| --- |
| 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: |
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