metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
widget:
  - src: >-
      https://cdn.discordapp.com/attachments/1120417968032063538/1191101288428097727/1.jpg?ex=65a43684&is=6591c184&hm=aed9f3278325ea30e30557e201adcfc43ce2ce77f2218b5f8f232a26b4ac2985&
  - src: >-
      https://cdn.discordapp.com/attachments/1120417968032063538/1191101301698867260/2.jpg?ex=65a43687&is=6591c187&hm=dee873150a2910177be30e5141f008b70ba7f55266e1e8725b422bfe0e6213f8&
metrics:
  - accuracy
model-index:
  - name: vogue-fashion-collection-15
    results: []
pipeline_tag: image-classification

vogue-fashion-collection-15

Model description

This model classifies an image into a fashion collection. It is trained on the tonyassi/vogue-runway-top15-512px dataset and fine-tuned version of google/vit-base-patch16-224-in21k.

Try the demo.

Dataset description

tonyassi/vogue-runway-top15-512px

15 fashion houses
1679 collections
87,547 images

How to use

from transformers import pipeline

# Initialize image classification pipeline
pipe = pipeline("image-classification", model="tonyassi/vogue-fashion-collection-15")

# Perform classification
result = pipe('image.png')

# Print results
print(result)

Examples

fendi,spring 2023 couture

gucci,spring 2017 ready to wear

prada,fall 2018 ready to wear

Training and evaluation data

It achieves the following results on the evaluation set:

Loss: 0.1795
Accuracy: 0.9454

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-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: 15

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0