---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: face_discriminator-2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: faces_resnet
      split: train
      args: faces_resnet
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9416243654822335
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# face_discriminator-2

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3598
- Accuracy: 0.9416

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5002        | 0.99  | 110  | 0.4721          | 0.8553   |
| 0.3774        | 1.99  | 220  | 0.3598          | 0.9416   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.11.0