---
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     |