Update README.md

README.md

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 - detection
 pipeline_tag: image-classification
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-MozieFinder (lightweight CNN-based model)
+# 🐾 MozieFinder
 
-MozieFinder is a lightweight convolutional neural network (CNN) model built from scratch using TensorFlow, designed for binary image classification: detecting whether an image contains a cat or a dog. The model is trained from the ground up on a curated dataset of ~20,000 labeled images (cats and dogs), resized to a resolution of 224x224 with data augmentation applied for improved generalization.
+**MozieFinder** is a lightweight convolutional neural network (CNN) model built from scratch using **TensorFlow**, designed to classify images as either **cats or dogs**.
 
-The model architecture is loosely inspired by ResNet, incorporating residual connections and batch normalization. MozieFinder reaches around 92% accuracy on validation data and includes approximately 1.2 million trainable parameters. It outputs a binary classification score (0 = Cat, 1 = Dog).
+- 📦 **Model Type**: Custom CNN (ResNet-inspired)
+- 🐶🐱 **Task**: Binary Image Classification (Cat vs. Dog)
+- 🧠 **Trainable Parameters**: ~1.2 million
+- 🖼️ **Input Resolution**: 224x224
+- 🏋️ **Training Data**: ~20,000 labeled cat and dog images
+- 🎯 **Validation Accuracy**: ~92%
 
-This model was created and trained from scratch by the author to demonstrate hands-on model development without pre-trained weights.
+MozieFinder was trained from scratch — no pre-trained weights were used — as a demonstration of how to build a robust image classification model end-to-end.
 
-Disclaimer: This model card has been written by the model creator. It has not been reviewed or endorsed by TensorFlow or any affiliated team.
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-⸻
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-Let me know if you’d like the Hugging Face model card version (README.md format) or include additional sections like intended use, limitations, or training details.
+> ⚠️ **Disclaimer**: This model card was written by the model creator. It has not been officially reviewed by TensorFlow or affiliated teams.