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: 0.55
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: 1.3694
- Accuracy: 0.55
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 1.9385 | 0.35 |
| No log | 2.0 | 80 | 1.6433 | 0.3875 |
| No log | 3.0 | 120 | 1.4689 | 0.5375 |
| No log | 4.0 | 160 | 1.3533 | 0.55 |
| No log | 5.0 | 200 | 1.3162 | 0.5813 |
| No log | 6.0 | 240 | 1.3131 | 0.5437 |
| No log | 7.0 | 280 | 1.2160 | 0.6 |
| No log | 8.0 | 320 | 1.2660 | 0.5437 |
| No log | 9.0 | 360 | 1.2594 | 0.55 |
| No log | 10.0 | 400 | 1.1873 | 0.5687 |
| No log | 11.0 | 440 | 1.1169 | 0.5875 |
| No log | 12.0 | 480 | 1.2015 | 0.5687 |
| 1.125 | 13.0 | 520 | 1.2653 | 0.5375 |
| 1.125 | 14.0 | 560 | 1.2801 | 0.5563 |
| 1.125 | 15.0 | 600 | 1.2304 | 0.5563 |
| 1.125 | 16.0 | 640 | 1.2341 | 0.5437 |
| 1.125 | 17.0 | 680 | 1.2981 | 0.5312 |
| 1.125 | 18.0 | 720 | 1.3277 | 0.5687 |
| 1.125 | 19.0 | 760 | 1.2174 | 0.5875 |
| 1.125 | 20.0 | 800 | 1.1810 | 0.6 |
| 1.125 | 21.0 | 840 | 1.2280 | 0.5687 |
| 1.125 | 22.0 | 880 | 1.3576 | 0.525 |
| 1.125 | 23.0 | 920 | 1.3897 | 0.5375 |
| 1.125 | 24.0 | 960 | 1.3216 | 0.5625 |
| 0.3612 | 25.0 | 1000 | 1.3033 | 0.6062 |
| 0.3612 | 26.0 | 1040 | 1.3501 | 0.5625 |
| 0.3612 | 27.0 | 1080 | 1.2310 | 0.575 |
| 0.3612 | 28.0 | 1120 | 1.2495 | 0.6062 |
| 0.3612 | 29.0 | 1160 | 1.2974 | 0.5875 |
| 0.3612 | 30.0 | 1200 | 1.2985 | 0.5813 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1