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detr-resnet-50_finetuned_cppe5

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End of training

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  1. README.md +44 -16
  2. model.safetensors +1 -1
  3. runs/Apr14_20-26-09_c24f0b7a0a52/events.out.tfevents.1713126586.c24f0b7a0a52.4475.5 +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 [WKLI22/detr-resnet-50_finetuned_cppe5](https://huggingface.co/WKLI22/detr-resnet-50_finetuned_cppe5) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5201
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  ## Model description
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@@ -42,27 +42,55 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 102
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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: 1
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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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- | 0.4924 | 0.07 | 2 | 0.5583 |
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- | 0.5373 | 0.14 | 4 | 0.5413 |
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- | 0.5166 | 0.21 | 6 | 0.5306 |
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- | 0.5765 | 0.28 | 8 | 0.5416 |
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- | 0.5405 | 0.36 | 10 | 0.5450 |
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- | 0.5289 | 0.43 | 12 | 0.5315 |
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- | 0.5312 | 0.5 | 14 | 0.5215 |
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- | 0.537 | 0.57 | 16 | 0.5313 |
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- | 0.5417 | 0.64 | 18 | 0.5255 |
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- | 0.5214 | 0.71 | 20 | 0.5251 |
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- | 0.5143 | 0.78 | 22 | 0.5309 |
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- | 0.5326 | 0.85 | 24 | 0.5269 |
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- | 0.5289 | 0.92 | 26 | 0.5310 |
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- | 0.5524 | 0.99 | 28 | 0.5201 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [WKLI22/detr-resnet-50_finetuned_cppe5](https://huggingface.co/WKLI22/detr-resnet-50_finetuned_cppe5) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4986
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  ## Model description
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  - total_train_batch_size: 102
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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: 3
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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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+ | 0.4724 | 0.07 | 2 | 0.5355 |
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+ | 0.4907 | 0.14 | 4 | 0.5277 |
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+ | 0.498 | 0.21 | 6 | 0.5214 |
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+ | 0.534 | 0.28 | 8 | 0.5274 |
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+ | 0.5305 | 0.36 | 10 | 0.5294 |
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+ | 0.5096 | 0.43 | 12 | 0.5273 |
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+ | 0.5149 | 0.5 | 14 | 0.5158 |
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+ | 0.5099 | 0.57 | 16 | 0.5163 |
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+ | 0.5308 | 0.64 | 18 | 0.5217 |
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+ | 0.5164 | 0.71 | 20 | 0.5076 |
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+ | 0.4932 | 0.78 | 22 | 0.5050 |
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+ | 0.5151 | 0.85 | 24 | 0.5102 |
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+ | 0.4982 | 0.92 | 26 | 0.5044 |
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+ | 0.526 | 0.99 | 28 | 0.5096 |
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+ | 0.5034 | 1.07 | 30 | 0.4980 |
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+ | 0.5155 | 1.14 | 32 | 0.5067 |
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+ | 0.513 | 1.21 | 34 | 0.5011 |
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+ | 0.5019 | 1.28 | 36 | 0.5066 |
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+ | 0.4704 | 1.35 | 38 | 0.5094 |
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+ | 0.5404 | 1.42 | 40 | 0.5126 |
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+ | 0.5263 | 1.49 | 42 | 0.5062 |
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+ | 0.4729 | 1.56 | 44 | 0.5223 |
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+ | 0.5032 | 1.63 | 46 | 0.5073 |
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+ | 0.476 | 1.7 | 48 | 0.5111 |
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+ | 0.4823 | 1.78 | 50 | 0.5094 |
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+ | 0.5223 | 1.85 | 52 | 0.5042 |
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+ | 0.4855 | 1.92 | 54 | 0.4962 |
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+ | 0.5038 | 1.99 | 56 | 0.5006 |
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+ | 0.5196 | 2.06 | 58 | 0.5022 |
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+ | 0.4847 | 2.13 | 60 | 0.4943 |
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+ | 0.4697 | 2.2 | 62 | 0.5007 |
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+ | 0.4893 | 2.27 | 64 | 0.5041 |
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+ | 0.4939 | 2.34 | 66 | 0.4910 |
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+ | 0.5093 | 2.41 | 68 | 0.4974 |
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+ | 0.4884 | 2.49 | 70 | 0.4962 |
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+ | 0.5087 | 2.56 | 72 | 0.5081 |
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+ | 0.4889 | 2.63 | 74 | 0.4974 |
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+ | 0.486 | 2.7 | 76 | 0.5003 |
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+ | 0.4747 | 2.77 | 78 | 0.4917 |
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+ | 0.5028 | 2.84 | 80 | 0.4849 |
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+ | 0.5366 | 2.91 | 82 | 0.4931 |
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+ | 0.5083 | 2.98 | 84 | 0.4986 |
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  ### Framework versions
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