Hugging Face's logo

wcosmas
/
vit-base-patch16-224-in21k-finetuned-papsmear

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
vit-base-patch16-224-in21k-finetuned-papsmear / README.md
wcosmas's picture
wcosmas
Update README.md
7ae9612 verified
preview code
|
raw
history blame contribute delete
4.62 kB
metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-papsmear
    results: []

vit-base-patch16-224-in21k-finetuned-biopsy

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1092
  • Accuracy: 0.9732

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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.1553 1.0 42 1.0950 0.5477
0.7791 2.0 84 0.6486 0.8526
0.433 3.0 126 0.3716 0.9129
0.3495 4.0 168 0.2869 0.9347
0.2556 5.0 210 0.2722 0.9280
0.2791 6.0 252 0.2611 0.9330
0.2343 7.0 294 0.2377 0.9380
0.186 8.0 336 0.2158 0.9397
0.1984 9.0 378 0.2222 0.9347
0.1751 10.0 420 0.1993 0.9514
0.1529 11.0 462 0.2101 0.9430
0.1616 12.0 504 0.2543 0.9296
0.1404 13.0 546 0.2029 0.9397
0.1078 14.0 588 0.2087 0.9414
0.1109 15.0 630 0.1381 0.9615
0.1072 16.0 672 0.1895 0.9414
0.0949 17.0 714 0.1981 0.9397
0.0908 18.0 756 0.1608 0.9581
0.0809 19.0 798 0.1764 0.9581
0.0708 20.0 840 0.1512 0.9531
0.0757 21.0 882 0.2027 0.9481
0.0919 22.0 924 0.1487 0.9615
0.07 23.0 966 0.1667 0.9615
0.0629 24.0 1008 0.1904 0.9531
0.0584 25.0 1050 0.1521 0.9631
0.0666 26.0 1092 0.1326 0.9665
0.062 27.0 1134 0.1772 0.9564
0.0568 28.0 1176 0.1465 0.9564
0.0453 29.0 1218 0.1347 0.9682
0.0469 30.0 1260 0.1687 0.9631
0.0541 31.0 1302 0.1390 0.9715
0.0602 32.0 1344 0.1618 0.9615
0.0497 33.0 1386 0.1415 0.9615
0.0493 34.0 1428 0.1521 0.9631
0.0606 35.0 1470 0.1429 0.9698
0.0332 36.0 1512 0.1671 0.9648
0.0432 37.0 1554 0.1441 0.9665
0.0354 38.0 1596 0.1593 0.9682
0.0432 39.0 1638 0.1395 0.9665
0.0363 40.0 1680 0.1092 0.9732
0.0288 41.0 1722 0.1550 0.9665
0.0305 42.0 1764 0.1462 0.9682
0.0326 43.0 1806 0.1343 0.9682
0.027 44.0 1848 0.1109 0.9732
0.0233 45.0 1890 0.1315 0.9732
0.042 46.0 1932 0.1261 0.9732
0.0251 47.0 1974 0.1320 0.9732
0.041 48.0 2016 0.1282 0.9732
0.0445 49.0 2058 0.1296 0.9732
0.0308 50.0 2100 0.1325 0.9732

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.19.1