File size: 4,238 Bytes
de52d53 e8f0eb8 de52d53 e8f0eb8 de52d53 a5109ad e8f0eb8 de52d53 a5109ad de52d53 e8f0eb8 de52d53 e8f0eb8 de52d53 e8f0eb8 de52d53 e8f0eb8 de52d53 e8f0eb8 de52d53 e8f0eb8 a5109ad e8f0eb8 |
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 |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: FFPP-Raw_1FPS_faces-expand-0-aligned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.99837772836593
- name: Recall
type: recall
value: 0.993161411568177
- name: Precision
type: precision
value: 0.9993696485790828
- name: F1
type: f1
value: 0.9962558584033724
---
<!-- 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. -->
# FFPP-Raw_1FPS_faces-expand-0-aligned
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
- Accuracy: 0.9984
- Recall: 0.9932
- Precision: 0.9994
- F1: 0.9963
- Roc Auc: 1.0000
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.0983 | 1.0 | 1377 | 0.0679 | 0.9743 | 0.9700 | 0.9165 | 0.9425 | 0.9961 |
| 0.0917 | 2.0 | 2755 | 0.0342 | 0.9896 | 0.9718 | 0.9803 | 0.9760 | 0.9993 |
| 0.0291 | 3.0 | 4132 | 0.0161 | 0.9940 | 0.9908 | 0.9818 | 0.9863 | 0.9998 |
| 0.0454 | 4.0 | 5510 | 0.0136 | 0.9950 | 0.9851 | 0.9917 | 0.9884 | 0.9998 |
| 0.0302 | 5.0 | 6887 | 0.0075 | 0.9972 | 0.9896 | 0.9976 | 0.9936 | 1.0000 |
| 0.0073 | 6.0 | 8265 | 0.0064 | 0.9976 | 0.9931 | 0.9957 | 0.9944 | 1.0000 |
| 0.016 | 7.0 | 9642 | 0.0067 | 0.9975 | 0.9934 | 0.9949 | 0.9941 | 1.0000 |
| 0.0054 | 8.0 | 11020 | 0.0058 | 0.9978 | 0.9915 | 0.9984 | 0.9949 | 1.0000 |
| 0.0237 | 9.0 | 12397 | 0.0063 | 0.9975 | 0.9894 | 0.9993 | 0.9943 | 1.0000 |
| 0.0088 | 10.0 | 13775 | 0.0042 | 0.9982 | 0.9920 | 0.9995 | 0.9957 | 1.0000 |
| 0.0078 | 11.0 | 15152 | 0.0043 | 0.9982 | 0.9921 | 0.9994 | 0.9957 | 1.0000 |
| 0.0142 | 12.0 | 16530 | 0.0040 | 0.9982 | 0.9939 | 0.9979 | 0.9959 | 1.0000 |
| 0.0058 | 13.0 | 17907 | 0.0035 | 0.9983 | 0.9930 | 0.9992 | 0.9961 | 1.0000 |
| 0.0076 | 14.0 | 19285 | 0.0040 | 0.9981 | 0.9920 | 0.9994 | 0.9957 | 1.0000 |
| 0.0032 | 15.0 | 20662 | 0.0036 | 0.9983 | 0.9926 | 0.9995 | 0.9960 | 1.0000 |
| 0.0154 | 16.0 | 22040 | 0.0033 | 0.9983 | 0.9928 | 0.9996 | 0.9962 | 1.0000 |
| 0.0041 | 17.0 | 23417 | 0.0032 | 0.9984 | 0.9925 | 0.9999 | 0.9962 | 1.0000 |
| 0.002 | 18.0 | 24795 | 0.0032 | 0.9984 | 0.9933 | 0.9992 | 0.9962 | 1.0000 |
| 0.0024 | 19.0 | 26172 | 0.0031 | 0.9984 | 0.9932 | 0.9994 | 0.9963 | 1.0000 |
| 0.0023 | 19.99 | 27540 | 0.0031 | 0.9984 | 0.9927 | 0.9998 | 0.9963 | 1.0000 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|