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
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
| Model | Phase | Avg Loss | Accuracy |
|---|
| resnet50-eurosat | Train | 0.076420 | 97.56% |
| Validation | 0.054377 | 98.30% |
| Test | 0.058930 | 98.07% |
| Model | Accuracy | Precision | Recall | F1 |
|---|
| resnet50-eurosat | 98.07% | 0.98078 | 0.98074 | 0.98074 |
