---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnext-large-224-22k-1k-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: 0.866306027820711
    - name: Precision
      type: precision
      value: 0.8617341777601428
    - name: Recall
      type: recall
      value: 0.866306027820711
    - name: F1
      type: f1
      value: 0.8629450778711495
---

<!-- 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. -->

# convnext-large-224-22k-1k-FV2-finetuned-memes

This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4290
- Accuracy: 0.8663
- Precision: 0.8617
- Recall: 0.8663
- F1: 0.8629

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8992        | 0.99  | 20   | 0.6455          | 0.7658   | 0.7512    | 0.7658 | 0.7534 |
| 0.4245        | 1.99  | 40   | 0.4008          | 0.8539   | 0.8680    | 0.8539 | 0.8541 |
| 0.2054        | 2.99  | 60   | 0.3245          | 0.8694   | 0.8631    | 0.8694 | 0.8650 |
| 0.1102        | 3.99  | 80   | 0.3231          | 0.8671   | 0.8624    | 0.8671 | 0.8645 |
| 0.0765        | 4.99  | 100  | 0.3882          | 0.8563   | 0.8603    | 0.8563 | 0.8556 |
| 0.0642        | 5.99  | 120  | 0.4133          | 0.8601   | 0.8604    | 0.8601 | 0.8598 |
| 0.0574        | 6.99  | 140  | 0.3889          | 0.8694   | 0.8657    | 0.8694 | 0.8667 |
| 0.0526        | 7.99  | 160  | 0.4145          | 0.8655   | 0.8705    | 0.8655 | 0.8670 |
| 0.0468        | 8.99  | 180  | 0.4256          | 0.8679   | 0.8642    | 0.8679 | 0.8650 |
| 0.0472        | 9.99  | 200  | 0.4290          | 0.8663   | 0.8617    | 0.8663 | 0.8629 |


### Framework versions

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