File size: 7,907 Bytes
a7028ed 005c001 0b62d38 a7028ed 2409a1c 0b62d38 005c001 d62374f 0b62d38 d62374f 0b62d38 116ea51 0b62d38 e43d592 0b62d38 e43d592 a7028ed 005c001 a7028ed 005c001 b948167 9e21d9b 0b62d38 e43d592 116ea51 e43d592 a7028ed 2409a1c a7028ed 005c001 170a155 005c001 170a155 005c001 55480e5 a8e1506 005c001 a7028ed 005c001 a7028ed d62374f a7028ed 0e08b02 aa91e80 733a9b8 0b62d38 e43d592 0b62d38 a7028ed d62374f ed770c1 d62374f a7028ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- FastJobs/Visual_Emotional_Analysis
metrics:
- accuracy
- precision
- f1
model-index:
- name: emotion_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: FastJobs/Visual_Emotional_Analysis
type: FastJobs/Visual_Emotional_Analysis
config: FastJobs--Visual_Emotional_Analysis
split: train
args: FastJobs--Visual_Emotional_Analysis
metrics:
- name: Accuracy
type: accuracy
value: 0.66875
- name: Precision
type: precision
value: 0.7104119480438352
- name: F1
type: f1
value: 0.6712765732314218
---
# Emotion Classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/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](https://huggingface.co/google/vit-base-patch16-224-in21k).
## Training and evaluation data
### Data Split
Trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/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
|