---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet50-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: 0.5741885625965997
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      type: custom
      name: custom
      split: test
    metrics:
    - type: f1
      value: 0.47811617701687364
      name: F1
    - type: precision
      value: 0.43689216537139497
      name: Precision
    - type: recall
      value: 0.5695517774343122
      name: Recall
---

<!-- 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. -->

# resnet50-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: 1.0625
- Accuracy: 0.5742

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4795        | 0.99  | 40   | 1.4641          | 0.4382   |
| 1.3455        | 1.99  | 80   | 1.3281          | 0.4389   |
| 1.262         | 2.99  | 120  | 1.2583          | 0.4583   |
| 1.1975        | 3.99  | 160  | 1.1978          | 0.4876   |
| 1.1358        | 4.99  | 200  | 1.1614          | 0.5139   |
| 1.1273        | 5.99  | 240  | 1.1316          | 0.5379   |
| 1.0379        | 6.99  | 280  | 1.1024          | 0.5464   |
| 1.041         | 7.99  | 320  | 1.0927          | 0.5580   |
| 0.9952        | 8.99  | 360  | 1.0790          | 0.5541   |
| 1.0146        | 9.99  | 400  | 1.0625          | 0.5742   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1