readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: aesthetic_attribute_classifier
    results: []
  widget:
  - text: Check your vertical on the main support; it looks a little off. I'd also like to see how it looks with a bit of the sky cropped from the photo
  
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # aesthetic_attribute_classifier
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [PCCD dataset](https://github.com/ivclab/DeepPhotoCritic-ICCV17).
  It achieves the following results on the evaluation set:
  - Loss: 0.3976
  - Precision: {'precision': 0.877129341279301}
  - Recall: {'recall': 0.8751381215469614}
  - F1: {'f1': 0.875529982855803}
  - Accuracy: {'accuracy': 0.8751381215469614}
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision                         | Recall                         | F1                         | Accuracy                         |
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:|
  | 0.452         | 1.0   | 1528 | 0.4109          | {'precision': 0.8632779077963935} | {'recall': 0.8615101289134438} | {'f1': 0.8618616182904953} | {'accuracy': 0.8615101289134438} |
  | 0.3099        | 2.0   | 3056 | 0.3976          | {'precision': 0.877129341279301}  | {'recall': 0.8751381215469614} | {'f1': 0.875529982855803}  | {'accuracy': 0.8751381215469614} |
  | 0.227         | 3.0   | 4584 | 0.4320          | {'precision': 0.876211408446225}  | {'recall': 0.874401473296501}  | {'f1': 0.8747427955387239} | {'accuracy': 0.874401473296501}  |
  | 0.1645        | 4.0   | 6112 | 0.4840          | {'precision': 0.8724641667216837} | {'recall': 0.8714548802946593} | {'f1': 0.8714577820909117} | {'accuracy': 0.8714548802946593} |
  | 0.1141        | 5.0   | 7640 | 0.5083          | {'precision': 0.8755445355051571} | {'recall': 0.8747697974217311} | {'f1': 0.8748766125899489} | {'accuracy': 0.8747697974217311} |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  