practica_2

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: 0.4715

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	17	1.0138
No log	2.0	34	0.9049
1.0786	3.0	51	0.7578
1.0786	4.0	68	0.6868
1.0786	5.0	85	0.6194
0.6333	6.0	102	0.6252
0.6333	7.0	119	0.5737
0.6333	8.0	136	0.5558
0.4516	9.0	153	0.5284
0.4516	10.0	170	0.5306
0.4516	11.0	187	0.5385
0.3783	12.0	204	0.5209
0.3783	13.0	221	0.5044
0.3783	14.0	238	0.5468
0.3213	15.0	255	0.4953
0.3213	16.0	272	0.4678
0.3213	17.0	289	0.4901
0.2909	18.0	306	0.5269
0.2909	19.0	323	0.4885
0.2909	20.0	340	0.4361
0.2532	21.0	357	0.4837
0.2532	22.0	374	0.4971
0.2532	23.0	391	0.4813
0.2312	24.0	408	0.4834
0.2312	25.0	425	0.4834
0.2312	26.0	442	0.4724
0.2075	27.0	459	0.4669
0.2075	28.0	476	0.4541
0.2075	29.0	493	0.4681
0.1792	30.0	510	0.5126
0.1792	31.0	527	0.4681
0.1792	32.0	544	0.4758
0.1717	33.0	561	0.4669
0.1717	34.0	578	0.4921
0.1717	35.0	595	0.4918
0.1669	36.0	612	0.4759
0.1669	37.0	629	0.4758
0.1669	38.0	646	0.4838
0.1614	39.0	663	0.4839
0.1614	40.0	680	0.4564
0.1614	41.0	697	0.4223
0.1492	42.0	714	0.5006
0.1492	43.0	731	0.4495
0.1492	44.0	748	0.4679
0.1374	45.0	765	0.4811
0.1374	46.0	782	0.4657
0.1374	47.0	799	0.4606
0.1326	48.0	816	0.4646
0.1326	49.0	833	0.4896
0.1323	50.0	850	0.4963
0.1323	51.0	867	0.4636
0.1323	52.0	884	0.4806
0.1255	53.0	901	0.4568
0.1255	54.0	918	0.4523
0.1255	55.0	935	0.4607
0.1178	56.0	952	0.4678
0.1178	57.0	969	0.4743
0.1178	58.0	986	0.4830
0.1105	59.0	1003	0.4721
0.1105	60.0	1020	0.5013
0.1105	61.0	1037	0.4657
0.1108	62.0	1054	0.4672
0.1108	63.0	1071	0.4606
0.1108	64.0	1088	0.4321
0.1085	65.0	1105	0.4613
0.1085	66.0	1122	0.4911
0.1085	67.0	1139	0.5074
0.1	68.0	1156	0.4333
0.1	69.0	1173	0.4372
0.1	70.0	1190	0.4237
0.0987	71.0	1207	0.4571
0.0987	72.0	1224	0.4450
0.0987	73.0	1241	0.4535
0.0943	74.0	1258	0.4631
0.0943	75.0	1275	0.4858
0.0943	76.0	1292	0.4881
0.0906	77.0	1309	0.4838
0.0906	78.0	1326	0.4543
0.0906	79.0	1343	0.4522
0.0933	80.0	1360	0.4555
0.0933	81.0	1377	0.4306
0.0933	82.0	1394	0.5012
0.089	83.0	1411	0.4685
0.089	84.0	1428	0.4543
0.089	85.0	1445	0.4630
0.0812	86.0	1462	0.4715
0.0812	87.0	1479	0.4896
0.0812	88.0	1496	0.4587
0.0779	89.0	1513	0.4929
0.0779	90.0	1530	0.4443
0.0779	91.0	1547	0.4598
0.0783	92.0	1564	0.4413
0.0783	93.0	1581	0.4412
0.0783	94.0	1598	0.4456
0.077	95.0	1615	0.5037
0.077	96.0	1632	0.4462
0.077	97.0	1649	0.4611
0.0819	98.0	1666	0.4617
0.0819	99.0	1683	0.4344
0.0765	100.0	1700	0.4715

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0