detr-resnet-50_finetuned_bbatch
This model is a fine-tuned version of facebook/detr-resnet-50 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3729
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
4.0202 | 4.17 | 50 | 3.2912 |
3.2043 | 8.33 | 100 | 3.1033 |
3.0326 | 12.5 | 150 | 3.0882 |
2.957 | 16.67 | 200 | 2.9938 |
2.8396 | 20.83 | 250 | 2.8404 |
2.7485 | 25.0 | 300 | 2.8785 |
2.6515 | 29.17 | 350 | 2.7908 |
2.5651 | 33.33 | 400 | 2.7162 |
2.5172 | 37.5 | 450 | 2.6793 |
2.434 | 41.67 | 500 | 2.6200 |
2.3921 | 45.83 | 550 | 2.6078 |
2.37 | 50.0 | 600 | 2.5634 |
2.3211 | 54.17 | 650 | 2.5730 |
2.2658 | 58.33 | 700 | 2.4883 |
2.2487 | 62.5 | 750 | 2.6039 |
2.2001 | 66.67 | 800 | 2.4645 |
2.1767 | 70.83 | 850 | 2.5015 |
2.1533 | 75.0 | 900 | 2.4727 |
2.1489 | 79.17 | 950 | 2.4663 |
2.1181 | 83.33 | 1000 | 2.4865 |
2.0739 | 87.5 | 1050 | 2.4522 |
2.0664 | 91.67 | 1100 | 2.4344 |
2.0347 | 95.83 | 1150 | 2.4347 |
2.0175 | 100.0 | 1200 | 2.4883 |
1.9989 | 104.17 | 1250 | 2.4523 |
1.9773 | 108.33 | 1300 | 2.4971 |
1.9473 | 112.5 | 1350 | 2.5026 |
1.9431 | 116.67 | 1400 | 2.4029 |
1.9274 | 120.83 | 1450 | 2.4887 |
1.907 | 125.0 | 1500 | 2.4216 |
1.9006 | 129.17 | 1550 | 2.4143 |
1.8775 | 133.33 | 1600 | 2.4107 |
1.8795 | 137.5 | 1650 | 2.3722 |
1.8582 | 141.67 | 1700 | 2.3952 |
1.8538 | 145.83 | 1750 | 2.3964 |
1.8409 | 150.0 | 1800 | 2.4083 |
1.8302 | 154.17 | 1850 | 2.4013 |
1.8203 | 158.33 | 1900 | 2.3768 |
1.8112 | 162.5 | 1950 | 2.4538 |
1.7935 | 166.67 | 2000 | 2.3903 |
1.8035 | 170.83 | 2050 | 2.3707 |
1.7955 | 175.0 | 2100 | 2.3588 |
1.7786 | 179.17 | 2150 | 2.3775 |
1.7988 | 183.33 | 2200 | 2.3376 |
1.7808 | 187.5 | 2250 | 2.3629 |
1.7802 | 191.67 | 2300 | 2.4191 |
1.769 | 195.83 | 2350 | 2.3897 |
1.7677 | 200.0 | 2400 | 2.3729 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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