---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fashion-clothing-decade
  results: []
---

<!-- 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
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750014078636052/1920s.jpg?ex=656516e2&is=6552a1e2&hm=23622ffb6b0860e3e6e22e1cb2436f8058db9a3295ecba3a4ed321ce9fe51bbf&)

### 1930s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750026640572486/1930s.jpg?ex=656516e5&is=6552a1e5&hm=26d536e7b37c3ed09c023f1dfe826a067ce17fdf0691e6ba7d66a0021cfd6326&)

### 1940s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750038753706034/1940s.jpg?ex=656516e8&is=6552a1e8&hm=29340af7325e42b3de6f51277cd12135487aa7a507f9cada6f49226add4741e3&)

### 1950s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750050346782752/1950s.jpg?ex=656516eb&is=6552a1eb&hm=ca8c332c46f25ec0418709ae1efe8bb214904058ea3c687bda95c751b8ce07ec&)

### 1960s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750067967054005/1960s.jpeg?ex=656516ef&is=6552a1ef&hm=df33b8fa2c63c6994c48571871d5c5e80b4ad413abcbed166f4a6b5b13d4a7c1&)

### 1970s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750075793625240/1970s.jpg?ex=656516f1&is=6552a1f1&hm=a5f83085adcb97a1cc95d6621c0e25551d20110fe5a29aa6053fa8ee8177047a&)

### 1980s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750090263973888/1980s.jpeg?ex=656516f4&is=6552a1f4&hm=ea80189337e909097aaf297168d7d12a99ac170848bfbd31040e043ab481ca82&)

### 1990s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750101764747435/1990s.jpg?ex=656516f7&is=6552a1f7&hm=bbdca6de615c3a130ec05e32f880e76b4f7aeea449192ccfc0fd6193e2bdde5f&)

### 2000s
![](https://cdn.discordapp.com/attachments/1120417968032063538/1173750113726906418/2000s.jpg?ex=656516fa&is=6552a1fa&hm=2f681aa8e8860e07451952a92c71195180ad1a44bdd7106a8b3676f50f5c394b&)

## 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