Update README.md

README.md

@@ -3,6 +3,48 @@ tags:
 - image-classification
 - timm
 library_name: timm
-license: apache-2.0
+datasets:
+- cm93/eurosat
+pipeline_tag: image-classification
 ---
-# Model card for resnet50-eurosat
+## Model Details
+
+**Model type:** Convolutional Neural Network (CNN)
+
+**Finetuned from model :** ResNet50 (pre-trained on ImageNet-1k at resolution 224x224)
+
+### 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     |