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--- |
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license: apache-2.0 |
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base_model: facebook/detr-resnet-101 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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model-index: |
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- name: detr-resnet-101_adamw_torch_finetuned_food-roboflow |
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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-101_adamw_torch_finetuned_food-roboflow |
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This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co./facebook/detr-resnet-101) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0549 |
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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: 8 |
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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: 15 |
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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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| 6.854 | 0.77 | 50 | 6.6564 | |
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| 5.8031 | 1.54 | 100 | 5.7440 | |
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| 5.0476 | 2.31 | 150 | 4.8884 | |
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| 4.1629 | 3.08 | 200 | 3.9522 | |
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| 3.4843 | 3.85 | 250 | 3.5612 | |
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| 3.0905 | 4.62 | 300 | 3.7328 | |
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| 3.0965 | 5.38 | 350 | 3.3028 | |
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| 2.8764 | 6.15 | 400 | 3.1671 | |
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| 2.8236 | 6.92 | 450 | 3.2082 | |
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| 2.8467 | 7.69 | 500 | 3.0500 | |
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| 2.6769 | 8.46 | 550 | 3.0538 | |
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| 2.7194 | 9.23 | 600 | 3.0982 | |
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| 2.6311 | 10.0 | 650 | 3.0520 | |
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| 2.6772 | 10.77 | 700 | 2.9950 | |
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| 2.577 | 11.54 | 750 | 3.0134 | |
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| 2.6274 | 12.31 | 800 | 3.0523 | |
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| 2.597 | 13.08 | 850 | 3.0120 | |
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| 2.56 | 13.85 | 900 | 2.9795 | |
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| 2.5803 | 14.62 | 950 | 3.0549 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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