jayanta / mit-b2-VF2-finetuned-memes

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Total runs: 11
24-hour runs: 0
7-day runs: 1
30-day runs: 0
Model's Last Updated: October 22 2022
image-classification

Introduction of mit-b2-VF2-finetuned-memes

Model Details of mit-b2-VF2-finetuned-memes

mit-b2-VF2-finetuned-memes

This model is a fine-tuned version of nvidia/mit-b2 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6547
  • Accuracy: 0.8308
  • Precision: 0.8272
  • Recall: 0.8308
  • F1: 0.8287
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
1.3077 0.99 20 1.1683 0.5549 0.5621 0.5549 0.5286
0.9359 1.99 40 0.8573 0.6731 0.6807 0.6731 0.6535
0.7219 2.99 60 0.7106 0.7272 0.7359 0.7272 0.7246
0.6013 3.99 80 0.6445 0.7550 0.7686 0.7550 0.7558
0.5243 4.99 100 0.6717 0.7573 0.8077 0.7573 0.7584
0.4409 5.99 120 0.5315 0.8068 0.8027 0.8068 0.7989
0.3325 6.99 140 0.5159 0.8230 0.8236 0.8230 0.8158
0.2719 7.99 160 0.5250 0.8215 0.8227 0.8215 0.8202
0.242 8.99 180 0.5087 0.8277 0.8260 0.8277 0.8268
0.2247 9.99 200 0.5313 0.8215 0.8275 0.8215 0.8218
0.1955 10.99 220 0.6167 0.8130 0.8062 0.8130 0.8073
0.1567 11.99 240 0.5859 0.8168 0.8185 0.8168 0.8173
0.1479 12.99 260 0.5938 0.8215 0.8169 0.8215 0.8178
0.1241 13.99 280 0.6187 0.8261 0.8234 0.8261 0.8239
0.1114 14.99 300 0.6419 0.8261 0.8351 0.8261 0.8293
0.1022 15.99 320 0.6322 0.8323 0.8284 0.8323 0.8294
0.0941 16.99 340 0.6595 0.8269 0.8266 0.8269 0.8263
0.0935 17.99 360 0.6674 0.8269 0.8218 0.8269 0.8237
0.089 18.99 380 0.6533 0.8253 0.8222 0.8253 0.8235
0.0794 19.99 400 0.6547 0.8308 0.8272 0.8308 0.8287
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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11
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