|
--- |
|
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: 0.675 |
|
- name: Precision |
|
type: precision |
|
value: 0.6854354001733034 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6750572520063745 |
|
--- |
|
|
|
# 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 0.1429. |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0683 |
|
- Accuracy: 0.675 |
|
- Precision: 0.6854 |
|
- F1: 0.6751 |
|
|
|
## 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 |
|
|
|
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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| |
|
| 2.0804 | 1.0 | 10 | 2.0881 | 0.1437 | 0.2313 | 0.1165 | |
|
| 2.0839 | 2.0 | 20 | 2.0846 | 0.1562 | 0.1772 | 0.1250 | |
|
| 2.072 | 3.0 | 30 | 2.0786 | 0.1562 | 0.1835 | 0.1251 | |
|
| 2.0676 | 4.0 | 40 | 2.0702 | 0.1562 | 0.2213 | 0.1265 | |
|
| 2.053 | 5.0 | 50 | 2.0586 | 0.1625 | 0.2289 | 0.1330 | |
|
| 2.0346 | 6.0 | 60 | 2.0390 | 0.1938 | 0.3508 | 0.1830 | |
|
| 2.0072 | 7.0 | 70 | 2.0080 | 0.2437 | 0.3131 | 0.2285 | |
|
| 1.9672 | 8.0 | 80 | 1.9506 | 0.325 | 0.3516 | 0.3209 | |
|
| 1.8907 | 9.0 | 90 | 1.8587 | 0.3438 | 0.4010 | 0.3361 | |
|
| 1.7841 | 10.0 | 100 | 1.7300 | 0.3937 | 0.4617 | 0.3860 | |
|
| 1.6688 | 11.0 | 110 | 1.6084 | 0.4625 | 0.4958 | 0.4402 | |
|
| 1.5803 | 12.0 | 120 | 1.5305 | 0.4875 | 0.5327 | 0.4661 | |
|
| 1.5069 | 13.0 | 130 | 1.4577 | 0.5437 | 0.5171 | 0.5126 | |
|
| 1.4353 | 14.0 | 140 | 1.3955 | 0.55 | 0.6004 | 0.5380 | |
|
| 1.3913 | 15.0 | 150 | 1.3353 | 0.5437 | 0.6508 | 0.4995 | |
|
| 1.3551 | 16.0 | 160 | 1.2874 | 0.5563 | 0.5251 | 0.5201 | |
|
| 1.2889 | 17.0 | 170 | 1.2618 | 0.5687 | 0.5829 | 0.5475 | |
|
| 1.2387 | 18.0 | 180 | 1.2455 | 0.5687 | 0.5723 | 0.5587 | |
|
| 1.1977 | 19.0 | 190 | 1.2210 | 0.5875 | 0.6221 | 0.5858 | |
|
| 1.1447 | 20.0 | 200 | 1.1909 | 0.6 | 0.6153 | 0.5840 | |
|
| 1.0959 | 21.0 | 210 | 1.1918 | 0.5813 | 0.5896 | 0.5609 | |
|
| 1.0657 | 22.0 | 220 | 1.1343 | 0.625 | 0.6352 | 0.6184 | |
|
| 0.9869 | 23.0 | 230 | 1.1309 | 0.625 | 0.6549 | 0.6258 | |
|
| 0.9576 | 24.0 | 240 | 1.1071 | 0.6312 | 0.6373 | 0.6280 | |
|
| 0.9234 | 25.0 | 250 | 1.1407 | 0.6312 | 0.6469 | 0.6279 | |
|
| 0.876 | 26.0 | 260 | 1.2006 | 0.5625 | 0.6040 | 0.5514 | |
|
| 0.8969 | 27.0 | 270 | 1.1007 | 0.6125 | 0.6290 | 0.6121 | |
|
| 0.8066 | 28.0 | 280 | 1.1208 | 0.6 | 0.6650 | 0.5971 | |
|
| 0.7579 | 29.0 | 290 | 1.1328 | 0.6125 | 0.6625 | 0.6035 | |
|
| 0.7581 | 30.0 | 300 | 1.1039 | 0.6125 | 0.6401 | 0.6121 | |
|
| 0.7164 | 31.0 | 310 | 1.0862 | 0.65 | 0.6723 | 0.6494 | |
|
| 0.7075 | 32.0 | 320 | 1.0575 | 0.65 | 0.6683 | 0.6485 | |
|
| 0.6655 | 33.0 | 330 | 1.1186 | 0.6125 | 0.6483 | 0.6134 | |
|
| 0.5947 | 34.0 | 340 | 1.1133 | 0.625 | 0.6439 | 0.6272 | |
|
| 0.5813 | 35.0 | 350 | 1.1071 | 0.6312 | 0.6735 | 0.6337 | |
|
| 0.6322 | 36.0 | 360 | 1.0839 | 0.6312 | 0.6591 | 0.6324 | |
|
| 0.561 | 37.0 | 370 | 1.1040 | 0.625 | 0.6425 | 0.6220 | |
|
| 0.558 | 38.0 | 380 | 1.0727 | 0.6125 | 0.6255 | 0.6112 | |
|
| 0.5372 | 39.0 | 390 | 1.1417 | 0.6312 | 0.6545 | 0.6292 | |
|
| 0.5146 | 40.0 | 400 | 1.0967 | 0.6312 | 0.6645 | 0.6285 | |
|
| 0.4968 | 41.0 | 410 | 1.1187 | 0.6312 | 0.6543 | 0.6316 | |
|
| 0.4593 | 42.0 | 420 | 1.0683 | 0.675 | 0.6854 | 0.6751 | |
|
| 0.4392 | 43.0 | 430 | 1.0937 | 0.6375 | 0.6481 | 0.6374 | |
|
| 0.4503 | 44.0 | 440 | 1.1320 | 0.625 | 0.6536 | 0.6255 | |
|
| 0.3918 | 45.0 | 450 | 1.1218 | 0.6312 | 0.6464 | 0.6312 | |
|
| 0.4236 | 46.0 | 460 | 1.2074 | 0.5938 | 0.6188 | 0.5911 | |
|
| 0.3858 | 47.0 | 470 | 1.1769 | 0.5813 | 0.6106 | 0.5809 | |
|
| 0.392 | 48.0 | 480 | 1.1572 | 0.625 | 0.6381 | 0.6216 | |
|
| 0.3708 | 49.0 | 490 | 1.2293 | 0.6 | 0.6388 | 0.5953 | |
|
| 0.3346 | 50.0 | 500 | 1.2205 | 0.5938 | 0.6188 | 0.5943 | |
|
| 0.3831 | 51.0 | 510 | 1.2875 | 0.5875 | 0.5982 | 0.5845 | |
|
| 0.4161 | 52.0 | 520 | 1.2355 | 0.5938 | 0.6421 | 0.5799 | |
|
| 0.3736 | 53.0 | 530 | 1.2361 | 0.6062 | 0.6301 | 0.6006 | |
|
| 0.3278 | 54.0 | 540 | 1.1670 | 0.6312 | 0.6520 | 0.6286 | |
|
| 0.3295 | 55.0 | 550 | 1.1807 | 0.6438 | 0.6712 | 0.6457 | |
|
| 0.3357 | 56.0 | 560 | 1.2007 | 0.625 | 0.6279 | 0.6239 | |
|
| 0.3169 | 57.0 | 570 | 1.2314 | 0.5938 | 0.6257 | 0.5942 | |
|
| 0.3193 | 58.0 | 580 | 1.2068 | 0.6188 | 0.6397 | 0.6208 | |
|
| 0.3128 | 59.0 | 590 | 1.2753 | 0.5875 | 0.5919 | 0.5760 | |
|
| 0.3077 | 60.0 | 600 | 1.2154 | 0.625 | 0.6432 | 0.6238 | |
|
| 0.2751 | 61.0 | 610 | 1.2596 | 0.6125 | 0.6216 | 0.6099 | |
|
| 0.2921 | 62.0 | 620 | 1.2716 | 0.6188 | 0.6467 | 0.6189 | |
|
| 0.2939 | 63.0 | 630 | 1.2213 | 0.625 | 0.6350 | 0.6264 | |
|
| 0.2732 | 64.0 | 640 | 1.3456 | 0.5938 | 0.6189 | 0.5897 | |
|
| 0.2806 | 65.0 | 650 | 1.2491 | 0.6188 | 0.6393 | 0.6162 | |
|
| 0.2453 | 66.0 | 660 | 1.2312 | 0.6188 | 0.6465 | 0.6195 | |
|
| 0.3077 | 67.0 | 670 | 1.2356 | 0.6375 | 0.6564 | 0.6373 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.0 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.13.3 |
|
|
