---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
- lewtun/dog_food
metrics:
- accuracy
model-index:
- name: resnet-18-finetuned-dogfood
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: lewtun/dog_food
      type: lewtun/dog_food
      args: lewtun--dog_food
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.896
  - 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: 0.8466666666666667
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8850127293141284
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.8466666666666667
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.8939157698241645
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8555113273379528
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.8466666666666667
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.8466666666666667
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8431399312051647
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.8466666666666667
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.8430272582865614
      verified: true
    - name: loss
      type: loss
      value: 0.3633290231227875
      verified: true
    - name: matthews_correlation
      type: matthews_correlation
      value: 0.7973101366252381
      verified: true
---

<!-- 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. -->

# resnet-18-finetuned-dogfood

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the lewtun/dog_food dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2991
- Accuracy: 0.896

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.846         | 1.0   | 16   | 0.2662          | 0.9156   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1