🌸 Flower Classification Model
A deep learning model that classifies flower images into 102 different species using a transformer-based vision architecture.
This model was trained for image classification tasks and can be used in applications such as botany research tools, educational apps, plant recognition systems, and computer vision experimentation.
Model Details
Model Description
This model is an image classification system trained to recognize 102 species of flowers from photographs.
It uses a ConvNeXt-based classifier based architecture fine-tuned on a labeled flower dataset. The model extracts visual features such as petal structure, color distribution, and shape patterns to predict the correct flower category.
The goal of the project is to demonstrate practical use of modern deep learning pipelines and transformer models for real-world computer vision tasks.
Key Information
- Developed by: Ayush Jena
- Project: Flower Classification AI
- Model Type: Image Classification (Computer Vision)
- Architecture: ConvNeXt-based classifier
- Framework: PyTorch + Transformers
- Classes: 102 Flower Categories
- License: MIT
Model Sources
- Repository: https://github.com/algorithmic-alpha-721/Flower-Classifier-HF
- Demo: Hugging Face Space (Gradio Interface)
- Framework: PyTorch + Transformers
Intended Uses
Direct Use
The model can directly classify a flower image and return the predicted species.
Example applications:
- Flower identification apps
- Educational tools for botany students
- Computer vision learning projects
- AI demonstration systems
Downstream Use
The model can also be integrated into larger systems such as:
- Mobile plant recognition apps
- Agricultural AI tools
- Smart gardening systems
- Vision-based educational assistants
Out-of-Scope Use
This model should not be used for:
- Medical or biological scientific conclusions
- Automated agricultural decision systems without validation
- Identification of plants outside the trained flower dataset
The model is trained only on specific labeled flower categories and may not generalize to all plant species.
Training Details
Training Dataset
The model was trained on the Oxford 102 Flower Dataset, which contains images of 102 different flower species commonly used in computer vision benchmarks.
Dataset characteristics:
- 102 flower classes
- Thousands of labeled flower images
- Diverse visual conditions (lighting, backgrounds)
Training Procedure
The model was fine-tuned using a pretrained vision backbone.
Training Pipeline
- Image preprocessing
- Data augmentation
- Feature extraction via pretrained backbone
- Classification head training
- Evaluation and model checkpoint selection
Training Hyperparameters
Typical configuration used during training:
- Optimizer: AdamW
- Loss Function: Cross Entropy
- Batch Size: 16–32
- Epochs: 10–20
- Learning Rate: 3e-5
Evaluation
Metrics
The model was evaluated using:
- Top-1 Accuracy
- Validation Loss
These metrics measure how accurately the model predicts the correct flower species.
Results
The trained model demonstrates strong classification performance on the validation dataset and successfully distinguishes visually similar flower species.
Example predictions include:
- Rose
- Sunflower
- Tulip
- Daisy
- Lotus
How to Use the Model
Example usage in Python:
from transformers import pipeline
from PIL import Image
classifier = pipeline(
"image-classification",
model="algorithmic-alpha-721/Flower-Classifier-HF"
)
image = Image.open("flower.jpg")
result = classifier(image)
print(result)
Limitations
- Performance depends on image quality.
- Background noise may affect predictions.
- The model only recognizes flowers from the training dataset.
- Rare or visually similar species may confuse the model.
Environmental Impact
Training deep learning models consumes computational resources.
Hardware
- GPU (training environment such as Google Colab)
Framework
- PyTorch
Estimated Training Time
- Several hours depending on configuration.
Technical Specifications
Architecture
Vision-based deep learning classifier using transformer-style feature extraction.
Software
- Python
- PyTorch
- Transformers
- Hugging Face Hub
Citation
If you use this model in research or projects, please cite the repository: Ayush Jena. Flower Classification AI Model. 2026.
- GitHub Repository: https://github.com/algorithmic-alpha-721/Flower-Classifier-HF
Contact
For questions or collaboration:
- GitHub: https://github.com/algorithmic-alpha-721
- Gmail: ayushjena0412@gmail.com
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