Update README.md

README.md

@@ -1,7 +1,102 @@
 ---
-license: mit
-datasets:
-- blanchon/FireRisk
-language:
+language: 
 - en
----
+
+license: mit
+
+library_name: pytorch
+
+tags:
+- computer-vision
+- image-classification
+- deep-learning
+- convnext
+- pytorch
+- forestry
+- plants
+- environment
+- ai-for-good
+
+pipeline_tag: image-classification
+
+model_name: FloraGuard
+
+model_type: ConvNeXt-Base
+
+base_model: timm/convnext_base
+
+author: Aarav
+
+description: >
+  FloraGuard is a deep learning–based image classification model designed to
+  identify and classify forest and plant-related categories. It uses a
+  ConvNeXt-Base backbone pretrained on ImageNet and a custom classification
+  head for improved accuracy and generalization.
+
+training:
+  framework: PyTorch
+  optimizer: AdamW
+  loss_function: CrossEntropyLoss
+  batch_size: 4
+  epochs: 20
+  scheduler: ReduceLROnPlateau
+  device: CUDA / CPU
+
+architecture:
+  backbone: ConvNeXt-Base (pretrained on ImageNet)
+  classification_head: >
+    Linear → BatchNorm → ReLU → Dropout → Linear
+
+dataset:
+  format: ImageFolder
+  structure: |
+    Dataset/
+    ├── train/
+    │   ├── class_1/
+    │   ├── class_2/
+    │   └── ...
+    └── val/
+        ├── class_1/
+        ├── class_2/
+        └── ...
+  description: >
+    Multi-class forest and plant image dataset organized by class folders.
+
+augmentation:
+  train:
+    - Resize (320x320)
+    - Random Horizontal Flip
+    - Random Rotation
+    - Color Jitter
+    - Normalization
+  validation:
+    - Resize (320x320)
+    - Normalization
+
+evaluation:
+  metrics:
+    - F1 Score
+    - Confusion Matrix
+    - Classification Report
+    - Training & Validation Loss Curves
+
+outputs:
+  best_model: best_model.pth
+
+use_cases:
+  - Forest monitoring
+  - Plant classification
+  - Environmental AI applications
+  - Research and education
+
+requirements:
+  - torch
+  - torchvision
+  - timm
+  - numpy
+  - matplotlib
+  - scikit-learn
+  - pillow
+---
+
+