hq_fer2013

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: 0.8438
• Accuracy: 0.7022
• Precision: 0.7039
• Recall: 0.7022
• F1: 0.7022

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 17
• 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: 13

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.3081	1.0	398	1.3132	0.5555	0.5079	0.5555	0.5137
0.991	2.0	796	1.0141	0.6332	0.6356	0.6332	0.6153
0.9099	3.0	1194	0.9257	0.6682	0.6677	0.6682	0.6631
0.8306	4.0	1592	0.8832	0.6765	0.6838	0.6765	0.6747
0.7755	5.0	1990	0.8583	0.6892	0.6896	0.6892	0.6876
0.7129	6.0	2388	0.8442	0.6931	0.6951	0.6931	0.6922
0.6549	7.0	2786	0.8494	0.6952	0.7054	0.6952	0.6978
0.6246	8.0	3184	0.8394	0.6963	0.7023	0.6963	0.6977
0.6138	9.0	3582	0.8421	0.6996	0.7080	0.6996	0.7013
0.5824	10.0	3980	0.8438	0.7022	0.7039	0.7022	0.7022
0.5517	11.0	4378	0.8497	0.7002	0.7034	0.7002	0.7005
0.5154	12.0	4776	0.8508	0.7021	0.7030	0.7021	0.7018
0.5318	13.0	5174	0.8540	0.7010	0.7029	0.7010	0.7013

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2