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metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: resnet-50-finetuned-hateful-meme-restructured-balanced
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.522

resnet-50-finetuned-hateful-meme-restructured-balanced

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6946
  • Accuracy: 0.522

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6941 0.98 47 0.6947 0.494
0.6906 1.99 95 0.6945 0.492
0.6885 2.99 143 0.6951 0.492
0.6873 4.0 191 0.6946 0.5
0.6851 4.98 238 0.6941 0.516
0.6813 5.99 286 0.6946 0.522
0.6817 6.99 334 0.6955 0.508
0.6849 8.0 382 0.6948 0.52
0.6834 8.98 429 0.6953 0.508
0.6758 9.84 470 0.6953 0.516

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3