Create README.md

README.md

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+---
+license: mit
+---
+# Flower Species Classifier
+
+## Model Name
+Flower Species CNN Classifier
+
+## Model Type
+Convolutional Neural Network (CNN)
+
+## Purpose
+- Classify flowers into 5 species
+- For educational and research use
+
+## Dataset
+- Source: Kaggle Flower Dataset
+- Classes: 5 (e.g., rose, tulip, sunflower…)
+- Size: [mention total images]
+
+## Architecture
+- Conv Layers: 16 → 32 → 64 → 128 filters
+- Dense Layer: 128 units + output layer
+- Activation: ReLU (Conv/Dense), Softmax (Output)
+- Loss: categorical_crossentropy
+- Optimizer: Adam
+- Dropout: 0.5
+
+## Training Details
+- Epochs: 50
+- Batch size: [mention batch size]
+- Learning rate schedule used
+
+## Performance
+- Training Accuracy: 90%
+- Validation Accuracy: 80%
+- Validation Loss: [mention latest]
+
+## Limitations
+- Slight overfitting
+- Works best on similar dataset images
+- May fail on noisy/real-world images
+
+## Usage
+- Predict flower species from image
+- Research, educational, hobby projects
+
+## Ethics / Disclaimer
+- Not for commercial critical use
+- May misclassify images not in dataset