---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
metrics:
- accuracy
model-index:
- name: fashion-clothing-decade
  results: []
pipeline_tag: image-classification
---

<!-- 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. -->

# Fashion Clothing Decade
This model predicts what decade clothing is from. It takes an image and outputs one of the following labels: 
**1910s, 1920s, 1930s, 1940s, 1950s, 1960s, 1970s, 1980s, 1990s, 2000s**

Try the [demo](https://huggingface.co/spaces/tonyassi/Which-decade-are-you-from)!

### How to use
```python
from transformers import pipeline

pipe = pipeline("image-classification", model="tonyassi/fashion-clothing-decade")
result = pipe('image.png')

print(result)
```

## Dataset
Trained on a total of 2500 images. ~250 images from each label.

### 1910s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/zdb7EyuVxp1ncGrkoAT7h.jpeg)

### 1920s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/GGM1mMwezbsfPg2dKIvvd.jpeg)

### 1930s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/rDcMdiH3q7UHtQcfSLYzn.jpeg)

### 1940s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/TpDsDnXMubqvfu8dn6nNA.jpeg)

### 1950s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/lpMCJ9PfolWjhFqb81D1w.jpeg)

### 1960s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/x0FOiI2IMtHXthCafa76t.jpeg)

### 1970s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/H45UJGv9lzXlxF_Z616Cj.jpeg)

### 1980s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/74d7kg69pRFDrv1QjTt9G.jpeg)

### 1990s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/FZ__rQWiIAZN_1q1eOaNJ.jpeg)

### 2000s
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/648a824a8ca6cf9857d1349c/h81edMfzSYnWBxb7ZVliB.jpeg)

## Model description
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).

## Training and evaluation data
- Loss: 0.8707
- Accuracy: 0.7505

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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: 10

### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1