metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - f1
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: 0.63125
          - name: Precision
            type: precision
            value: 0.6430986797647803
          - name: F1
            type: f1
            value: 0.6224944698106615

emotion_classification

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: 1.1031
Accuracy: 0.6312
Precision: 0.6431
F1: 0.6225

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: 0.0002
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 20
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	F1
2.0742	1.0	10	2.0533	0.1938	0.1942	0.1858
2.0081	2.0	20	1.8908	0.3438	0.3701	0.3368
1.7211	3.0	30	1.5199	0.5312	0.4821	0.4844
1.5641	4.0	40	1.4248	0.4875	0.5314	0.4532
1.3979	5.0	50	1.2973	0.5375	0.5162	0.5023
1.2997	6.0	60	1.2016	0.525	0.4828	0.4826
1.2348	7.0	70	1.1670	0.5875	0.6375	0.5941
1.1481	8.0	80	1.1292	0.6	0.6111	0.5961
1.079	9.0	90	1.1782	0.5188	0.5265	0.5005
0.9909	10.0	100	1.1115	0.5813	0.5892	0.5668
0.9662	11.0	110	1.1047	0.5938	0.6336	0.5723
0.8149	12.0	120	1.0944	0.5563	0.5648	0.5499
0.7661	13.0	130	1.0932	0.5625	0.5738	0.5499
0.7067	14.0	140	1.0787	0.6062	0.6318	0.6045
0.6708	15.0	150	1.1140	0.6188	0.6463	0.6134
0.6268	16.0	160	1.0875	0.5813	0.6016	0.5815
0.5473	17.0	170	1.1483	0.5938	0.6027	0.5844
0.5228	18.0	180	1.1031	0.6312	0.6431	0.6225
0.4805	19.0	190	1.1747	0.5813	0.6057	0.5848
0.4995	20.0	200	1.1865	0.6062	0.6062	0.5980
0.456	21.0	210	1.2619	0.6	0.6020	0.5843
0.4697	22.0	220	1.2476	0.5625	0.5804	0.5647
0.3656	23.0	230	1.3106	0.6125	0.6645	0.6130
0.394	24.0	240	1.3398	0.5437	0.5627	0.5460
0.35	25.0	250	1.3391	0.5938	0.5940	0.5860
0.3508	26.0	260	1.2846	0.575	0.6070	0.5821
0.3106	27.0	270	1.3495	0.575	0.6258	0.5663
0.3265	28.0	280	1.4450	0.5375	0.6512	0.5248
0.2806	29.0	290	1.5145	0.5188	0.5840	0.5151
0.3276	30.0	300	1.5207	0.5188	0.5741	0.5164
0.2932	31.0	310	1.3179	0.6312	0.6421	0.6298
0.3542	32.0	320	1.3720	0.5875	0.6157	0.5780
0.3321	33.0	330	1.4787	0.5625	0.6088	0.5714
0.2641	34.0	340	1.5468	0.5375	0.5817	0.5385
0.2432	35.0	350	1.4893	0.5687	0.6012	0.5538
0.275	36.0	360	1.4775	0.575	0.5827	0.5710
0.239	37.0	370	1.4812	0.575	0.6100	0.5739
0.2658	38.0	380	1.7335	0.5563	0.6547	0.5436
0.3026	39.0	390	1.5692	0.5875	0.6401	0.5854
0.1867	40.0	400	1.4908	0.5687	0.5921	0.5741
0.1931	41.0	410	1.6608	0.5375	0.5834	0.5396
0.2416	42.0	420	1.5172	0.5938	0.6259	0.5935
0.1943	43.0	430	1.5260	0.5437	0.5775	0.5498

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3