Update README.md

README.md

@@ -10,4 +10,67 @@ metrics:
 - recall
 - precision
 pipeline_tag: image-classification
----
+---
+
+---
+language: en
+tags:
+- pytorch
+- image-classification
+- cats-vs-dogs
+- computer-vision
+datasets:
+- microsoft/cats_vs_dogs
+model-index:
+- name: Dogs vs Cats Classifier
+ results:
+ - task:
+     type: image-classification
+     name: Image Classification
+   metrics:
+     - type: accuracy
+       value: 93.25 
+       name: Validation Accuracy
+
+license: mit
+---
+
+# Dogs vs Cats Classifier
+
+This model classifies images as either cats or dogs using a Convolutional Neural Network (CNN) architecture.
+
+## Model description
+
+Architecture:
+- 4 convolutional blocks (Conv2D → ReLU → BatchNorm → MaxPool)
+- Feature channels: 3→64→128→256→512
+- Global average pooling
+- Fully connected layers: 512→256→1
+- Binary classification output
+
+## Training
+
+- Dataset: microsoft/cats_vs_dogs
+- Training/Validation split: 80/20
+- Input size: 224x224 RGB images
+- Trained for 10 epochs
+- Best validation accuracy: 93.25%
+
+## Intended uses
+
+- Image classification between cats and dogs
+- Transfer learning base for similar pet/animal classification tasks
+
+## Limitations
+
+- Only trained on cats and dogs
+- May not perform well on:
+ - Low quality/blurry images
+ - Unusual angles/poses
+ - Multiple animals in one image
+
+## Input
+
+RGB images resized to 224x224 pixels, normalized using ImageNet statistics:
+- mean=[0.485, 0.456, 0.406]
+- std=[0.229, 0.224, 0.225]