resnet152-FV-finetuned-memes

This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6772
  • Accuracy: 0.7558
  • Precision: 0.7557
  • Recall: 0.7558
  • F1: 0.7546

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: 0.00012
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.5739 0.99 20 1.5427 0.4521 0.3131 0.4521 0.2880
1.4353 1.99 40 1.3786 0.4490 0.3850 0.4490 0.2791
1.3026 2.99 60 1.2734 0.4799 0.3073 0.4799 0.3393
1.1579 3.99 80 1.1378 0.5278 0.4300 0.5278 0.4143
1.0276 4.99 100 1.0231 0.5734 0.4497 0.5734 0.4865
0.8826 5.99 120 0.9228 0.6252 0.5983 0.6252 0.5637
0.766 6.99 140 0.8441 0.6662 0.6474 0.6662 0.6320
0.6732 7.99 160 0.8009 0.6901 0.6759 0.6901 0.6704
0.5653 8.99 180 0.7535 0.7218 0.7141 0.7218 0.7129
0.4957 9.99 200 0.7317 0.7257 0.7248 0.7257 0.7200
0.4534 10.99 220 0.6808 0.7434 0.7405 0.7434 0.7390
0.3792 11.99 240 0.6949 0.7450 0.7454 0.7450 0.7399
0.3489 12.99 260 0.6746 0.7496 0.7511 0.7496 0.7474
0.3113 13.99 280 0.6637 0.7573 0.7638 0.7573 0.7579
0.2947 14.99 300 0.6451 0.7589 0.7667 0.7589 0.7610
0.2776 15.99 320 0.6754 0.7543 0.7565 0.7543 0.7525
0.2611 16.99 340 0.6808 0.7550 0.7607 0.7550 0.7529
0.2428 17.99 360 0.7005 0.7457 0.7497 0.7457 0.7404
0.2346 18.99 380 0.6597 0.7573 0.7642 0.7573 0.7590
0.2367 19.99 400 0.6772 0.7558 0.7557 0.7558 0.7546

Framework versions

  • Transformers 4.24.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 2.6.1.dev0
  • Tokenizers 0.13.1
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Evaluation results