cm93/resnet50-eurosat

Model Details

This model is based on the ResNet-50 architecture and it has been fine-tuned for satellite image classification tasks on the EuroSAT dataset.

Model type: Convolutional Neural Network (CNN)

Finetuned from model : ResNet50 (pre-trained on ImageNet-1k)

Model Sources

Repository: https://github.com/chathumal93/EuroSat-RGB-Classifiers

Training Details

Training Data

The dataset comprises JPEG composite chips extracted from Sentinel-2 satellite imagery, representing the Red, Green, and Blue bands. It encompasses 27,000 labeled and geo-referenced images across 10 Land Use and Land Cover (LULC) classes

Training Procedure

Preprocessing: Standard image preprocessing including resizing, center cropping, normalization, and data augmentation techniques [RandomHorizontalFlip and RandomVerticalFlip]

Training Hyperparameters

• Learning rate: 3e-5
• Batch size: 64
• Optimizer: AdamW
• Scheduler: PolynomialLR
• Loss: CrossEntropyLoss
• Betas=(0.9, 0.999)
• Weight_decay=0.01
• Epochs: 20

Evaluation

Results