|
--- |
|
license: apache-2.0 |
|
base_model: google/efficientnet-b2 |
|
metrics: |
|
- accuracy |
|
pipeline_tag: image-classification |
|
tags: |
|
- biology |
|
- efficientnet-b2 |
|
- image-classification |
|
- vision |
|
--- |
|
|
|
# Bird Classifier EfficientNet-B2 |
|
|
|
## 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. |
|
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 = '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]) |
|
``` |
|
|
|
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 = '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']) |
|
``` |
|
|
|
## 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: |
|
|
|
 |
|
|
|
|
