vit-base-patch16-224-finetuned-on-all-affectnet_short

This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0520
• Accuracy: 0.7259
• Precision: 0.7293
• Recall: 0.7259
• F1: 0.7255

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: 64
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 14
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.5399	1.0	91	1.4698	0.5097	0.5079	0.5097	0.4827
1.284	2.0	182	1.2026	0.6409	0.6514	0.6409	0.6226
1.2259	3.0	273	1.1367	0.6722	0.6749	0.6722	0.6694
1.1663	4.0	364	1.1086	0.6838	0.6903	0.6838	0.6814
1.1401	5.0	455	1.0782	0.7055	0.7070	0.7055	0.7042
1.1229	6.0	546	1.0734	0.7055	0.7093	0.7055	0.7036
1.0929	7.0	637	1.0674	0.7120	0.7147	0.7120	0.7099
1.0826	8.0	728	1.0601	0.7210	0.7226	0.7210	0.7191
1.0414	9.0	819	1.0558	0.7203	0.7211	0.7203	0.7196
1.0649	10.0	910	1.0499	0.7179	0.7194	0.7179	0.7175
1.0554	11.0	1001	1.0520	0.7259	0.7293	0.7259	0.7255
1.0496	12.0	1092	1.0466	0.7210	0.7212	0.7210	0.7204
1.064	13.0	1183	1.0502	0.7220	0.7235	0.7220	0.7211
1.0386	14.0	1274	1.0475	0.7206	0.7208	0.7206	0.7200

Framework versions

• Transformers 4.29.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3