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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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- image_folder |
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metrics: |
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- accuracy |
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model-index: |
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- name: resnet-50-finetuned-FER2013-0.001 |
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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: image_folder |
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type: image_folder |
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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: 0.6847311228754528 |
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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-FER2013-0.001 |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co./microsoft/resnet-50) on the image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9002 |
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- Accuracy: 0.6847 |
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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: 0.001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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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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| 1.4723 | 1.0 | 224 | 1.3382 | 0.4887 | |
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| 1.2236 | 2.0 | 448 | 1.1090 | 0.5751 | |
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| 1.1728 | 3.0 | 672 | 1.0262 | 0.6158 | |
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| 1.1545 | 4.0 | 896 | 0.9717 | 0.6339 | |
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| 1.0776 | 5.0 | 1120 | 0.9885 | 0.6360 | |
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| 1.0183 | 6.0 | 1344 | 0.9475 | 0.6560 | |
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| 0.9856 | 7.0 | 1568 | 0.9114 | 0.6700 | |
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| 0.953 | 8.0 | 1792 | 0.9074 | 0.6767 | |
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| 0.9151 | 9.0 | 2016 | 0.9076 | 0.6833 | |
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| 0.9355 | 10.0 | 2240 | 0.9002 | 0.6847 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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