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--- |
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license: apache-2.0 |
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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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metrics: |
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- accuracy |
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model-index: |
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- name: resnet-50-finetuned-omar |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9143576826196473 |
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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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# resnet-50-finetuned-omar |
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This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2645 |
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- Accuracy: 0.9144 |
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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: 5e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.0695 | 1.0 | 111 | 1.0576 | 0.5315 | |
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| 0.971 | 2.0 | 223 | 0.9366 | 0.5416 | |
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| 0.8121 | 3.0 | 334 | 0.7493 | 0.7103 | |
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| 0.6861 | 4.0 | 446 | 0.5625 | 0.8363 | |
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| 0.606 | 5.0 | 557 | 0.4239 | 0.8816 | |
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| 0.5001 | 6.0 | 669 | 0.3159 | 0.9219 | |
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| 0.4704 | 7.0 | 780 | 0.3254 | 0.9118 | |
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| 0.4332 | 8.0 | 892 | 0.2808 | 0.9194 | |
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| 0.4432 | 9.0 | 1003 | 0.2854 | 0.9219 | |
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| 0.4768 | 10.0 | 1115 | 0.2782 | 0.9219 | |
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| 0.4432 | 11.0 | 1226 | 0.2768 | 0.9320 | |
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| 0.4752 | 12.0 | 1338 | 0.2744 | 0.9219 | |
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| 0.489 | 13.0 | 1449 | 0.2693 | 0.9194 | |
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| 0.3743 | 14.0 | 1561 | 0.2715 | 0.9270 | |
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| 0.417 | 14.93 | 1665 | 0.2645 | 0.9144 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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