transformers tensorboard safetensors swinv2 image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swinv2-tiny-patch4-window8-256 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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
---

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

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/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
