Number of prototypes: 1550
Prototype size: 1×1
Training process: Warm up → Joint training → Push → Last layer fine-tuning
Weight coefficients:
- Cross entropy: 1.0
- Clustering: -0.8
- Separation: 0.1
- L1: 0.01
- Orthogonal: 0.001
- Coherence: 0.003
Batch size: 128

Dataset Description

A face recognition dataset collected from Pinterest containing 155 different identity classes Dataset link: https://www.kaggle.com/datasets/hereisburak/pins-face-recognition

Usage

python

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load model and processor
model = AutoModelForImageClassification.from_pretrained("Ayushnangia/protovit-deit_small_patch16_224-pins")
processor = AutoImageProcessor.from_pretrained("Ayushnangia/protovit-deit_small_patch16_224-pins")

# Prepare image
image = Image.open("path_to_your_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Make prediction
outputs = model(**inputs)
predicted_label = outputs.logits.argmax(-1).item()

Additional Information

Github repo by authors of the paper ![GitHub repository][https://github.com/Henrymachiyu/ProtoViT]

For more details about the implementation and training process, please visit the my fork of ProtoVit GitHub repository.