---
license: apache-2.0
base_model: google/efficientnet-b2
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- biology
- vision
- image-classification
widgets:
- src: https://upload.wikimedia.org/wikipedia/commons/thumb/4/45/Eopsaltria_australis_-_Mogo_Campground.jpg/640px-Eopsaltria_australis_-_Mogo_Campground.jpg
  example_title: Eastern Yellow Robin
- src: https://peregrinefund.org/sites/default/files/styles/raptor_banner_600x430/public/2019-11/raptor-er-bald-eagle-portrait-screaming-james-bodkin.jpg?itok=ruN52TUp
  example_title: Bald Eagle
---

# Bird Classifier EfficientNet-B2 

## Model Description

This model is a fine-tuned version of [google/efficientnet-b2](https://huggingface.co/google/efficientnet-b2)
on the [gpiosenka/100-bird-species](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) dataset available on Kaggle.
The dataset used to train the model was taken on September 24th, 2023.

In theory, the accuracy for a random guess on this dataset is 0.0019047619 (essentially 1/525). 
The model performed significantly well on all three sets with result being:

- **Training**: 0.999480
- **Validation**: 0.985904
- **Test**: 0.991238

## Intended Uses

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=efficientnet) to look for
fine-tuned versions on a task that interests you.

Here is an example of the model in action using a picture of a bird

```python
# Importing the libraries needed
import torch
import urllib.request
from PIL import Image
from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification

# Determining the file URL
url = 'https://images.unsplash.com/photo-1444464666168-49d633b86797?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8YmlyZHxlbnwwfHwwfHx8MA%3D%3D&w=1000&q=80'

# Opening the image using PIL
img = Image.open(urllib.request.urlretrieve(url)[0])

# Loading the model and preprocessor from HuggingFace
preprocessor = EfficientNetImageProcessor.from_pretrained("dennisjooo/Birds-Classifier-EfficientNetB2")
model = EfficientNetForImageClassification.from_pretrained("dennisjooo/Birds-Classifier-EfficientNetB2")

# Preprocessing the input
inputs = preprocessor(img, return_tensors="pt")

# Running the inference
with torch.no_grad():
    logits = model(**inputs).logits

# Getting the predicted label
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

Or alternatively you can streamline it using Huggingface's Pipeline

```python
# Importing the libraries needed
import torch
import urllib.request
from PIL import Image
from transformers import pipeline

# Determining the file URL
url = 'https://images.unsplash.com/photo-1444464666168-49d633b86797?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8YmlyZHxlbnwwfHwwfHx8MA%3D%3D&w=1000&q=80'

# Opening the image using PIL
img = Image.open(urllib.request.urlretrieve(url)[0])

# Loading the model and preprocessor using Pipeline
pipe = pipeline("image-classification", model="dennisjooo/Birds-Classifier-EfficientNetB2")

# Running the inference
result = pipe(img)[0]

# Printing the result label
print(result['label'])
```

## Training and Evaluation

### Data

The dataset was taken from [gpiosenka/100-bird-species](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) on Kaggle. 
It contains a set of 525 bird species, with 84,635 training images, 2,625 each for validation and test images. 
Every image in the dataset is a 224 by 224 RGB image.

The training process used the same split provided by the author.
For more details, please refer to the [author's Kaggle page](https://www.kaggle.com/datasets/gpiosenka/100-bird-species).

### Training Procedure 

The training was done using PyTorch on Kaggle's free P100 GPU. The process also includes the usage of Lightning and Torchmetrics libraries.

### Preprocessing 
Each image is preprocessed according to the the orginal author's [config](https://huggingface.co/google/efficientnet-b2/blob/main/preprocessor_config.json).

The training set was also augmented using:

- Random rotation of 10 degrees with probability of 50%
- Random horizontal flipping with probability of 50%

### Training Hyperparameters

The following are the hyperparameters used for training:

- **Training regime:** fp32
- **Optimizer**: Adam with default betas
- **Learning rate**: 1e-3
- **Learning rate scheduler**: Reduce on plateau which monitors validation loss with patience of 2 and decay rate of 0.1
- **Batch size**: 64
- **Early stopping**: Monitors validation accuracy with patience of 10

### Results

The image below is the result of the training process both on the training and validation set:

![Training results](https://drive.google.com/uc?export=view&id=1cf1gPGiP9ItoFDGcyrXxC7OHdxTYkYlM)