readme: ---
  tags:
  - autotrain
  - vision
  - image-classification
  datasets:
  - lewtun/dog_food
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  library_name: transformers
  co2_eq_emissions:
    emissions: 6.799888815236616
  eval_info:
    col_mapping: test
  model-index:
  - name: NimaBoscarino/dog_food
    results:
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        name: lewtun/dog_food
        type: lewtun/dog_food
        config: lewtun--dog_food
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 1.0
        verified: true
      - name: Precision Macro
        type: precision
        value: 1.0
        verified: true
      - name: Precision Micro
        type: precision
        value: 1.0
        verified: true
      - name: Precision Weighted
        type: precision
        value: 1.0
        verified: true
      - name: Recall Macro
        type: recall
        value: 1.0
        verified: true
      - name: Recall Micro
        type: recall
        value: 1.0
        verified: true
      - name: Recall Weighted
        type: recall
        value: 1.0
        verified: true
      - name: F1 Macro
        type: f1
        value: 1.0
        verified: true
      - name: F1 Micro
        type: f1
        value: 1.0
        verified: true
      - name: F1 Weighted
        type: f1
        value: 1.0
        verified: true
      - name: loss
        type: loss
        value: 1.848173087637406e-05
        verified: true
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 1647758504
  - CO2 Emissions (in grams): 6.7999
  
  ## Validation Metrics
  
  - Loss: 0.001
  - Accuracy: 1.000
  - Macro F1: 1.000
  - Micro F1: 1.000
  - Weighted F1: 1.000
  - Macro Precision: 1.000
  - Micro Precision: 1.000
  - Weighted Precision: 1.000
  - Macro Recall: 1.000
  - Micro Recall: 1.000
  - Weighted Recall: 1.000