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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cvt-13-384-22k-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: 0.8315301391035549
- name: Precision
type: precision
value: 0.8302128280229624
- name: Recall
type: recall
value: 0.8315301391035549
- name: F1
type: f1
value: 0.8292026505769348
---
<!-- 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. -->
# cvt-13-384-22k-fv-finetuned-memes
This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
- Accuracy: 0.8315
- Precision: 0.8302
- Recall: 0.8315
- F1: 0.8292
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3821 | 0.99 | 20 | 1.2780 | 0.4969 | 0.5083 | 0.4969 | 0.4458 |
| 1.0785 | 1.99 | 40 | 0.8633 | 0.6669 | 0.6658 | 0.6669 | 0.6500 |
| 0.8862 | 2.99 | 60 | 0.7110 | 0.7218 | 0.7258 | 0.7218 | 0.7013 |
| 0.665 | 3.99 | 80 | 0.5515 | 0.8045 | 0.8137 | 0.8045 | 0.8050 |
| 0.6056 | 4.99 | 100 | 0.5956 | 0.7960 | 0.8041 | 0.7960 | 0.7846 |
| 0.4779 | 5.99 | 120 | 0.6229 | 0.7937 | 0.7945 | 0.7937 | 0.7857 |
| 0.4554 | 6.99 | 140 | 0.5355 | 0.8099 | 0.8126 | 0.8099 | 0.8086 |
| 0.4249 | 7.99 | 160 | 0.5447 | 0.8269 | 0.8275 | 0.8269 | 0.8236 |
| 0.4313 | 8.99 | 180 | 0.5530 | 0.8153 | 0.8140 | 0.8153 | 0.8132 |
| 0.423 | 9.99 | 200 | 0.5346 | 0.8238 | 0.8230 | 0.8238 | 0.8223 |
| 0.3997 | 10.99 | 220 | 0.5413 | 0.8338 | 0.8347 | 0.8338 | 0.8338 |
| 0.4095 | 11.99 | 240 | 0.5999 | 0.8207 | 0.8231 | 0.8207 | 0.8177 |
| 0.3979 | 12.99 | 260 | 0.5632 | 0.8284 | 0.8255 | 0.8284 | 0.8250 |
| 0.3408 | 13.99 | 280 | 0.5725 | 0.8207 | 0.8198 | 0.8207 | 0.8196 |
| 0.3828 | 14.99 | 300 | 0.5631 | 0.8277 | 0.8258 | 0.8277 | 0.8260 |
| 0.3595 | 15.99 | 320 | 0.6005 | 0.8308 | 0.8297 | 0.8308 | 0.8275 |
| 0.3789 | 16.99 | 340 | 0.5840 | 0.8300 | 0.8271 | 0.8300 | 0.8273 |
| 0.3545 | 17.99 | 360 | 0.5983 | 0.8246 | 0.8226 | 0.8246 | 0.8222 |
| 0.3472 | 18.99 | 380 | 0.5795 | 0.8416 | 0.8382 | 0.8416 | 0.8390 |
| 0.355 | 19.99 | 400 | 0.5761 | 0.8315 | 0.8302 | 0.8315 | 0.8292 |
### 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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