practica_2_kangaroo

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: 0.6938

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: 1e-05
• train_batch_size: 8
• 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: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	19	0.9975
No log	2.0	38	0.8337
0.8733	3.0	57	0.9003
0.8733	4.0	76	0.7992
0.8733	5.0	95	0.7225
0.577	6.0	114	0.8095
0.577	7.0	133	0.8329
0.4498	8.0	152	0.7701
0.4498	9.0	171	0.7072
0.4498	10.0	190	0.7774
0.3697	11.0	209	0.7421
0.3697	12.0	228	0.6773
0.3697	13.0	247	0.6309
0.3348	14.0	266	0.7009
0.3348	15.0	285	0.7800
0.2907	16.0	304	0.7364
0.2907	17.0	323	0.6137
0.2907	18.0	342	0.6721
0.2595	19.0	361	0.6353
0.2595	20.0	380	0.6392
0.2595	21.0	399	0.6280
0.244	22.0	418	0.5759
0.244	23.0	437	0.5613
0.2154	24.0	456	0.6886
0.2154	25.0	475	0.6181
0.2154	26.0	494	0.6223
0.1989	27.0	513	0.5730
0.1989	28.0	532	0.6037
0.1848	29.0	551	0.7125
0.1848	30.0	570	0.6218
0.1848	31.0	589	0.5871
0.1686	32.0	608	0.6126
0.1686	33.0	627	0.6017
0.1686	34.0	646	0.7448
0.1667	35.0	665	0.6713
0.1667	36.0	684	0.7800
0.1584	37.0	703	0.7249
0.1584	38.0	722	0.6830
0.1584	39.0	741	0.6575
0.1424	40.0	760	0.6051
0.1424	41.0	779	0.6029
0.1424	42.0	798	0.6182
0.1399	43.0	817	0.5813
0.1399	44.0	836	0.6202
0.1312	45.0	855	0.6301
0.1312	46.0	874	0.7338
0.1312	47.0	893	0.7173
0.1278	48.0	912	0.6548
0.1278	49.0	931	0.7101
0.1166	50.0	950	0.6286
0.1166	51.0	969	0.5544
0.1166	52.0	988	0.6381
0.1108	53.0	1007	0.7138
0.1108	54.0	1026	0.6907
0.1108	55.0	1045	0.7450
0.1097	56.0	1064	0.7085
0.1097	57.0	1083	0.6120
0.1063	58.0	1102	0.6301
0.1063	59.0	1121	0.6081
0.1063	60.0	1140	0.5714
0.1025	61.0	1159	0.6341
0.1025	62.0	1178	0.5742
0.1025	63.0	1197	0.6593
0.1017	64.0	1216	0.6832
0.1017	65.0	1235	0.6422
0.0931	66.0	1254	0.6032
0.0931	67.0	1273	0.6909
0.0931	68.0	1292	0.6501
0.0888	69.0	1311	0.6737
0.0888	70.0	1330	0.7715
0.0888	71.0	1349	0.5660
0.0801	72.0	1368	0.5877
0.0801	73.0	1387	0.6078
0.0848	74.0	1406	0.5911
0.0848	75.0	1425	0.6001
0.0848	76.0	1444	0.7010
0.0827	77.0	1463	0.5590
0.0827	78.0	1482	0.5833
0.0767	79.0	1501	0.5435
0.0767	80.0	1520	0.5577
0.0767	81.0	1539	0.6186
0.0724	82.0	1558	0.6701
0.0724	83.0	1577	0.6461
0.0724	84.0	1596	0.5634
0.0707	85.0	1615	0.7126
0.0707	86.0	1634	0.6726
0.0707	87.0	1653	0.5629
0.0707	88.0	1672	0.6799
0.0707	89.0	1691	0.6672
0.0707	90.0	1710	0.7435
0.0707	91.0	1729	0.6398
0.0707	92.0	1748	0.6162
0.0802	93.0	1767	0.5773
0.0802	94.0	1786	0.6004
0.0659	95.0	1805	0.6375
0.0659	96.0	1824	0.6713
0.0659	97.0	1843	0.7374
0.0651	98.0	1862	0.6655
0.0651	99.0	1881	0.7368
0.0624	100.0	1900	0.6938

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.1
• Tokenizers 0.21.0