---
tags:
- image-classification
- Birds
- Kaggle
- MobileNetV3Large
metrics:
- accuracy

model-index:
- name: MobileNetV3Large-Bird-Classification-Kaggle
  results:
  - task:
      name: Image Classification
      type: image-classification
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9336
---

# 500 Species Bird Classification
by Daniel Glownia

## Data
- Size: 224 x 224 x 3 ​
- 500 different bird species with at least 130 train images per species​
- 80% male birds (more colorful) and only 20% female (sex is not labeled)​
- One bird per image​
- Bird takes up 50%+ of pixels​
- Some images include noise like watermarks
  
| Dataset  | Image count​ |
| ------------- | ------------- |
| Train  | 85,085  |
| Test  | 2,500  |
| Validation  | 2,500  |

## CNN Implementation

- MobileNetV3 as base model(transfer learning)
- Trained on 100 epochs
- Optimizer: Adam
- Loss: Categorical Cross Entropy

```python
epochs = 100
batch_size = 256

inputs = pretrained_model.input
x = processing_layers(inputs)

x = Dense(256, activation='relu')(pretrained_model.output)
x = Dropout(0.2)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.2)(x)
x = Dense(64, activation='relu')(x)
x = Dropout(0.2)(x)


outputs = Dense(500, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)
```

## Results
The following confusion matrix represents the lowest performing classes. Classes with perfect scores were removed.

![plot](conf.png)

| Dataset  | Accuracy |
| ------------- | ------------- |
| Train  | 84.87%  |
| Test  | 92.20%  |
| Validation  | 93.36%  |

![plot](loss.png)