---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-student_two_classes
  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: 0.85
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet-50-finetuned-student_two_classes

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: 0.4531
- Accuracy: 0.85

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5955        | 1.0   | 13   | 0.4665          | 0.85     |
| 0.5303        | 2.0   | 26   | 0.4790          | 0.85     |
| 0.6127        | 3.0   | 39   | 0.4787          | 0.85     |
| 0.5025        | 4.0   | 52   | 0.4547          | 0.85     |
| 0.471         | 5.0   | 65   | 0.4621          | 0.85     |
| 0.4673        | 6.0   | 78   | 0.4775          | 0.86     |
| 0.4492        | 7.0   | 91   | 0.4648          | 0.86     |
| 0.4144        | 8.0   | 104  | 0.4733          | 0.85     |
| 0.4963        | 9.0   | 117  | 0.4575          | 0.85     |
| 0.4149        | 10.0  | 130  | 0.4691          | 0.85     |
| 0.4588        | 11.0  | 143  | 0.4596          | 0.84     |
| 0.3995        | 12.0  | 156  | 0.4754          | 0.85     |
| 0.359         | 13.0  | 169  | 0.4616          | 0.85     |
| 0.4246        | 14.0  | 182  | 0.4552          | 0.85     |
| 0.4001        | 15.0  | 195  | 0.4839          | 0.85     |
| 0.3919        | 16.0  | 208  | 0.4708          | 0.85     |
| 0.4137        | 17.0  | 221  | 0.4416          | 0.85     |
| 0.3912        | 18.0  | 234  | 0.4507          | 0.85     |
| 0.4322        | 19.0  | 247  | 0.4237          | 0.85     |
| 0.4043        | 20.0  | 260  | 0.4531          | 0.85     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1