metadata

license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.594017094017094

swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8795
Accuracy: 0.5940

Model description

Predict Trophectoderm Grade - Gardner Score from an embryo image

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0943	0.94	11	1.0750	0.6325
0.9996	1.96	23	0.8011	0.6325
0.7731	2.98	35	0.7182	0.6325
0.7564	4.0	47	0.7109	0.6325
0.7331	4.94	58	0.7026	0.6325
0.7336	5.96	70	0.6848	0.6325
0.7305	6.98	82	0.6938	0.6325
0.7314	8.0	94	0.6549	0.6325
0.6905	8.94	105	0.6364	0.6867
0.7315	9.96	117	0.6223	0.6687
0.6839	10.98	129	0.6528	0.7530
0.6931	12.0	141	0.6209	0.7410
0.6705	12.94	152	0.6296	0.7169
0.7227	13.96	164	0.6039	0.7108
0.6695	14.98	176	0.6049	0.7530
0.6981	16.0	188	0.5965	0.7048
0.6566	16.94	199	0.6111	0.7410
0.6828	17.96	211	0.5969	0.7530
0.6632	18.72	220	0.5947	0.7530

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.16.0
Tokenizers 0.15.0