metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
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: 0.56875
emotion_classification
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: 1.2074
- Accuracy: 0.5687
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 80 | 1.7180 | 0.3812 |
| No log | 2.0 | 160 | 1.5309 | 0.3625 |
| No log | 3.0 | 240 | 1.4981 | 0.4188 |
| No log | 4.0 | 320 | 1.4135 | 0.4313 |
| No log | 5.0 | 400 | 1.3722 | 0.4562 |
| No log | 6.0 | 480 | 1.3234 | 0.5188 |
| 1.3335 | 7.0 | 560 | 1.2675 | 0.525 |
| 1.3335 | 8.0 | 640 | 1.3068 | 0.5125 |
| 1.3335 | 9.0 | 720 | 1.2965 | 0.5437 |
| 1.3335 | 10.0 | 800 | 1.3408 | 0.5125 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1