Create README.md

README.md

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+---
+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)
+