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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: deit-base-patch16-224-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: 0.848531684698609
- name: Precision
type: precision
value: 0.8458069264500935
- name: Recall
type: recall
value: 0.848531684698609
- name: F1
type: f1
value: 0.8463625265241504
---
<!-- 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. -->
# deit-base-patch16-224-FV-finetuned-memes
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6769
- Accuracy: 0.8485
- Precision: 0.8458
- Recall: 0.8485
- F1: 0.8464
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2733 | 0.99 | 20 | 1.0893 | 0.5811 | 0.5790 | 0.5811 | 0.5293 |
| 0.7284 | 1.99 | 40 | 0.7351 | 0.7210 | 0.7642 | 0.7210 | 0.7271 |
| 0.4267 | 2.99 | 60 | 0.5202 | 0.7991 | 0.8104 | 0.7991 | 0.8033 |
| 0.2181 | 3.99 | 80 | 0.4605 | 0.8346 | 0.8351 | 0.8346 | 0.8334 |
| 0.1504 | 4.99 | 100 | 0.5281 | 0.8253 | 0.8281 | 0.8253 | 0.8266 |
| 0.1001 | 5.99 | 120 | 0.4945 | 0.8369 | 0.8336 | 0.8369 | 0.8347 |
| 0.0874 | 6.99 | 140 | 0.5902 | 0.8338 | 0.8370 | 0.8338 | 0.8348 |
| 0.0634 | 7.99 | 160 | 0.6088 | 0.8253 | 0.8221 | 0.8253 | 0.8234 |
| 0.0699 | 8.99 | 180 | 0.6210 | 0.8207 | 0.8202 | 0.8207 | 0.8186 |
| 0.0661 | 9.99 | 200 | 0.5675 | 0.8385 | 0.8417 | 0.8385 | 0.8393 |
| 0.0592 | 10.99 | 220 | 0.6550 | 0.8253 | 0.8324 | 0.8253 | 0.8275 |
| 0.0559 | 11.99 | 240 | 0.6400 | 0.8416 | 0.8370 | 0.8416 | 0.8387 |
| 0.0501 | 12.99 | 260 | 0.6726 | 0.8393 | 0.8353 | 0.8393 | 0.8350 |
| 0.0529 | 13.99 | 280 | 0.6285 | 0.8408 | 0.8399 | 0.8408 | 0.8401 |
| 0.0478 | 14.99 | 300 | 0.6423 | 0.8400 | 0.8380 | 0.8400 | 0.8384 |
| 0.0458 | 15.99 | 320 | 0.6632 | 0.8369 | 0.8337 | 0.8369 | 0.8348 |
| 0.048 | 16.99 | 340 | 0.6719 | 0.8423 | 0.8401 | 0.8423 | 0.8404 |
| 0.0417 | 17.99 | 360 | 0.6807 | 0.8423 | 0.8415 | 0.8423 | 0.8408 |
| 0.0461 | 18.99 | 380 | 0.6732 | 0.8454 | 0.8440 | 0.8454 | 0.8438 |
| 0.044 | 19.99 | 400 | 0.6769 | 0.8485 | 0.8458 | 0.8485 | 0.8464 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
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