Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- scikit-learn/iris
+language:
+- en
+base_model:
+- NeuralNine999/INET
+pipeline_tag: tabular-classification
+tags:
+- biology
+---
+
+# INet - PyTorch Iris Classifier
+
+## Overview
+INet is a simple fully-connected neural network trained on the Iris dataset using PyTorch.
+It classifies iris flowers into 4 categories based on 4 features: sepal length, sepal width, petal length, and petal width.
+
+## Model Architecture
+- Input: 4 features
+- Hidden layers: 64 → 32 → 16 → 8 neurons (ReLU activations)
+- Output: 4 classes
+
+Architecture flow:
+Input(4) → Linear(64) → ReLU → Linear(32) → ReLU → Linear(16) → ReLU → Linear(8) → ReLU → Linear(4)
+
+- Loss: CrossEntropyLoss
+- Optimizer: Adam, lr=0.01
+- Epochs: 30
+
+## Files
+- inet.pth → Trained model weights
+- model.py → Contains INet class and architecture
+- README.md → This file
+
+## How to Load
+
+```python
+import torch
+from model import INet  # make sure INet class is in model.py
+
+model = INet()
+model.load_state_dict(torch.load("inet.pth"))
+model.eval()
+
+# Example usage:
+sample_input = torch.tensor([[5.1, 3.5, 1.4, 0.2]])
+pred = model(sample_input)
+pred_class = pred.argmax(dim=1).item()
+print(pred_class)
+```
+
+## Notes
+
+* Make sure PyTorch is installed correctly
+  
+```python
+pip install torch
+```
+
+* The model expects input as a tensor of shape [batch_size, 4] with float32 values.