metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - image_folder
metrics:
  - accuracy
  - precision
  - f1
model-index:
  - name: emotion_classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: image_folder
          type: image_folder
          config: FastJobs--Visual_Emotional_Analysis
          split: train
          args: FastJobs--Visual_Emotional_Analysis
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.64375
          - name: Precision
            type: precision
            value: 0.6639732142857142
          - name: F1
            type: f1
            value: 0.640682001352849

emotion_classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset. It achieves the following results on the evaluation set:

Loss: 1.0750
Accuracy: 0.6438
Precision: 0.6640
F1: 0.6407

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: cosine_with_restarts
lr_scheduler_warmup_steps: 50
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	F1
2.0755	1.0	10	2.0787	0.1437	0.1529	0.1414
2.0711	2.0	20	2.0698	0.1875	0.1926	0.1832
2.0533	3.0	30	2.0520	0.2	0.2127	0.1961
2.0225	4.0	40	2.0173	0.225	0.2228	0.2054
1.9569	5.0	50	1.9289	0.2812	0.3345	0.2544
1.8501	6.0	60	1.7792	0.3688	0.4904	0.3225
1.7072	7.0	70	1.6236	0.4313	0.4131	0.3883
1.6065	8.0	80	1.5276	0.45	0.4533	0.3920
1.539	9.0	90	1.4747	0.4938	0.4748	0.4563
1.5086	10.0	100	1.4393	0.4938	0.4557	0.4466
1.4479	11.0	110	1.3893	0.5188	0.4563	0.4696
1.3683	12.0	120	1.3534	0.5437	0.5081	0.5149
1.3288	13.0	130	1.3392	0.5563	0.5569	0.5323
1.2514	14.0	140	1.2723	0.5625	0.5467	0.5246
1.2116	15.0	150	1.2526	0.5875	0.5554	0.5601
1.1824	16.0	160	1.2047	0.5938	0.6100	0.5697
1.1323	17.0	170	1.1950	0.5813	0.5331	0.5472
1.0782	18.0	180	1.1802	0.5875	0.5911	0.5807
1.0304	19.0	190	1.1534	0.6125	0.6133	0.6012
0.982	20.0	200	1.1302	0.6	0.5923	0.5806
0.9309	21.0	210	1.1849	0.5938	0.6157	0.5723
0.9205	22.0	220	1.1483	0.6	0.6137	0.5882
0.8275	23.0	230	1.1332	0.5938	0.6192	0.5894
0.8472	24.0	240	1.1195	0.625	0.6444	0.6242
0.7974	25.0	250	1.1444	0.6062	0.6277	0.6035
0.7532	26.0	260	1.1312	0.5875	0.6036	0.5832
0.7596	27.0	270	1.1217	0.6062	0.6412	0.6098
0.6818	28.0	280	1.1736	0.5625	0.6180	0.5473
0.6484	29.0	290	1.1630	0.5563	0.5887	0.5367
0.6578	30.0	300	1.0750	0.6438	0.6640	0.6407
0.6235	31.0	310	1.0676	0.6438	0.6556	0.6422
0.5966	32.0	320	1.0531	0.6438	0.6421	0.6385
0.5819	33.0	330	1.1244	0.6188	0.6315	0.6176
0.5585	34.0	340	1.1466	0.5813	0.6136	0.5790
0.5696	35.0	350	1.0703	0.6438	0.6614	0.6481
0.5476	36.0	360	1.1136	0.6438	0.6764	0.6466
0.475	37.0	370	1.1122	0.6375	0.6612	0.6340
0.5381	38.0	380	1.1547	0.6188	0.6570	0.6122
0.5161	39.0	390	1.2268	0.5875	0.6161	0.5704
0.4528	40.0	400	1.1065	0.6188	0.6314	0.6122
0.401	41.0	410	1.1209	0.6438	0.6550	0.6440
0.4067	42.0	420	1.1440	0.6312	0.6345	0.6251
0.3831	43.0	430	1.1972	0.6188	0.6480	0.6075
0.4073	44.0	440	1.2422	0.6062	0.6644	0.6028
0.371	45.0	450	1.2152	0.5875	0.6087	0.5848
0.396	46.0	460	1.1972	0.6125	0.6306	0.6106
0.3322	47.0	470	1.2979	0.5813	0.6158	0.5811
0.3691	48.0	480	1.1657	0.625	0.6371	0.6162
0.3219	49.0	490	1.1786	0.6	0.6417	0.5997
0.3371	50.0	500	1.2126	0.6188	0.6396	0.6149
0.3781	51.0	510	1.2246	0.6	0.6244	0.5972
0.3629	52.0	520	1.1820	0.6188	0.6437	0.6122
0.3025	53.0	530	1.1795	0.6062	0.6326	0.6063
0.309	54.0	540	1.1647	0.625	0.6510	0.6252
0.2999	55.0	550	1.2023	0.6375	0.6449	0.6373

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3