Fish Classification CNN Model

Recognise fish (or other sea animals) by uploading a photo to an app or live feed fish underwater on your phone (in a case). The app will come later, for now, I will develop a dataset and train a model. I am looking for 95% accuracy on the validation test set for this project.

Prepare your Fish Classification Dataset.

The first thing to do is to locate datasets and organise them. This will also give me an idea of the fish I will be classifying.

Datasets I have found are;

1. From QUT – https://wiki.qut.edu.au/display/cyphy/Fish+Dataset
2. From Kaggle competition – https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring/data
3. Fish Recognition ground truth Data – http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/RECOG/
4. Google Images 😉 – https://google.com

That should do for now.

I downloaded and extracted all the datasets, each with its own classification methods. Let’s take a look at these datasets.

1. 4415 Images / 468 Species – Cropped image set 4415.
2. 3778 Images / 6 Species (+ Unknown & No Fish) – Unlabeled test images 13153.
3. 27370 Images / 23 Species – Image masks for all images 27370.

So some are cropped and ready, others are in a boat or harder to recognise images, and others have image masks. I need to create the classes, or species of fish and put them in named folders. Let’s see what we already have classified and make folders for everything ready to .zip.

Removing all of the unwanted images gives me a total of 30,207 images in 28 Classes. Some will have more than others (the last five classes contain fewer images), but it is a good start.

I will make a Data Set with 8 categories and 200 images per category for starters. All the spare images can go into an unfiltered folder for use to make a bigger dataset later. FOr not the 200 images will be augmented to get me 1000 for each category.

Build some Fish Classification CNN Models.

I built three models. The code divides so the same format can be used for datasets, being a .zip of (main_folder / classes / 1.jpg etc).

001

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 100, 100, 16)      448       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 50, 50, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 50, 50, 32)        4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 25, 25, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 25, 25, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 9216)              0         
_________________________________________________________________
dropout (Dropout)            (None, 9216)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               4719104   
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 9)                 4617      
=================================================================
Total params: 4,747,305
Trainable params: 4,747,305
Non-trainable params: 0
_________________________________________________________________

002

This used the same Model as above with the addition of image augmentation.

003

This model uses the above model with a MobileNET V2 layer that is not trainable.

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer (KerasLayer)     (None, 1280)              2257984   
_________________________________________________________________
dense (Dense)                (None, 8)                 10248     
=================================================================
Total params: 2,268,232
Trainable params: 10,248
Non-trainable params: 2,257,984
_________________________________________________________________

004

NOTE: I have made a fourth model using ResNet that outperformed all the above models. I also configured my GPU to speed up training over 200 epochs. I think it was around 97% training and 94% validation. You can check it out, It is on GitHub. I have started working on the saved model and exporting it to TensorFlow lite.

Conclusion and improvements for future Fish Classification CNN Models.

I am sure a convolutional neural network is a good start, but let’s take a look at some papers and examples of fish classification using deep learning.

• http://www.animalsworlds.com/classification-of-fish.html
• RCNN – Region-Based CNN. This will help with images that have extra elements.
• Fish Classification with CNN – https://arxiv.org/ftp/arxiv/papers/1805/1805.10106.pdf
• Contour Mapping Fish – http://www.timeseriesclassification.com/description.php?Dataset=Fish
• Image Location – The location of the image as a feature will help classify possible species, but it should have too much weight as incorrect locations could throw things out.
• Fish Classification from Video – https://www.researchgate.net/publication/317558591_Automatic_fish_species_classification_in_underwater_videos_Exploiting_pretrained_deep_neural_network_models_to_compensate_for_limited_labelled_data

Now I have an idea of what approaches to take for future model training. The next instalment of this project will take the above into account.