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: 1.2110
Accuracy: 0.55

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: 64
eval_batch_size: 64
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
2.0717	1.0	10	2.0593	0.2062
2.005	2.0	20	1.9999	0.2625
1.9169	3.0	30	1.8931	0.35
1.7635	4.0	40	1.7616	0.4062
1.6614	5.0	50	1.6452	0.4562
1.6182	6.0	60	1.5661	0.4125
1.5434	7.0	70	1.5183	0.4125
1.46	8.0	80	1.4781	0.4875
1.4564	9.0	90	1.3939	0.5125
1.2966	10.0	100	1.3800	0.4562
1.3732	11.0	110	1.3557	0.475
1.2907	12.0	120	1.3473	0.5
1.2875	13.0	130	1.3416	0.5312
1.2743	14.0	140	1.2964	0.4875
1.1249	15.0	150	1.2385	0.525
1.0963	16.0	160	1.2775	0.5062
1.0261	17.0	170	1.2751	0.5125
0.9298	18.0	180	1.2318	0.525
1.0668	19.0	190	1.2520	0.5437
0.9933	20.0	200	1.2512	0.525
1.1069	21.0	210	1.3016	0.5
1.0279	22.0	220	1.3279	0.475
0.967	23.0	230	1.2481	0.5
0.8115	24.0	240	1.1791	0.5563
0.7912	25.0	250	1.2336	0.55
0.9294	26.0	260	1.1759	0.5813
0.8936	27.0	270	1.1685	0.6
0.7706	28.0	280	1.2403	0.5312
0.7694	29.0	290	1.2479	0.5687
0.7265	30.0	300	1.2000	0.5625
0.6781	31.0	310	1.1856	0.55
0.6676	32.0	320	1.2661	0.5437
0.7254	33.0	330	1.1986	0.5437
0.7396	34.0	340	1.1497	0.575
0.5532	35.0	350	1.2796	0.5062
0.622	36.0	360	1.2749	0.5125
0.6958	37.0	370	1.2034	0.5687
0.6102	38.0	380	1.2576	0.5188
0.6161	39.0	390	1.2635	0.5062
0.6927	40.0	400	1.1535	0.5437
0.549	41.0	410	1.1405	0.6
0.6668	42.0	420	1.2683	0.5312
0.5144	43.0	430	1.2249	0.6
0.6703	44.0	440	1.2297	0.5687
0.6383	45.0	450	1.1507	0.6062
0.5211	46.0	460	1.2914	0.4813
0.4743	47.0	470	1.2782	0.5125
0.553	48.0	480	1.2256	0.5375
0.6407	49.0	490	1.2149	0.5687
0.4195	50.0	500	1.2024	0.5625

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

RickyIG/emotion_face_image_classification

emotion_face_image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RickyIG/
emotion_face_image_classification