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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: google-vit-base-patch16-224-cartoon-emotion-detection
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: 0.8807339449541285
- name: Precision
type: precision
value: 0.8768597487153273
- name: Recall
type: recall
value: 0.8807339449541285
- name: F1
type: f1
value: 0.8782945902988435
---
<!-- 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. -->
# google-vit-base-patch16-224-cartoon-emotion-detection
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3706
- Accuracy: 0.8807
- Precision: 0.8769
- Recall: 0.8807
- F1: 0.8783
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.97 | 8 | 0.9902 | 0.5596 | 0.5506 | 0.5596 | 0.5360 |
| 1.242 | 1.97 | 16 | 0.5157 | 0.8165 | 0.8195 | 0.8165 | 0.8132 |
| 0.4438 | 2.97 | 24 | 0.3871 | 0.8440 | 0.8516 | 0.8440 | 0.8446 |
| 0.1768 | 3.97 | 32 | 0.3531 | 0.8624 | 0.8653 | 0.8624 | 0.8585 |
| 0.0661 | 4.97 | 40 | 0.3780 | 0.8716 | 0.8693 | 0.8716 | 0.8674 |
| 0.0661 | 5.97 | 48 | 0.3747 | 0.8624 | 0.8649 | 0.8624 | 0.8632 |
| 0.0375 | 6.97 | 56 | 0.3760 | 0.8991 | 0.8961 | 0.8991 | 0.8971 |
| 0.0362 | 7.97 | 64 | 0.4092 | 0.8716 | 0.8684 | 0.8716 | 0.8681 |
| 0.0322 | 8.97 | 72 | 0.3499 | 0.8899 | 0.8880 | 0.8899 | 0.8888 |
| 0.029 | 9.97 | 80 | 0.3706 | 0.8807 | 0.8769 | 0.8807 | 0.8783 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.11.0
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