metadata
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: 0.875
emotion-classification
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.8208
- Accuracy: 0.875
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: 3e-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: 3
Training results
KeyboardInterrupt: because full disk, continue from checkpoint 270 / from epoch 54 checkpoint = "C:\Users\willi\Documents\MachineLearning_Collab\model\emotion-classification\checkpoint-270"
| Training Loss | Epoch | Validation Loss | Accuracy |
|---|---|---|---|
| 0.8456 | 55 | 0.8537 | 0.8562 |
| 0.7982 | 56 | 0.8021 | 0.8875 |
| 0.8028 | 57 | 0.8028 | 0.8438 |
The result of evaluation from trainer.evaluate(eval_dataset=emotion["test"]):
{'eval_loss': 0.8208037614822388, 'eval_accuracy': 0.875, 'eval_runtime': 5.3137, 'eval_samples_per_second': 30.111, 'eval_steps_per_second': 0.941, 'epoch': 3.0}
Framework versions
- Transformers 4.41.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1