JavideuS/aid-image-classification

Model Card for Aerial Image Classification (CNN & Classic ML)

Model Details

Model Description

This repository contains two types of models for classifying aerial images from the AID dataset:

1. 1.Convolutional Neural Network (CNN): A lightweight ResNet-based model.
2. 2.Classic Machine Learning: A Bag of Features (BoF) pipeline using SIFT descriptors and Softmax Regression.

These models were developed as part of a machine learning assignment to evaluate deep learning approaches against classical computer vision methods.

• —Model types:
• —CNN (PyTorch)
• —BoVW + ML algorithm (Scikit-learn/Joblib)
• —Language(s): English
• —Resources:
• —CNN: ~1M parameters, ~16MB.
• —Classic ML: ~100MB (includes vocabulary).

Model Architecture

The architecture consists of an initial convolution layer followed by three residual blocks.

• —Residual Blocks: Enable deeper feature extraction without degradation.
• —Layers: Convolution, Batch Normalization, Max-Pooling.
• —Input: $600 \times 600$ pixel images (resized as needed by the pipeline).

Uses

Direct Use

The model is intended for classifying high-resolution aerial scenes into one of 30 categories. It is suitable for:

• —Autonomous UAV navigation and mapping.
• —Environmental monitoring.
• —Land use classification.

Downstream Use

This model can be fine-tuned on other aerial or satellite imagery datasets.

Training Data

The model was trained on the Aerial Image Dataset (AID).

• —Source: Google Earth imagery.
• —Size: 10,000 images.
• —Classes: 30 (e.g., Airport, Beach, Forest, Industrial, etc.).
• —Split: 90% Training / 10% Test.

Performance

The CNN significantly outperformed classical Machine Learning methods (SVM, Random Forest, etc.) evaluated on the same dataset.

Comparison with Classical Methods

Limitations

• —Data Bias: The model is trained on Google Earth imagery (AID), so it may not generalize perfectly to aerial images with significantly different sensors, resolutions, or lighting conditions.
• —Scope: Limited to the 30 classes defined in the AID dataset.

How to Get Started

You can use the provided demo.ipynb notebook for a complete example. Below is a snippet to load both models.

1. Load Classic ML Model

from huggingface_hub import hf_hub_download
import joblib

# Download model
model_path = hf_hub_download(
    repo_id="JavideuS/aid-image-classification",
    filename="classicML/models/bovw_softmax.pkl"
)

# Load pipeline
bundle = joblib.load(model_path)
pipeline = bundle['pipeline']
label_encoder = bundle['label_encoder']

# Predict
# pipeline.predict(["path/to/image.jpg"])

2. Load CNN Model

import torch
from NeuralNets.model import PiattiCNN # Ensure you have the model definition
from huggingface_hub import hf_hub_download

# Download checkpoints
checkpoints_path = hf_hub_download(
    repo_id="JavideuS/aid-image-classification",
    filename="neuralNet/models/PiattiVL_v0.69.pth"
)

# Load model
checkpoints = torch.load(checkpoints_path, map_location='cpu')
model = PiattiCNN(num_classes=checkpoints['num_classes'])
model.load_state_dict(checkpoints['model_state_dict'])
model.eval()

# Inference
# ...

Citation

If you use this model or the AID dataset, please cite the original dataset paper:

@article{aid_dataset,
  title={AID: A Scene Classification Dataset},
  author={Xia, Gui-Song and et al.},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017}
}