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-exp-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: 0.8389261744966443

swinv2-tiny-patch4-window8-256-finetuned-gardner-exp-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: 0.5312
Accuracy: 0.8389

Model description

Predict Expansion 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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.6068	0.97	14	1.5809	0.5415
1.56	2.0	29	1.2830	0.5415
1.1852	2.97	43	1.0794	0.5415
1.1132	4.0	58	0.9314	0.6488
0.9416	4.97	72	0.8935	0.6341
0.9143	6.0	87	0.8009	0.6829
0.8243	6.97	101	0.8067	0.6634
0.8171	8.0	116	0.7783	0.6780
0.7901	8.97	130	0.7871	0.6585
0.7944	10.0	145	0.7414	0.6976
0.7669	10.97	159	0.6977	0.7122
0.7478	12.0	174	0.7043	0.7122
0.766	12.97	188	0.7778	0.6585
0.7322	14.0	203	0.7504	0.6780
0.7242	14.97	217	0.7291	0.6829
0.7554	16.0	232	0.7694	0.6634
0.7422	16.97	246	0.7569	0.6829
0.7292	18.0	261	0.7389	0.6780
0.7354	18.97	275	0.6684	0.7122
0.6847	20.0	290	0.6821	0.7122
0.7231	20.97	304	0.6839	0.7024
0.6962	22.0	319	0.6958	0.6878
0.7079	22.97	333	0.7039	0.6878
0.7088	24.0	348	0.6974	0.6878
0.7106	24.14	350	0.6975	0.6878

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.16.0
Tokenizers 0.15.0