dennisjooo
/

Birds-Classifier-EfficientNetB2

@@ -1,135 +1,19 @@
----
-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 "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!
-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)

+This directory includes a few sample datasets to get you started.
+*   `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
+*   `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/
+*   `anscombe.json` contains a copy of
+    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
+    was originally described in
+    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
+    Statistician. 27 (1): 17-21. JSTOR 2682899.
+    and our copy was prepared by the
+    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).