README.md

metadata

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: []

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

How to use

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

Model description

This model is a fine-tuned version of 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