---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet152-FV-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: 0.7557959814528593
    - name: Precision
      type: precision
      value: 0.7556690736625777
    - name: Recall
      type: recall
      value: 0.7557959814528593
    - name: F1
      type: f1
      value: 0.7545674798253312
---

<!-- 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. -->

# resnet152-FV-finetuned-memes

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6772
- Accuracy: 0.7558
- Precision: 0.7557
- Recall: 0.7558
- F1: 0.7546

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5739        | 0.99  | 20   | 1.5427          | 0.4521   | 0.3131    | 0.4521 | 0.2880 |
| 1.4353        | 1.99  | 40   | 1.3786          | 0.4490   | 0.3850    | 0.4490 | 0.2791 |
| 1.3026        | 2.99  | 60   | 1.2734          | 0.4799   | 0.3073    | 0.4799 | 0.3393 |
| 1.1579        | 3.99  | 80   | 1.1378          | 0.5278   | 0.4300    | 0.5278 | 0.4143 |
| 1.0276        | 4.99  | 100  | 1.0231          | 0.5734   | 0.4497    | 0.5734 | 0.4865 |
| 0.8826        | 5.99  | 120  | 0.9228          | 0.6252   | 0.5983    | 0.6252 | 0.5637 |
| 0.766         | 6.99  | 140  | 0.8441          | 0.6662   | 0.6474    | 0.6662 | 0.6320 |
| 0.6732        | 7.99  | 160  | 0.8009          | 0.6901   | 0.6759    | 0.6901 | 0.6704 |
| 0.5653        | 8.99  | 180  | 0.7535          | 0.7218   | 0.7141    | 0.7218 | 0.7129 |
| 0.4957        | 9.99  | 200  | 0.7317          | 0.7257   | 0.7248    | 0.7257 | 0.7200 |
| 0.4534        | 10.99 | 220  | 0.6808          | 0.7434   | 0.7405    | 0.7434 | 0.7390 |
| 0.3792        | 11.99 | 240  | 0.6949          | 0.7450   | 0.7454    | 0.7450 | 0.7399 |
| 0.3489        | 12.99 | 260  | 0.6746          | 0.7496   | 0.7511    | 0.7496 | 0.7474 |
| 0.3113        | 13.99 | 280  | 0.6637          | 0.7573   | 0.7638    | 0.7573 | 0.7579 |
| 0.2947        | 14.99 | 300  | 0.6451          | 0.7589   | 0.7667    | 0.7589 | 0.7610 |
| 0.2776        | 15.99 | 320  | 0.6754          | 0.7543   | 0.7565    | 0.7543 | 0.7525 |
| 0.2611        | 16.99 | 340  | 0.6808          | 0.7550   | 0.7607    | 0.7550 | 0.7529 |
| 0.2428        | 17.99 | 360  | 0.7005          | 0.7457   | 0.7497    | 0.7457 | 0.7404 |
| 0.2346        | 18.99 | 380  | 0.6597          | 0.7573   | 0.7642    | 0.7573 | 0.7590 |
| 0.2367        | 19.99 | 400  | 0.6772          | 0.7558   | 0.7557    | 0.7558 | 0.7546 |


### Framework versions

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