Hugging Face's logo

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

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
vit-base-patch16-224-in21k-finetuned-biopsy / README.md
wcosmas's picture
wcosmas
End of training
5fbf096 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-biopsy
    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: 0.0838
  • Accuracy: 0.9799

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
1.1862 1.0 42 1.1167 0.5611
0.7235 2.0 84 0.6029 0.8543
0.4286 3.0 126 0.3452 0.9280
0.3612 4.0 168 0.3485 0.8945
0.3015 5.0 210 0.2590 0.9296
0.2917 6.0 252 0.2219 0.9414
0.2312 7.0 294 0.2400 0.9280
0.1708 8.0 336 0.2120 0.9414
0.1806 9.0 378 0.1784 0.9514
0.1703 10.0 420 0.1571 0.9481
0.139 11.0 462 0.1544 0.9648
0.1301 12.0 504 0.1431 0.9598
0.122 13.0 546 0.1297 0.9631
0.1104 14.0 588 0.1401 0.9598
0.1075 15.0 630 0.1200 0.9665
0.0986 16.0 672 0.1665 0.9581
0.092 17.0 714 0.1399 0.9531
0.1123 18.0 756 0.1122 0.9698
0.0766 19.0 798 0.1337 0.9564
0.0762 20.0 840 0.0974 0.9732
0.0994 21.0 882 0.1023 0.9698
0.0687 22.0 924 0.0976 0.9749
0.0767 23.0 966 0.0952 0.9765
0.0581 24.0 1008 0.1096 0.9665
0.0544 25.0 1050 0.1123 0.9715
0.079 26.0 1092 0.1040 0.9682
0.0661 27.0 1134 0.0838 0.9799
0.068 28.0 1176 0.1169 0.9715
0.0722 29.0 1218 0.0897 0.9732
0.048 30.0 1260 0.0864 0.9732
0.0509 31.0 1302 0.0858 0.9749
0.047 32.0 1344 0.0801 0.9782
0.0411 33.0 1386 0.1221 0.9648
0.0378 34.0 1428 0.1011 0.9648
0.0358 35.0 1470 0.0834 0.9799
0.0347 36.0 1512 0.0993 0.9715
0.0434 37.0 1554 0.0938 0.9732
0.0507 38.0 1596 0.0874 0.9782
0.0466 39.0 1638 0.0932 0.9765
0.0502 40.0 1680 0.1012 0.9698
0.0289 41.0 1722 0.0841 0.9715
0.0274 42.0 1764 0.0883 0.9682
0.0251 43.0 1806 0.0843 0.9782
0.0343 44.0 1848 0.0812 0.9782
0.0289 45.0 1890 0.0805 0.9782
0.0277 46.0 1932 0.0943 0.9698
0.0332 47.0 1974 0.0807 0.9765
0.0328 48.0 2016 0.0826 0.9749
0.0257 49.0 2058 0.0852 0.9749
0.0287 50.0 2100 0.0848 0.9782

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.1