---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned
  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: 0.199
---

<!-- 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

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: 2.2724
- Accuracy: 0.199

## 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
- 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3021        | 0.14  | 10   | 2.2994          | 0.112    |
| 2.2929        | 0.28  | 20   | 2.2911          | 0.137    |
| 2.2875        | 0.43  | 30   | 2.2848          | 0.151    |
| 2.2824        | 0.57  | 40   | 2.2812          | 0.175    |
| 2.2792        | 0.71  | 50   | 2.2758          | 0.191    |
| 2.2766        | 0.85  | 60   | 2.2726          | 0.197    |
| 2.2765        | 0.99  | 70   | 2.2724          | 0.199    |


### Framework versions

- Transformers 4.31.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.6
- Tokenizers 0.13.3