MT-proud-rain-95 / README.md
Amine
MT-proud-rain-95
8e0e6ec
metadata
base_model: toobiza/MT-ancient-spaceship-83
tags:
  - generated_from_trainer
model-index:
  - name: MT-proud-rain-95
    results: []

MT-proud-rain-95

This model is a fine-tuned version of toobiza/MT-ancient-spaceship-83 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1562
  • Loss Ce: 0.0000
  • Loss Bbox: 0.0213
  • Cardinality Error: 1.0
  • Giou: 97.5230

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Loss Ce Loss Bbox Cardinality Error Giou
0.1665 0.48 200 0.2101 0.0000 0.0293 1.0 96.8141
0.1967 0.97 400 0.1844 0.0000 0.0255 1.0 97.1659
0.1624 1.45 600 0.1833 0.0000 0.0253 1.0 97.1706
0.1594 1.93 800 0.1720 0.0000 0.0237 1.0 97.3363
0.1598 2.42 1000 0.1729 0.0000 0.0238 1.0 97.3105
0.1941 2.9 1200 0.1494 0.0000 0.0203 1.0 97.6099
0.1223 3.38 1400 0.1525 0.0000 0.0209 1.0 97.6036
0.1514 3.86 1600 0.1512 0.0000 0.0207 1.0 97.6045
0.1585 4.35 1800 0.1569 0.0000 0.0215 1.0 97.5391
0.128 4.83 2000 0.1535 0.0000 0.0210 1.0 97.5658
0.1089 5.31 2200 0.1594 0.0000 0.0220 1.0 97.5180
0.1624 5.8 2400 0.1650 0.0000 0.0228 1.0 97.4441
0.1074 6.28 2600 0.1648 0.0000 0.0227 1.0 97.4209
0.1693 6.76 2800 0.1554 0.0000 0.0212 1.0 97.5341
0.1075 7.25 3000 0.1595 0.0000 0.0218 1.0 97.4777
0.1271 7.73 3200 0.1570 0.0000 0.0215 1.0 97.5156
0.1293 8.21 3400 0.1549 0.0000 0.0211 1.0 97.5331
0.1143 8.7 3600 0.1564 0.0000 0.0214 1.0 97.5335
0.0966 9.18 3800 0.1555 0.0000 0.0213 1.0 97.5400
0.104 9.66 4000 0.1562 0.0000 0.0213 1.0 97.5230

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.13.3