README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-emotions-fp16
    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: 0.9859375

vit-emotions-fp16

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: 0.0725
• Accuracy: 0.9859

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: 5e-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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	40	1.3965	0.4938
No log	2.0	80	1.4154	0.425
No log	3.0	120	1.3729	0.4562
No log	4.0	160	1.3532	0.4562
No log	5.0	200	1.2993	0.5062
No log	6.0	240	1.3438	0.4938
No log	7.0	280	1.3741	0.5
No log	8.0	320	1.5267	0.4313
No log	9.0	360	1.2778	0.5375
No log	10.0	400	1.3864	0.5062
No log	11.0	440	1.4221	0.4875
No log	12.0	480	1.5059	0.5062
0.7596	13.0	520	1.5004	0.5188
0.7596	14.0	560	1.4539	0.5125
0.7596	15.0	600	1.5219	0.5375
0.7596	16.0	640	1.6179	0.4813
0.7596	17.0	680	1.4562	0.55
0.7596	18.0	720	1.5473	0.4875
0.7596	19.0	760	1.5820	0.5188
0.7596	20.0	800	1.5877	0.5125
0.7596	21.0	840	1.4965	0.55
0.7596	22.0	880	1.5947	0.5375
0.7596	23.0	920	1.4672	0.5437
0.7596	24.0	960	1.7930	0.5
0.2328	25.0	1000	1.8033	0.4875
0.2328	26.0	1040	1.7193	0.5312
0.2328	27.0	1080	1.8072	0.4813
0.2328	28.0	1120	1.6767	0.5437
0.2328	29.0	1160	1.6138	0.5625
0.2328	30.0	1200	1.8484	0.4938
0.2328	31.0	1240	1.7691	0.5062
0.2328	32.0	1280	1.7797	0.5062
0.2328	33.0	1320	1.7575	0.5375
0.2328	34.0	1360	1.7550	0.5062
0.2328	35.0	1400	1.7933	0.5
0.2328	36.0	1440	1.7056	0.5563
0.2328	37.0	1480	1.8739	0.4938
0.1517	38.0	1520	1.7637	0.5188
0.1517	39.0	1560	1.7178	0.5563
0.1517	40.0	1600	1.9114	0.5
0.1517	41.0	1640	1.8453	0.5188
0.1517	42.0	1680	1.7571	0.5625
0.1517	43.0	1720	1.7757	0.5437
0.1517	44.0	1760	1.8389	0.5125
0.1517	45.0	1800	1.8109	0.5375
0.1517	46.0	1840	1.8537	0.4688
0.1517	47.0	1880	1.7422	0.5563
0.1517	48.0	1920	1.7807	0.5687
0.1517	49.0	1960	1.8111	0.525
0.1045	50.0	2000	1.9057	0.5125

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.2