---
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
datasets:
- yunusserhat/FireRisk_Original
---


**Evaluation Results**

*Classification Report & Confusion Matrix------>*

![image](https://cdn-uploads.huggingface.co/production/uploads/6946d101b51464b501b63953/mRic1Jb_ITOzL_79-CzAp.png)


**Notes**

-*Overall accuracy:* 0.63 or 63%

-*Best performing classes:*

    1.Non-burnable (F1: 0.85)

    2.Very_Low (F1: 0.74)

    3.Water (F1: 0.78)

-*Weaker performance on:*

    1.Moderate

    2.High

    3.Low