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---
tags:
- image-classification
- timm
library_name: timm
license: mit
datasets:
- cm93/eurosat
pipeline_tag: image-classification
---
## 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
| 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 | |