It’s difficult to fathom just how vast and diverse our natural world is. There are over 5,000 species of mammals, 10,000 species of birds, 30,000 species of fish – and astonishingly, over 400,000 different types of flowers. In this competition, participants were challenged to build a machine learning model that identifies the type of flowers in a dataset of images (for simplicity, just over 100 types).
- DenseNets
- EfficentNets
- Transfer Learning
- Augmentation
- Ensemble
| Sl. No. | Notebook Name | GitHub Link | Kaggle Live Link |
|---|---|---|---|
| 1. | Kernel Merge Sub (DenseNet201 + EfficentNetB7) | GitHub Link | Kaggle Live Link |
| 2. | EfficientNet-With-All-5-Imagesets-S1 | GitHub Link | Kaggle Live Link |
| 3. | FlowerFlowerWhoAreYou-OnlySubmissions (Ensembling) | GitHub Link | Kaggle Live Link |
| 4. | Flower Classification (DenseNet + EffecientNetB7) | GitHub Link | Kaggle Live Link |
Solo 84th out of 848 teams (Top 10%) Leaderboard Link