readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: swin-food102
    results: []
  datasets:
  - juliensimon/food102
  ---
  
  # swin-food102
  
  This model is a fine-tuned version of [juliensimon/autotrain-food101-1471154053](https://huggingface.co/juliensimon/autotrain-food101-1471154053) on the [food102](https://huggingface.co/datasets/juliensimon/food102) dataset, namely the [food101](https://huggingface.co/datasets/food101) dataset with an extra class generated with a Stable Diffusion model. 
  
  A detailed walk-through is available on [YouTube](https://youtu.be/sIe0eo3fYQ4).
  
  The achieves the following results on the evaluation set:
  - Loss: 0.2510
  - Accuracy: 0.9338
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1648        | 1.0   | 597  | 0.3118          | 0.9218   |
  | 0.31          | 2.0   | 1194 | 0.2606          | 0.9322   |
  | 0.2488        | 3.0   | 1791 | 0.2510          | 0.9338   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.4.0
  - Tokenizers 0.13.1
  