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--- |
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base_model: toobiza/MT-ancient-spaceship-83 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: MT-proud-rain-95 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# MT-proud-rain-95 |
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This model is a fine-tuned version of [toobiza/MT-ancient-spaceship-83](https://huggingface.co/toobiza/MT-ancient-spaceship-83) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1562 |
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- Loss Ce: 0.0000 |
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- Loss Bbox: 0.0213 |
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- Cardinality Error: 1.0 |
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- Giou: 97.5230 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Loss Ce | Loss Bbox | Cardinality Error | Giou | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-----------------:|:-------:| |
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| 0.1665 | 0.48 | 200 | 0.2101 | 0.0000 | 0.0293 | 1.0 | 96.8141 | |
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| 0.1967 | 0.97 | 400 | 0.1844 | 0.0000 | 0.0255 | 1.0 | 97.1659 | |
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| 0.1624 | 1.45 | 600 | 0.1833 | 0.0000 | 0.0253 | 1.0 | 97.1706 | |
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| 0.1594 | 1.93 | 800 | 0.1720 | 0.0000 | 0.0237 | 1.0 | 97.3363 | |
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| 0.1598 | 2.42 | 1000 | 0.1729 | 0.0000 | 0.0238 | 1.0 | 97.3105 | |
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| 0.1941 | 2.9 | 1200 | 0.1494 | 0.0000 | 0.0203 | 1.0 | 97.6099 | |
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| 0.1223 | 3.38 | 1400 | 0.1525 | 0.0000 | 0.0209 | 1.0 | 97.6036 | |
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| 0.1514 | 3.86 | 1600 | 0.1512 | 0.0000 | 0.0207 | 1.0 | 97.6045 | |
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| 0.1585 | 4.35 | 1800 | 0.1569 | 0.0000 | 0.0215 | 1.0 | 97.5391 | |
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| 0.128 | 4.83 | 2000 | 0.1535 | 0.0000 | 0.0210 | 1.0 | 97.5658 | |
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| 0.1089 | 5.31 | 2200 | 0.1594 | 0.0000 | 0.0220 | 1.0 | 97.5180 | |
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| 0.1624 | 5.8 | 2400 | 0.1650 | 0.0000 | 0.0228 | 1.0 | 97.4441 | |
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| 0.1074 | 6.28 | 2600 | 0.1648 | 0.0000 | 0.0227 | 1.0 | 97.4209 | |
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| 0.1693 | 6.76 | 2800 | 0.1554 | 0.0000 | 0.0212 | 1.0 | 97.5341 | |
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| 0.1075 | 7.25 | 3000 | 0.1595 | 0.0000 | 0.0218 | 1.0 | 97.4777 | |
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| 0.1271 | 7.73 | 3200 | 0.1570 | 0.0000 | 0.0215 | 1.0 | 97.5156 | |
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| 0.1293 | 8.21 | 3400 | 0.1549 | 0.0000 | 0.0211 | 1.0 | 97.5331 | |
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| 0.1143 | 8.7 | 3600 | 0.1564 | 0.0000 | 0.0214 | 1.0 | 97.5335 | |
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| 0.0966 | 9.18 | 3800 | 0.1555 | 0.0000 | 0.0213 | 1.0 | 97.5400 | |
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| 0.104 | 9.66 | 4000 | 0.1562 | 0.0000 | 0.0213 | 1.0 | 97.5230 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.13.3 |
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