	0.689	0.8568
0.4689	26.0	1040	0.6594	0.6857	0.9271	0.8051	-1.0	0.3031	0.7257	0.6773	0.7977	0.8477	-1.0	0.625	0.87	0.6857	0.8477
0.4689	27.0	1080	0.6489	0.682	0.9255	0.8053	-1.0	0.3031	0.7218	0.6795	0.8068	0.8545	-1.0	0.625	0.8775	0.682	0.8545
0.4689	28.0	1120	0.6577	0.6788	0.9273	0.8069	-1.0	0.3032	0.7185	0.6773	0.8091	0.8568	-1.0	0.625	0.88	0.6788	0.8568
0.4689	29.0	1160	0.6528	0.6866	0.9268	0.8064	-1.0	0.3032	0.7269	0.6841	0.8114	0.8591	-1.0	0.625	0.8825	0.6866	0.8591
0.4689	30.0	1200	0.6527	0.6866	0.9268	0.8064	-1.0	0.3032	0.7269	0.6841	0.8114	0.8591	-1.0	0.625	0.8825	0.6866	0.8591

Framework versions

• Transformers 4.57.6
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2