Hugging Face model: AaravK25/FireRiskDatasetModel
Author: AaravK25
Created: 2026-01-12 09:54:16
Last modified: 2026-01-12 10:38:22
Pipeline: image-classification
Library: pytorch
Likes: 0 | Downloads: 0
Tags: pytorch, computer-vision, image-classification, deep-learning, convnext, forestry, plants, environment, ai-for-good, en, dataset:yunusserhat/FireRisk_Original, license:mit, region:us
Card metadata:
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