| --- |
| 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 |
