mvkvc's picture
update model card README.md
7d7519a
metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: convnextv2-base-22k-224-finetuned-critique-100k
    results: []

convnextv2-base-22k-224-finetuned-critique-100k

This model is a fine-tuned version of facebook/convnextv2-base-22k-224 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1309
  • Accuracy: 0.9479

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: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6277 0.07 50 0.5987 0.6767
0.5459 0.14 100 0.5187 0.7401
0.4397 0.21 150 0.4448 0.7768
0.4197 0.28 200 0.3686 0.8401
0.3397 0.36 250 0.3153 0.8664
0.3345 0.43 300 0.3071 0.8701
0.3177 0.5 350 0.2576 0.8938
0.3182 0.57 400 0.2546 0.8926
0.2596 0.64 450 0.2320 0.9004
0.2563 0.71 500 0.2205 0.9082
0.2543 0.78 550 0.2142 0.9147
0.2768 0.85 600 0.2136 0.9132
0.2486 0.92 650 0.2052 0.9175
0.2504 1.0 700 0.2314 0.9058
0.2437 1.07 750 0.1943 0.9235
0.212 1.14 800 0.2019 0.9183
0.1891 1.21 850 0.1845 0.9254
0.2105 1.28 900 0.1834 0.9288
0.2285 1.35 950 0.1994 0.9206
0.2214 1.42 1000 0.1804 0.9251
0.1848 1.49 1050 0.1975 0.9196
0.191 1.56 1100 0.1795 0.9269
0.1794 1.64 1150 0.1606 0.9358
0.2084 1.71 1200 0.1807 0.9293
0.199 1.78 1250 0.1697 0.9307
0.1874 1.85 1300 0.1650 0.9372
0.1681 1.92 1350 0.1515 0.939
0.1696 1.99 1400 0.1473 0.9416
0.1651 2.06 1450 0.1489 0.9428
0.1627 2.13 1500 0.1529 0.9395
0.1754 2.2 1550 0.1540 0.9379
0.1302 2.28 1600 0.1579 0.939
0.1643 2.35 1650 0.1518 0.9401
0.1938 2.42 1700 0.1479 0.941
0.1441 2.49 1750 0.1451 0.9436
0.1478 2.56 1800 0.1324 0.9472
0.1275 2.63 1850 0.1340 0.9466
0.1582 2.7 1900 0.1501 0.9391
0.1472 2.77 1950 0.1354 0.9451
0.1522 2.84 2000 0.1309 0.9479
0.1593 2.92 2050 0.1433 0.9452
0.1541 2.99 2100 0.1381 0.9466
0.1297 3.06 2150 0.1320 0.9479

Framework versions

  • Transformers 4.30.2
  • Pytorch 1.13.1
  • Datasets 2.13.1
  • Tokenizers 0.13.3