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detr-resnet-50_fine_tuned_loc-2023
This model is a fine-tuned version of facebook/detr-resnet-50 on the loc_beyond_words dataset. It achieves the following results on the evaluation set:
- Loss: 0.8784
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: 0.0001
- 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: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.731 | 0.16 | 50 | 2.6356 |
| 2.4875 | 0.31 | 100 | 2.2348 |
| 2.1786 | 0.47 | 150 | 2.1148 |
| 1.9845 | 0.62 | 200 | 1.8847 |
| 1.8507 | 0.78 | 250 | 1.8331 |
| 1.6813 | 0.94 | 300 | 1.5620 |
| 1.5613 | 1.09 | 350 | 1.5898 |
| 1.4966 | 1.25 | 400 | 1.4161 |
| 1.4831 | 1.41 | 450 | 1.4831 |
| 1.4587 | 1.56 | 500 | 1.3218 |
| 1.433 | 1.72 | 550 | 1.3529 |
| 1.33 | 1.88 | 600 | 1.2453 |
| 1.2842 | 2.03 | 650 | 1.2956 |
| 1.2807 | 2.19 | 700 | 1.1993 |
| 1.1767 | 2.34 | 750 | 1.1557 |
| 1.2134 | 2.5 | 800 | 1.1393 |
| 1.1897 | 2.66 | 850 | 1.2016 |
| 1.1784 | 2.81 | 900 | 1.1235 |
| 1.2016 | 2.97 | 950 | 1.1378 |
| 1.06 | 3.12 | 1000 | 1.0803 |
| 1.1124 | 3.28 | 1050 | 1.1145 |
| 1.1191 | 3.44 | 1100 | 1.0523 |
| 1.0819 | 3.59 | 1150 | 1.0165 |
| 1.1196 | 3.75 | 1200 | 1.0349 |
| 1.0534 | 3.91 | 1250 | 1.0441 |
| 1.0365 | 4.06 | 1300 | 1.1177 |
| 0.9853 | 4.22 | 1350 | 1.0721 |
| 0.9984 | 4.38 | 1400 | 0.9923 |
| 0.9802 | 4.53 | 1450 | 1.0079 |
| 1.04 | 4.69 | 1500 | 1.0198 |
| 1.098 | 4.84 | 1550 | 0.9788 |
| 1.079 | 5.0 | 1600 | 1.0291 |
| 1.0664 | 5.16 | 1650 | 0.9691 |
| 0.9715 | 5.31 | 1700 | 0.9380 |
| 0.9723 | 5.47 | 1750 | 1.0164 |
| 1.0019 | 5.62 | 1800 | 1.0064 |
| 0.9895 | 5.78 | 1850 | 1.0364 |
| 0.9835 | 5.94 | 1900 | 0.9848 |
| 0.994 | 6.09 | 1950 | 0.9353 |
| 0.9693 | 6.25 | 2000 | 0.9425 |
| 0.9413 | 6.41 | 2050 | 0.9173 |
| 0.9375 | 6.56 | 2100 | 0.9663 |
| 0.952 | 6.72 | 2150 | 0.8951 |
| 0.8927 | 6.88 | 2200 | 0.9099 |
| 0.8777 | 7.03 | 2250 | 0.9238 |
| 0.8976 | 7.19 | 2300 | 0.9715 |
| 0.9451 | 7.34 | 2350 | 0.9373 |
| 0.8972 | 7.5 | 2400 | 0.8959 |
| 0.9393 | 7.66 | 2450 | 1.0062 |
| 0.9 | 7.81 | 2500 | 0.8920 |
| 0.915 | 7.97 | 2550 | 0.8833 |
| 0.9018 | 8.12 | 2600 | 0.8671 |
| 0.8272 | 8.28 | 2650 | 0.9304 |
| 0.943 | 8.44 | 2700 | 0.8593 |
| 0.8667 | 8.59 | 2750 | 0.8875 |
| 0.871 | 8.75 | 2800 | 0.8457 |
| 0.9023 | 8.91 | 2850 | 0.8448 |
| 0.8733 | 9.06 | 2900 | 0.8261 |
| 0.8686 | 9.22 | 2950 | 0.8489 |
| 0.8412 | 9.38 | 3000 | 0.8244 |
| 0.8385 | 9.53 | 3050 | 0.8830 |
| 0.891 | 9.69 | 3100 | 0.8349 |
| 0.8692 | 9.84 | 3150 | 0.8672 |
| 0.8247 | 10.0 | 3200 | 0.8811 |
| 0.799 | 10.16 | 3250 | 0.8784 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
