--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_classifier 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: 0.4125 --- # emotion_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6092 - Accuracy: 0.4125 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 2.0750 | 0.15 | | No log | 2.0 | 80 | 2.0046 | 0.1875 | | No log | 3.0 | 120 | 1.8909 | 0.3063 | | No log | 4.0 | 160 | 1.7726 | 0.3563 | | No log | 5.0 | 200 | 1.6970 | 0.3438 | | No log | 6.0 | 240 | 1.6562 | 0.3937 | | No log | 7.0 | 280 | 1.6269 | 0.4062 | | No log | 8.0 | 320 | 1.6092 | 0.4125 | | No log | 9.0 | 360 | 1.6012 | 0.4125 | | No log | 10.0 | 400 | 1.5955 | 0.4125 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cpu - Datasets 3.2.0 - Tokenizers 0.21.0