Birds Classifier EfficientNetB2

🚀 Bird Classifier EfficientNet-B2

This model is a fine - tuned version of Google's EfficientNet - B2, designed for bird species image classification, achieving high accuracy on a dataset of 525 bird species.

🚀 Quick Start

You can use the raw model for image classification. Here are two ways to use the model with code examples:

💻 Usage Examples

Basic Usage

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

# Determining the file URL
url = 'some url'

# 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])

Advanced Usage

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

# Determining the file URL
url = 'some url'

# 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'])

✨ Features

• High - accuracy Classification: The model performs significantly well on all three sets (training, validation, and test), with accuracies of 0.999480, 0.985904, and 0.991238 respectively.
• Based on a Popular Model: It is a fine - tuned version of [google/efficientnet - b2](https://huggingface.co/google/efficientnet - b2), which was originally trained on ImageNet - 1K.

📦 Installation

The README does not provide specific installation steps, so this section is skipped.

📚 Documentation

Model Description

Have you look at a bird and said "Boahh if only I know what bird that is". Unless you're an avid bird spotter (or just love birds in general), it's hard to differentiate some species of birds. Well you're in luck, turns out you can use a image classifier to identify bird species!

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.

The original model itself was trained on ImageNet - 1K, thus it might still have some useful features for identifying creatures like birds.

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.

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
• Loss: Cross entropy
• Optimizer: Adam with default betas (0.99, 0.999)
• 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:


🔧 Technical Details

• Model Type: Fine - tuned EfficientNet - B2
• Training Data: [gpiosenka/100 - bird - species](https://www.kaggle.com/datasets/gpiosenka/100 - bird - species) dataset on Kaggle

📄 License

The model is licensed under the Apache - 2.0 license.