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--- |
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license: apache-2.0 |
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base_model: WKLI22/detr-resnet-50_finetuned_cppe5 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: detr-resnet-50_finetuned_cppe5 |
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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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# detr-resnet-50_finetuned_cppe5 |
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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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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: 17 |
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- eval_batch_size: 17 |
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- seed: 42 |
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- gradient_accumulation_steps: 6 |
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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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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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