Dashboard: Flower Classification with Transfer Learning¶
Description¶
Computer Vision pipeline that classifies flower images using Transfer Learning with MobileNetV2.
What is Transfer Learning? Instead of training a neural network from scratch (which would require millions of images), we use a network already trained on ImageNet and adapt it to our problem.
Pipeline¶
1. DOWNLOAD 2. EMBEDDINGS 3. CLASSIFICATION 4. VISUALIZATION
3,670 flowers MobileNetV2 Traditional ML Dashboard
5 classes 1280 features KNN/SVM/RF Plotly
Results¶
| Model | Accuracy |
|---|---|
| SVM | 89.9% |
| Random Forest | 86.5% |
| KNN | 86.2% |
Visualizations¶
The dashboard includes 4 interactive tabs:
- t-SNE: 2D projection of embeddings - similar flowers appear together
- Comparison: Bars with accuracy for each model
- Confusion Matrix: Hits/errors by class (percentages)
- Distribution: Radar chart of the dataset
View Dashboard¶
Run the Exercise¶
cd ejercicios/04_machine_learning/flores_transfer_learning/
pip install -r requirements.txt
python 01_flores_transfer_learning.py
Requirements: TensorFlow (GPU recommended but works on CPU)
--- Course: Big Data with Python - From Zero to Production Professor: Juan Marcelo Gutierrez Miranda | @TodoEconometria Hash ID: 4e8d9b1a5f6e7c3d2b1a0f9e8d7c6b5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c
Academic references:
- Sandler, M., et al. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR.
- Yosinski, J., et al. (2014). How transferable are features in deep neural networks? NeurIPS.
- van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. JMLR.