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---
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------>*
**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 |