lumenggan/katara-detector

Model Card for Katara Detector

This model identifies whether an image contains Katara from Avatar: The Last Airbender. It achieves 96% accuracy and 96.1% F1 score on the validation set.

Model Details

Model Description

A binary image classifier that determines if Katara from the animated series "Avatar: The Last Airbender" is present in an image.

• Developed by: Your Name/Organization
• Model type: Image Classification
• License: MIT
• Finetuned from model: facebook/dinov2-small

Uses

Direct Use

This model can be used to:

• Identify Katara in screenshots or fan art
• Filter or categorize ATLA-related image collections
• Power fan applications that track character appearances

# Use a pipeline as a high-level helper
from PIL import Image
from transformers import pipeline

pipe = pipeline("image-classification", model="lumenggan/katara-detector")

image = Image.open("yourimage.png")

pipe(image)

Out-of-Scope Use

This model should not be used for:

• Critical identification tasks
• Monitoring or surveillance purposes
• Making judgments about real people

Training Details

Training Data

The model was trained on a custom dataset of Katara images and non-Katara images from Avatar: The Last Airbender. The dataset was split 80/20 for training and validation.

Training Procedure

The model was fine-tuned from DINOv2-small using the following techniques:

• Dropout regularization (rate=0.3)
• Weight decay (0.01-0.05)
• Cosine learning rate schedule with restarts

Training Hyperparameters

• Learning rate: 2e-5
• Weight decay: 0.01-0.05
• Epochs: 5-15
• Batch size: 16 (effective 32 with gradient accumulation)
• Training regime: fp16 mixed precision

Evaluation

Metrics

• Accuracy: 96.0%
• F1 Score: 96.1%
• Precision: 96.8%
• Recall: 95.5%
• ROC AUC: 99.4%

Technical Specifications

Model Architecture

• Base model: facebook/dinov2-with-registers-small
• Custom classification head with dropout
• Input size: 224x224 RGB images

Compute Infrastructure

• GPU: (e.g., NVIDIA T4, A100, etc.)
• Training time: Approximately 1-2 hours

Model Card Contact

https://github.com/unLomTrois/