---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet-50-FV2-finetuned-memes
  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: 0.6452859350850078
    - name: Precision
      type: precision
      value: 0.5727919568038408
    - name: Recall
      type: recall
      value: 0.6452859350850078
    - name: F1
      type: f1
      value: 0.5963647629954705
---

<!-- 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-FV2-finetuned-memes

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: 0.9263
- Accuracy: 0.6453
- Precision: 0.5728
- Recall: 0.6453
- F1: 0.5964

## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5763        | 0.99  | 20   | 1.5575          | 0.4281   | 0.2966    | 0.4281 | 0.2669 |
| 1.4761        | 1.99  | 40   | 1.4424          | 0.4343   | 0.1886    | 0.4343 | 0.2630 |
| 1.3563        | 2.99  | 60   | 1.3240          | 0.4343   | 0.1886    | 0.4343 | 0.2630 |
| 1.2824        | 3.99  | 80   | 1.2636          | 0.4389   | 0.3097    | 0.4389 | 0.2734 |
| 1.2315        | 4.99  | 100  | 1.2119          | 0.4529   | 0.3236    | 0.4529 | 0.3042 |
| 1.1956        | 5.99  | 120  | 1.1764          | 0.4900   | 0.3731    | 0.4900 | 0.3692 |
| 1.1452        | 6.99  | 140  | 1.1424          | 0.5147   | 0.3963    | 0.5147 | 0.4090 |
| 1.1076        | 7.99  | 160  | 1.1190          | 0.5371   | 0.4121    | 0.5371 | 0.4392 |
| 1.0679        | 8.99  | 180  | 1.0825          | 0.5719   | 0.4465    | 0.5719 | 0.4831 |
| 1.0432        | 9.99  | 200  | 1.0482          | 0.5750   | 0.5404    | 0.5750 | 0.4930 |
| 0.9903        | 10.99 | 220  | 1.0275          | 0.5958   | 0.5459    | 0.5958 | 0.5241 |
| 0.9675        | 11.99 | 240  | 1.0145          | 0.6051   | 0.5350    | 0.6051 | 0.5379 |
| 0.9335        | 12.99 | 260  | 0.9860          | 0.6175   | 0.5537    | 0.6175 | 0.5527 |
| 0.9157        | 13.99 | 280  | 0.9683          | 0.6105   | 0.5386    | 0.6105 | 0.5504 |
| 0.8901        | 14.99 | 300  | 0.9558          | 0.6352   | 0.5686    | 0.6352 | 0.5833 |
| 0.8722        | 15.99 | 320  | 0.9382          | 0.6345   | 0.5657    | 0.6345 | 0.5807 |
| 0.854         | 16.99 | 340  | 0.9322          | 0.6376   | 0.5623    | 0.6376 | 0.5856 |
| 0.8494        | 17.99 | 360  | 0.9287          | 0.6422   | 0.6675    | 0.6422 | 0.5918 |
| 0.8652        | 18.99 | 380  | 0.9212          | 0.6399   | 0.5640    | 0.6399 | 0.5863 |
| 0.846         | 19.99 | 400  | 0.9263          | 0.6453   | 0.5728    | 0.6453 | 0.5964 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1