File size: 4,262 Bytes
f3fbb51 fcb31ae f3fbb51 fcb31ae f3fbb51 fcb31ae f3fbb51 fcb31ae f3fbb51 fcb31ae f3fbb51 |
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 |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-FER2013
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7011494252873564
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-FER2013
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8313
- Accuracy: 0.7011
## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7483 | 1.0 | 202 | 1.7005 | 0.3386 |
| 1.4419 | 2.0 | 404 | 1.3213 | 0.5315 |
| 1.2917 | 3.0 | 606 | 1.1559 | 0.5785 |
| 1.2437 | 4.0 | 808 | 1.0729 | 0.6162 |
| 1.1635 | 5.0 | 1010 | 1.0161 | 0.6311 |
| 1.1087 | 6.0 | 1212 | 0.9862 | 0.6465 |
| 1.0964 | 7.0 | 1414 | 0.9901 | 0.6440 |
| 1.0895 | 8.0 | 1616 | 0.9410 | 0.6555 |
| 1.0384 | 9.0 | 1818 | 0.9221 | 0.6628 |
| 1.0333 | 10.0 | 2020 | 0.9142 | 0.6681 |
| 1.0016 | 11.0 | 2222 | 0.9081 | 0.6681 |
| 0.9503 | 12.0 | 2424 | 0.9013 | 0.6712 |
| 0.9804 | 13.0 | 2626 | 0.8937 | 0.6771 |
| 0.9712 | 14.0 | 2828 | 0.8809 | 0.6830 |
| 1.0151 | 15.0 | 3030 | 0.8704 | 0.6855 |
| 0.9739 | 16.0 | 3232 | 0.8886 | 0.6775 |
| 0.9267 | 17.0 | 3434 | 0.8653 | 0.6855 |
| 0.9428 | 18.0 | 3636 | 0.8633 | 0.6848 |
| 0.9654 | 19.0 | 3838 | 0.8697 | 0.6809 |
| 0.9256 | 20.0 | 4040 | 0.8559 | 0.6855 |
| 0.9345 | 21.0 | 4242 | 0.8533 | 0.6883 |
| 0.9479 | 22.0 | 4444 | 0.8548 | 0.6907 |
| 0.8829 | 23.0 | 4646 | 0.8461 | 0.6851 |
| 0.8999 | 24.0 | 4848 | 0.8399 | 0.6883 |
| 0.9047 | 25.0 | 5050 | 0.8403 | 0.6973 |
| 0.9415 | 26.0 | 5252 | 0.8437 | 0.6952 |
| 0.937 | 27.0 | 5454 | 0.8393 | 0.6931 |
| 0.8692 | 28.0 | 5656 | 0.8331 | 0.6977 |
| 0.9396 | 29.0 | 5858 | 0.8418 | 0.6973 |
| 0.8712 | 30.0 | 6060 | 0.8392 | 0.6921 |
| 0.9426 | 31.0 | 6262 | 0.8324 | 0.7011 |
| 0.884 | 32.0 | 6464 | 0.8325 | 0.6959 |
| 0.8433 | 33.0 | 6666 | 0.8300 | 0.6987 |
| 0.8869 | 34.0 | 6868 | 0.8328 | 0.6963 |
| 0.89 | 35.0 | 7070 | 0.8324 | 0.6973 |
| 0.8639 | 36.0 | 7272 | 0.8317 | 0.6956 |
| 0.8844 | 37.0 | 7474 | 0.8315 | 0.6970 |
| 0.8621 | 38.0 | 7676 | 0.8334 | 0.6991 |
| 0.8942 | 39.0 | 7878 | 0.8350 | 0.6998 |
| 0.8609 | 40.0 | 8080 | 0.8313 | 0.7011 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|