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---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- fair_face
metrics:
- accuracy
model-index:
- name: trained-race
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: fair_face
type: fair_face
config: '0.25'
split: validation
args: '0.25'
metrics:
- name: Accuracy
type: accuracy
value: 0.634380135110462
---
<!-- 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. -->
# trained-race
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9495
- Accuracy: 0.6344
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4995 | 0.18 | 1000 | 1.5133 | 0.4135 |
| 1.3103 | 0.37 | 2000 | 1.2797 | 0.5045 |
| 1.2417 | 0.55 | 3000 | 1.1583 | 0.5451 |
| 1.2142 | 0.74 | 4000 | 1.0952 | 0.5780 |
| 1.0633 | 0.92 | 5000 | 1.0508 | 0.5973 |
| 0.9559 | 1.11 | 6000 | 1.0179 | 0.6106 |
| 0.9984 | 1.29 | 7000 | 0.9958 | 0.6179 |
| 0.9843 | 1.48 | 8000 | 0.9780 | 0.6315 |
| 0.8943 | 1.66 | 9000 | 0.9470 | 0.6386 |
| 0.9941 | 1.84 | 10000 | 0.9495 | 0.6344 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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