metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: visual_emotional_analysis
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: 0.55625
visual_emotional_analysis
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2815
- Accuracy: 0.5563
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 1.8308 | 0.375 |
| No log | 2.0 | 40 | 1.5510 | 0.4875 |
| No log | 3.0 | 60 | 1.4138 | 0.5062 |
| No log | 4.0 | 80 | 1.3845 | 0.4875 |
| No log | 5.0 | 100 | 1.3245 | 0.525 |
| No log | 6.0 | 120 | 1.2645 | 0.6 |
| No log | 7.0 | 140 | 1.2887 | 0.5188 |
| No log | 8.0 | 160 | 1.2395 | 0.5875 |
| No log | 9.0 | 180 | 1.2267 | 0.55 |
| No log | 10.0 | 200 | 1.1883 | 0.6 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3