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detr

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detr
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End of training

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  1. README.md +15 -11
  2. model.safetensors +1 -1
  3. runs/Apr21_20-56-20_black-ubuntu/events.out.tfevents.1713722180.black-ubuntu +2 -2
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4507
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  ## Model description
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@@ -34,27 +34,31 @@ More information needed
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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: 16
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  - eval_batch_size: 8
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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: 4
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | 1.8557 | 0.48 | 300 | 1.8996 |
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- | 1.7822 | 0.96 | 600 | 1.7404 |
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- | 1.657 | 1.44 | 900 | 1.6266 |
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- | 1.5922 | 1.92 | 1200 | 1.5711 |
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- | 1.5762 | 2.4 | 1500 | 1.5155 |
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- | 1.5673 | 2.88 | 1800 | 1.4816 |
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- | 1.4927 | 3.36 | 2100 | 1.4529 |
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- | 1.5326 | 3.84 | 2400 | 1.4507 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8756
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 8
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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: 2
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | 1.7508 | 0.16 | 300 | 1.7472 |
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+ | 1.412 | 0.32 | 600 | 1.3611 |
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+ | 1.2549 | 0.48 | 900 | 1.1682 |
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+ | 1.0701 | 0.64 | 1200 | 1.0696 |
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+ | 0.9812 | 0.8 | 1500 | 1.0103 |
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+ | 1.0346 | 0.96 | 1800 | 0.9782 |
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+ | 1.014 | 1.12 | 2100 | 0.9429 |
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+ | 0.9691 | 1.28 | 2400 | 0.9248 |
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+ | 0.9749 | 1.44 | 2700 | 0.8947 |
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+ | 0.9707 | 1.6 | 3000 | 0.8887 |
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+ | 0.938 | 1.76 | 3300 | 0.8889 |
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+ | 0.9477 | 1.92 | 3600 | 0.8756 |
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  ### Framework versions
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