|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: cvt-13-384-in22k-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: 0.8346213292117465 |
|
- name: Precision |
|
type: precision |
|
value: 0.8326806465391725 |
|
- name: Recall |
|
type: recall |
|
value: 0.8346213292117465 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8322067261008879 |
|
--- |
|
|
|
<!-- 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-in22k-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: 0.5595 |
|
- Accuracy: 0.8346 |
|
- Precision: 0.8327 |
|
- Recall: 0.8346 |
|
- F1: 0.8322 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 1.4066 | 0.99 | 20 | 1.2430 | 0.5124 | 0.5141 | 0.5124 | 0.4371 | |
|
| 1.0813 | 1.99 | 40 | 0.8244 | 0.6893 | 0.6834 | 0.6893 | 0.6616 | |
|
| 0.8392 | 2.99 | 60 | 0.6334 | 0.7612 | 0.7670 | 0.7612 | 0.7570 | |
|
| 0.7065 | 3.99 | 80 | 0.5819 | 0.7767 | 0.7799 | 0.7767 | 0.7672 | |
|
| 0.5751 | 4.99 | 100 | 0.5365 | 0.8176 | 0.8216 | 0.8176 | 0.8130 | |
|
| 0.4896 | 5.99 | 120 | 0.4943 | 0.8308 | 0.8257 | 0.8308 | 0.8265 | |
|
| 0.4487 | 6.99 | 140 | 0.5399 | 0.8107 | 0.8069 | 0.8107 | 0.8054 | |
|
| 0.4349 | 7.99 | 160 | 0.4892 | 0.8300 | 0.8285 | 0.8300 | 0.8273 | |
|
| 0.43 | 8.99 | 180 | 0.4984 | 0.8454 | 0.8465 | 0.8454 | 0.8426 | |
|
| 0.4372 | 9.99 | 200 | 0.5573 | 0.8192 | 0.8221 | 0.8192 | 0.8157 | |
|
| 0.3994 | 10.99 | 220 | 0.5158 | 0.8300 | 0.8284 | 0.8300 | 0.8281 | |
|
| 0.3883 | 11.99 | 240 | 0.5495 | 0.8354 | 0.8317 | 0.8354 | 0.8314 | |
|
| 0.406 | 12.99 | 260 | 0.5298 | 0.8284 | 0.8285 | 0.8284 | 0.8246 | |
|
| 0.3355 | 13.99 | 280 | 0.5401 | 0.8393 | 0.8346 | 0.8393 | 0.8357 | |
|
| 0.395 | 14.99 | 300 | 0.5915 | 0.8308 | 0.8278 | 0.8308 | 0.8261 | |
|
| 0.3612 | 15.99 | 320 | 0.5852 | 0.8408 | 0.8378 | 0.8408 | 0.8368 | |
|
| 0.3765 | 16.99 | 340 | 0.5509 | 0.8385 | 0.8351 | 0.8385 | 0.8356 | |
|
| 0.3688 | 17.99 | 360 | 0.5668 | 0.8416 | 0.8398 | 0.8416 | 0.8387 | |
|
| 0.3503 | 18.99 | 380 | 0.5626 | 0.8393 | 0.8371 | 0.8393 | 0.8365 | |
|
| 0.3611 | 19.99 | 400 | 0.5595 | 0.8346 | 0.8327 | 0.8346 | 0.8322 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0.dev0 |
|
- Pytorch 1.11.0+cu102 |
|
- Datasets 2.6.1.dev0 |
|
- Tokenizers 0.13.1 |
|
|
