metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- FastJobs/Visual_Emotional_Analysis
metrics:
- accuracy
model-index:
- name: rgai_emotion_recognition
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: 0.58125
rgai_emotion_recognition
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the FastJobs/Visual_Emotional_Analysis dataset. It achieves the following results on the evaluation set:
- Loss: 1.3077
- Accuracy: 0.5813
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0698 | 1.0 | 25 | 2.0921 | 0.1125 |
| 1.973 | 2.0 | 50 | 1.9930 | 0.1938 |
| 1.8091 | 3.0 | 75 | 1.8374 | 0.3937 |
| 1.5732 | 4.0 | 100 | 1.6804 | 0.475 |
| 1.4087 | 5.0 | 125 | 1.5660 | 0.5125 |
| 1.2653 | 6.0 | 150 | 1.4769 | 0.5375 |
| 1.1443 | 7.0 | 175 | 1.4084 | 0.55 |
| 0.9888 | 8.0 | 200 | 1.3633 | 0.5625 |
| 0.9029 | 9.0 | 225 | 1.3305 | 0.55 |
| 0.8372 | 10.0 | 250 | 1.3077 | 0.5813 |
| 0.7569 | 11.0 | 275 | 1.2983 | 0.5625 |
| 0.6886 | 12.0 | 300 | 1.2806 | 0.5687 |
| 0.6216 | 13.0 | 325 | 1.2718 | 0.5687 |
| 0.6385 | 14.0 | 350 | 1.2700 | 0.5563 |
| 0.6029 | 15.0 | 375 | 1.2693 | 0.5625 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3