yolo_finetuned_raccoons

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: 0.7046
Map: 0.658
Map 50: 0.952
Map 75: 0.7705
Map Small: -1.0
Map Medium: 0.3924
Map Large: 0.6802
Mar 1: 0.6619
Mar 10: 0.8119
Mar 100: 0.8643
Mar Small: -1.0
Mar Medium: 0.7667
Mar Large: 0.8718
Map Raccoon: 0.658
Mar 100 Raccoon: 0.8643

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	1.4924	0.1005	0.2393	0.0536	-1.0	0.0337	0.108	0.2643	0.4619	0.6214	-1.0	0.0667	0.6641	0.1005	0.6214
No log	2.0	80	1.2578	0.1838	0.3781	0.1844	-1.0	0.0455	0.1962	0.3429	0.5548	0.6857	-1.0	0.1333	0.7282	0.1838	0.6857
No log	3.0	120	1.1647	0.2632	0.4958	0.2776	-1.0	0.059	0.2815	0.3857	0.5452	0.719	-1.0	0.3	0.7513	0.2632	0.719
No log	4.0	160	1.1535	0.2156	0.4201	0.2113	-1.0	0.1655	0.2295	0.3357	0.6024	0.7238	-1.0	0.3667	0.7513	0.2156	0.7238
No log	5.0	200	1.1569	0.2712	0.5589	0.2293	-1.0	0.2032	0.2886	0.3976	0.6	0.7095	-1.0	0.2333	0.7462	0.2712	0.7095
No log	6.0	240	1.3297	0.3085	0.6026	0.2708	-1.0	0.1344	0.324	0.4	0.581	0.669	-1.0	0.2333	0.7026	0.3085	0.669
No log	7.0	280	1.0214	0.431	0.7435	0.483	-1.0	0.2347	0.4499	0.4929	0.7	0.7929	-1.0	0.7667	0.7949	0.431	0.7929
No log	8.0	320	0.8291	0.5124	0.7755	0.6232	-1.0	0.4072	0.5266	0.5429	0.7905	0.8405	-1.0	0.8	0.8436	0.5124	0.8405
No log	9.0	360	0.9139	0.5458	0.858	0.5725	-1.0	0.1965	0.5704	0.5119	0.7476	0.8143	-1.0	0.7667	0.8179	0.5458	0.8143
No log	10.0	400	0.8602	0.6176	0.8772	0.7345	-1.0	0.4388	0.6333	0.5786	0.7786	0.8333	-1.0	0.6667	0.8462	0.6176	0.8333
No log	11.0	440	0.8314	0.6233	0.8991	0.7409	-1.0	0.4084	0.6434	0.5976	0.769	0.8286	-1.0	0.7	0.8385	0.6233	0.8286
No log	12.0	480	0.7951	0.6299	0.907	0.7521	-1.0	0.4821	0.6457	0.6381	0.7976	0.8429	-1.0	0.7667	0.8487	0.6299	0.8429
1.0466	13.0	520	0.7849	0.6541	0.9256	0.8048	-1.0	0.459	0.6738	0.6048	0.7929	0.8595	-1.0	0.8667	0.859	0.6541	0.8595
1.0466	14.0	560	0.7600	0.6694	0.9497	0.7896	-1.0	0.4651	0.688	0.669	0.7952	0.8524	-1.0	0.8667	0.8513	0.6694	0.8524
1.0466	15.0	600	0.7345	0.6434	0.9296	0.7621	-1.0	0.492	0.6578	0.6286	0.7738	0.8381	-1.0	0.7333	0.8462	0.6434	0.8381
1.0466	16.0	640	0.7626	0.6485	0.9363	0.7964	-1.0	0.4148	0.6752	0.6357	0.7929	0.8405	-1.0	0.7	0.8513	0.6485	0.8405
1.0466	17.0	680	0.7340	0.6734	0.9444	0.7902	-1.0	0.5253	0.691	0.6548	0.8024	0.8524	-1.0	0.7667	0.859	0.6734	0.8524
1.0466	18.0	720	0.6968	0.6594	0.9536	0.7763	-1.0	0.4151	0.6817	0.6429	0.8024	0.869	-1.0	0.8	0.8744	0.6594	0.869
1.0466	19.0	760	0.7251	0.6514	0.9568	0.7993	-1.0	0.3946	0.6734	0.65	0.8024	0.8595	-1.0	0.7	0.8718	0.6514	0.8595
1.0466	20.0	800	0.6883	0.6755	0.9532	0.7893	-1.0	0.4113	0.6979	0.6571	0.8143	0.8857	-1.0	0.8	0.8923	0.6755	0.8857
1.0466	21.0	840	0.7113	0.6548	0.9522	0.7777	-1.0	0.3957	0.6765	0.65	0.8024	0.869	-1.0	0.7667	0.8769	0.6548	0.869
1.0466	22.0	880	0.7312	0.6527	0.9488	0.7759	-1.0	0.3907	0.6758	0.6524	0.8024	0.8595	-1.0	0.7667	0.8667	0.6527	0.8595
1.0466	23.0	920	0.6949	0.6559	0.9482	0.7656	-1.0	0.4267	0.6767	0.6595	0.8119	0.881	-1.0	0.8333	0.8846	0.6559	0.881
1.0466	24.0	960	0.7081	0.6479	0.9505	0.7725	-1.0	0.3961	0.6695	0.6548	0.8048	0.869	-1.0	0.8333	0.8718	0.6479	0.869
0.5924	25.0	1000	0.7108	0.6531	0.9518	0.77	-1.0	0.4288	0.672	0.6571	0.8143	0.8738	-1.0	0.8333	0.8769	0.6531	0.8738
0.5924	26.0	1040	0.7020	0.6576	0.9505	0.7702	-1.0	0.4284	0.6776	0.6619	0.8119	0.869	-1.0	0.8	0.8744	0.6576	0.869
0.5924	27.0	1080	0.7090	0.6596	0.9518	0.7689	-1.0	0.3925	0.6816	0.6619	0.8119	0.8619	-1.0	0.7667	0.8692	0.6596	0.8619
0.5924	28.0	1120	0.7043	0.6589	0.952	0.7702	-1.0	0.3927	0.6807	0.6595	0.8167	0.8643	-1.0	0.7667	0.8718	0.6589	0.8643
0.5924	29.0	1160	0.7049	0.6558	0.952	0.7704	-1.0	0.3925	0.6776	0.6595	0.8143	0.8643	-1.0	0.7667	0.8718	0.6558	0.8643
0.5924	30.0	1200	0.7046	0.658	0.952	0.7705	-1.0	0.3924	0.6802	0.6619	0.8119	0.8643	-1.0	0.7667	0.8718	0.658	0.8643

Framework versions

Transformers 4.57.6
Pytorch 2.9.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

magomerob/yolo_finetuned_raccoons

yolo_finetuned_raccoons

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种