Ayushnangia/protovit-deit_small_patch16_224-cub

ProtoViT Model - deit_small_patch16_224 (CUB)

This is a fine-tuned deit_small_patch16_224 model trained on CUB-200-2011 from the paper "Interpretable Image Classification with Adaptive Prototype-based Vision Transformers".

Model Details

• Base architecture: deit_small_patch16_224
• Dataset: CUB-200-2011
• Number of classes: 200
• Fine-tuned checkpoint: 14finetuned0.8576
• Accuracy: 85.76%

Training Details

• Number of prototypes: 2000
• 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

Fine-grained bird species classification dataset with 200 different bird species Dataset link: https://www.vision.caltech.edu/datasets/cub_200_2011/

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

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

# 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][https://github.com/ayushnangia/ProtoViT].