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README.md
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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-FER2013-0.003
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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: 0.6971301198105322
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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-FER2013-0.003
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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: 0.9036
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- Accuracy: 0.6971
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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: 0.003
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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: 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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| 1.4393 | 1.0 | 224 | 1.2746 | 0.5173 |
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| 1.2564 | 2.0 | 448 | 1.1456 | 0.5542 |
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| 1.218 | 3.0 | 672 | 1.1102 | 0.5816 |
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| 1.1919 | 4.0 | 896 | 1.0255 | 0.6151 |
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| 1.1222 | 5.0 | 1120 | 1.0257 | 0.6167 |
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| 1.0925 | 6.0 | 1344 | 0.9676 | 0.6317 |
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| 1.0241 | 7.0 | 1568 | 0.9406 | 0.6510 |
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| 1.0015 | 8.0 | 1792 | 0.9465 | 0.6532 |
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| 0.987 | 9.0 | 2016 | 0.9002 | 0.6748 |
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| 0.9768 | 10.0 | 2240 | 0.9086 | 0.6737 |
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| 0.9408 | 11.0 | 2464 | 0.8975 | 0.6793 |
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| 0.8907 | 12.0 | 2688 | 0.8966 | 0.6769 |
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| 0.8051 | 13.0 | 2912 | 0.9142 | 0.6826 |
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| 0.8169 | 14.0 | 3136 | 0.9082 | 0.6870 |
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| 0.7729 | 15.0 | 3360 | 0.9036 | 0.6971 |
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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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