README.md

metadata

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

resnet-18-finetuned-dogfood

This model is a fine-tuned version of 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