Update README.md

Updated dataset details

README.md

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+---
+annotations_creators:
+- other
+language:
+- en
+license:
+- other
+multilinguality:
+- no
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- image-classification
+task_ids:
+- multi-class-classification
+---
+
+# EuroSAT Dataset
+
+## Overview
+
+This dataset contains satellite images from the EuroSAT datasetThe dataset consists of RGB images with 10 different classes, each representing a distinct type of land use.
+
+## Dataset Summary
+
+- **Classes**: 10 (e.g., Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, Sea/Lake)
+- **Number of Images**: 27,000+ images split into training and validation sets
+- **Image Size**: 64x64 pixels, 3 channels (RGB)
+- **Data Augmentation**: Random flipping, random rotation, and normalization
+
+## Usage
+
+This dataset is ideal for training and fine-tuning image classification models, specifically for applications in remote sensing, urban planning, environmental monitoring, and agricultural management.
+
+### Key Features
+
+- **Preprocessing**: Images were preprocessed using TensorFlow, including resizing, normalizing, and applying augmentation techniques to improve model robustness.
+- **Model**: Fine-tuned with Vision Transformer (ViT) to leverage attention-based mechanisms for superior performance in image classification tasks.
+
+## Dataset Structure
+
+- **Training Set**: Approximately 80% of the dataset
+- **Validation Set**: Approximately 20% of the dataset
+- **Format**: Images are stored as PNG files with associated labels.
+
+### Labels
+- Class labels are encoded as integers and correspond to the following categories:
+  0. Annual Crop
+  1. Forest
+  2. Herbaceous Vegetation
+  3. Highway
+  4. Industrial
+  5. Pasture
+  6. Permanent Crop
+  7. Residential
+  8. River
+  9. Sea/Lake
+
+## Performance Metrics
+
+- **Accuracy**: Achieved X% accuracy on the validation set
+- **F1-Score**: Achieved X% F1-score across all classes
+
+## Practical Applications
+
+- **Urban Planning**: Identifying residential and industrial areas from satellite imagery.
+- **Agriculture**: Monitoring crop types and assessing agricultural land use.
+- **Environmental Protection**: Tracking changes in natural land covers like forests and rivers.
+
+## How to Use
+
+To use this dataset, you can directly load it through the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your_username/eurosat-land-use-classification")
+```
+
 ---
 dataset_info:
   features: