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