Botched the README

README.md

@@ -1,19 +1,135 @@
-This directory includes a few sample datasets to get you started.
+---
+license: apache-2.0
+base_model: google/efficientnet-b2
+metrics:
+- accuracy
+pipeline_tag: image-classification
+tags:
+- biology
+- efficientnet-b2
+- image-classification
+- vision
+---
 
-*   `california_housing_data*.csv` is California housing data from the 1990 US
-    Census; more information is available at:
-    https://developers.google.com/machine-learning/crash-course/california-housing-data-description
+# Bird Classifier EfficientNet-B2 
 
-*   `mnist_*.csv` is a small sample of the
-    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
-    described at: http://yann.lecun.com/exdb/mnist/
+## Model Description
 
-*   `anscombe.json` contains a copy of
-    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
-    was originally described in
+Have you look at a bird and said "Woahh 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!
 
-    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
-    Statistician. 27 (1): 17-21. JSTOR 2682899.
+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. 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 = '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
+- **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)
 
-    and our copy was prepared by the
-    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).