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MT-proud-rain-95

Object Detection
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PyTorch
table-transformer
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MT-proud-rain-95

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  1. README.md +79 -0
  2. pytorch_model.bin +1 -1
README.md ADDED
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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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+
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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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+
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+ # MT-proud-rain-95
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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