dennisjooo/emotion_classification

Emotion Classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the FastJobs/Visual_Emotional_Analysis dataset.

In theory, the accuracy for a random guess on this dataset is 0.125 (8 labels and you need to choose one).

It achieves the following results on the evaluation set:

• Loss: 1.0511
• Accuracy: 0.6687
• Precision: 0.7104
• F1: 0.6713

Model description

The Vision Transformer base version trained on ImageNet-21K released by Google. Further details can be found on their repo.

Training and evaluation data

Data Split

Trained on FastJobs/Visual_Emotional_Analysis dataset. Used a 4:1 ratio for training and development sets and a random seed of 42. Also used a seed of 42 for batching the data, completely unrelated lol.

Pre-processing Augmentation

The main pre-processing phase for both training and evaluation includes:

• Bilinear interpolation to resize the image to (224, 224, 3) because it uses ImageNet images to train the original model
• Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] just like the original model

Other than the aforementioned pre-processing, the training set was augmented using:

• Random horizontal & vertical flip
• Color jitter
• Random resized crop

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: 150
• num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	F1
2.079	1.0	10	2.0895	0.0563	0.0604	0.0521
2.0789	2.0	20	2.0851	0.0563	0.0602	0.0529
2.0717	3.0	30	2.0773	0.0813	0.0858	0.0783
2.0613	4.0	40	2.0658	0.125	0.1997	0.1333
2.0445	5.0	50	2.0483	0.1875	0.2569	0.1934
2.0176	6.0	60	2.0206	0.2313	0.2692	0.2384
1.9894	7.0	70	1.9763	0.3063	0.3033	0.2983
1.9232	8.0	80	1.8912	0.3625	0.3307	0.3194
1.8256	9.0	90	1.7775	0.4062	0.3531	0.3600
1.732	10.0	100	1.6580	0.4688	0.4158	0.4133
1.6406	11.0	110	1.5597	0.5	0.4358	0.4370
1.5584	12.0	120	1.4855	0.5125	0.4792	0.4784
1.4898	13.0	130	1.4248	0.5437	0.5011	0.5098
1.4216	14.0	140	1.3692	0.5687	0.5255	0.5289
1.3701	15.0	150	1.3158	0.5687	0.5346	0.5360
1.3438	16.0	160	1.2842	0.5437	0.5451	0.5098
1.2799	17.0	170	1.2620	0.5625	0.5169	0.5194
1.2481	18.0	180	1.2321	0.5938	0.6003	0.5811
1.1993	19.0	190	1.2108	0.5687	0.5640	0.5412
1.1599	20.0	200	1.1853	0.55	0.5434	0.5259
1.1087	21.0	210	1.1839	0.5563	0.5670	0.5380
1.0757	22.0	220	1.1905	0.55	0.5682	0.5308
0.9985	23.0	230	1.1509	0.6375	0.6714	0.6287
0.9776	24.0	240	1.1048	0.6188	0.6222	0.6127
0.9331	25.0	250	1.1196	0.6125	0.6345	0.6072
0.8887	26.0	260	1.1424	0.5938	0.6174	0.5867
0.879	27.0	270	1.1232	0.6062	0.6342	0.5978
0.8369	28.0	280	1.1172	0.6	0.6480	0.5865
0.7864	29.0	290	1.1285	0.5938	0.6819	0.5763
0.7775	30.0	300	1.0511	0.6687	0.7104	0.6713
0.7281	31.0	310	1.0295	0.6562	0.6596	0.6514
0.7348	32.0	320	1.0398	0.6375	0.6353	0.6319
0.6896	33.0	330	1.0729	0.6062	0.6205	0.6062
0.613	34.0	340	1.0505	0.6438	0.6595	0.6421
0.6034	35.0	350	1.0827	0.6375	0.6593	0.6376
0.6236	36.0	360	1.1271	0.6125	0.6238	0.6087
0.5607	37.0	370	1.0985	0.6062	0.6254	0.6015
0.5835	38.0	380	1.0791	0.6375	0.6624	0.6370
0.5889	39.0	390	1.1300	0.6062	0.6529	0.6092
0.5137	40.0	400	1.1062	0.625	0.6457	0.6226
0.4804	41.0	410	1.1452	0.6188	0.6403	0.6158
0.4811	42.0	420	1.1271	0.6375	0.6478	0.6347
0.5179	43.0	430	1.1942	0.5875	0.6185	0.5874
0.4744	44.0	440	1.1515	0.6125	0.6329	0.6160
0.4327	45.0	450	1.1321	0.6375	0.6669	0.6412
0.4565	46.0	460	1.1742	0.625	0.6478	0.6251
0.4006	47.0	470	1.1675	0.6062	0.6361	0.6079
0.4541	48.0	480	1.1542	0.6125	0.6404	0.6152
0.3689	49.0	490	1.2190	0.5875	0.6134	0.5896
0.3794	50.0	500	1.2002	0.6062	0.6155	0.6005
0.429	51.0	510	1.2904	0.575	0.6207	0.5849
0.431	52.0	520	1.2416	0.5875	0.6028	0.5794
0.3813	53.0	530	1.2073	0.6125	0.6449	0.6142
0.365	54.0	540	1.2083	0.6062	0.6454	0.6075
0.3714	55.0	550	1.1627	0.6375	0.6576	0.6390
0.3393	56.0	560	1.1620	0.6438	0.6505	0.6389
0.3676	57.0	570	1.1501	0.625	0.6294	0.6258
0.3371	58.0	580	1.2779	0.5875	0.6000	0.5792
0.3325	59.0	590	1.2719	0.575	0.5843	0.5651
0.3509	60.0	600	1.2956	0.6	0.6422	0.6059

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3