|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- imagefolder |
|
|
metrics: |
|
|
- accuracy |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
model-index: |
|
|
- name: hq_fer2013notest |
|
|
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: 0.7052268506235075 |
|
|
- name: Precision |
|
|
type: precision |
|
|
value: 0.7048074435355876 |
|
|
- name: Recall |
|
|
type: recall |
|
|
value: 0.7052268506235075 |
|
|
- name: F1 |
|
|
type: f1 |
|
|
value: 0.7036260157126459 |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# hq_fer2013notest |
|
|
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.8294 |
|
|
- Accuracy: 0.7052 |
|
|
- Precision: 0.7048 |
|
|
- Recall: 0.7052 |
|
|
- F1: 0.7036 |
|
|
|
|
|
## 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: 1e-05 |
|
|
- train_batch_size: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 17 |
|
|
- gradient_accumulation_steps: 4 |
|
|
- total_train_batch_size: 128 |
|
|
- 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 | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
|
| 1.2982 | 1.0 | 353 | 1.2708 | 0.5635 | 0.5107 | 0.5635 | 0.5168 | |
|
|
| 1.0218 | 2.0 | 706 | 1.0159 | 0.6411 | 0.6397 | 0.6411 | 0.6301 | |
|
|
| 0.9437 | 3.0 | 1059 | 0.9452 | 0.6631 | 0.6698 | 0.6631 | 0.6556 | |
|
|
| 0.8282 | 4.0 | 1412 | 0.8873 | 0.6829 | 0.6798 | 0.6829 | 0.6743 | |
|
|
| 0.7717 | 5.0 | 1765 | 0.8612 | 0.6884 | 0.6888 | 0.6884 | 0.6835 | |
|
|
| 0.7678 | 6.0 | 2118 | 0.8473 | 0.6985 | 0.6989 | 0.6985 | 0.6966 | |
|
|
| 0.7096 | 7.0 | 2471 | 0.8363 | 0.7018 | 0.7001 | 0.7018 | 0.6989 | |
|
|
| 0.6803 | 8.0 | 2824 | 0.8333 | 0.7036 | 0.7036 | 0.7036 | 0.7019 | |
|
|
| 0.6521 | 9.0 | 3177 | 0.8309 | 0.7050 | 0.7039 | 0.7050 | 0.7028 | |
|
|
| 0.6671 | 10.0 | 3530 | 0.8294 | 0.7052 | 0.7048 | 0.7052 | 0.7036 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.27.0.dev0 |
|
|
- Pytorch 1.13.1+cu116 |
|
|
- Datasets 2.9.0 |
|
|
- Tokenizers 0.13.2 |
|
|
|
