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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 20E-affecthq
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: 0.7188003869719445
- name: Precision
type: precision
value: 0.7219837313936599
- name: Recall
type: recall
value: 0.7188003869719445
- name: F1
type: f1
value: 0.718989971086903
---
<!-- 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. -->
# 20E-affecthq
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8271
- Accuracy: 0.7188
- Precision: 0.7220
- Recall: 0.7188
- F1: 0.7190
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 17
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9149 | 1.0 | 194 | 1.8887 | 0.3750 | 0.3413 | 0.3750 | 0.3045 |
| 1.2903 | 2.0 | 388 | 1.2485 | 0.5792 | 0.5726 | 0.5792 | 0.5526 |
| 1.071 | 3.0 | 582 | 1.0587 | 0.6321 | 0.6258 | 0.6321 | 0.6228 |
| 1.0185 | 4.0 | 776 | 0.9817 | 0.6617 | 0.6584 | 0.6617 | 0.6553 |
| 0.894 | 5.0 | 970 | 0.9293 | 0.6869 | 0.6872 | 0.6869 | 0.6820 |
| 0.8283 | 6.0 | 1164 | 0.8881 | 0.6936 | 0.6929 | 0.6936 | 0.6905 |
| 0.8185 | 7.0 | 1358 | 0.8659 | 0.6982 | 0.7011 | 0.6982 | 0.6988 |
| 0.7499 | 8.0 | 1552 | 0.8558 | 0.7046 | 0.7050 | 0.7046 | 0.7021 |
| 0.7219 | 9.0 | 1746 | 0.8399 | 0.7124 | 0.7165 | 0.7124 | 0.7127 |
| 0.7382 | 10.0 | 1940 | 0.8300 | 0.7159 | 0.7184 | 0.7159 | 0.7145 |
| 0.6392 | 11.0 | 2134 | 0.8329 | 0.7088 | 0.7135 | 0.7088 | 0.7095 |
| 0.6549 | 12.0 | 2328 | 0.8297 | 0.7133 | 0.7135 | 0.7133 | 0.7120 |
| 0.6762 | 13.0 | 2522 | 0.8180 | 0.7156 | 0.7162 | 0.7156 | 0.7153 |
| 0.5937 | 14.0 | 2716 | 0.8271 | 0.7188 | 0.7220 | 0.7188 | 0.7190 |
| 0.569 | 15.0 | 2910 | 0.8245 | 0.7178 | 0.7175 | 0.7178 | 0.7165 |
| 0.5623 | 16.0 | 3104 | 0.8228 | 0.7165 | 0.7153 | 0.7165 | 0.7157 |
| 0.5291 | 17.0 | 3298 | 0.8238 | 0.7162 | 0.7165 | 0.7162 | 0.7156 |
| 0.5775 | 18.0 | 3492 | 0.8246 | 0.7153 | 0.7162 | 0.7153 | 0.7151 |
| 0.545 | 19.0 | 3686 | 0.8257 | 0.7178 | 0.7192 | 0.7178 | 0.7174 |
| 0.5409 | 20.0 | 3880 | 0.8245 | 0.7178 | 0.7187 | 0.7178 | 0.7177 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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