NotJackFruit-Classifier

Do you watch HBO's Silicon Valley? Because I do and I was inspired by Mr. Jian-Yang to make my own not hotdog classifier. But this is different, its the Indian version, not jackfruit?

"What would you say if I told you there is a app on the market that tell you if you have a jackfruit or not a jackfruit." - Sasi

Step 1: Setting up

Usage

Step #1 : Install virtualenv

pip install virtualenv

Step #2 : Create a virtualenv

virtualenv -p python3 vir

Step #3: Activate Virtualenv

source ./vir/bin/activate

Step #4 : Clone Repo

• Clone the Repo. (cd into the dir after extracing the .zip)

Step #5 : Install requirements

• Install the Requirements.

pip install -r requirements.txt

If you just need to use the model without training, I have added a pretrained model. Go to step 7.

Step 2: Collecting data

The very first step in making a classifier is to collect data. Thus we need to find images of jackfruit and not-jackfruit.

To do this I just used ImageNet to search for my images, since ImageNet is like a database of images. To find the hotdog images I just searched for “jackfruit”, after downloading all of the images it would me about around 1000 jackfruit images. For the not-jackfruit images I searched for “food”, “furniture”, “people” and “pets” this give me about 4024 not-jackfruit images.

Now to actually download these images I need to get the URLs, to do that I just need to click on the tab download tab click on link called URLs

then copy the URL of the page your on which will use in a script that we'll write to download all of these images.

Next we need to write our scripts to download and save all of these images, here is my python code for saving the images. The function store_raw_images takes in a list of folders names where you want to save the images to from each of the links.

def store_raw_images(folders, links):
    pic_num = 1
    for link, folder in zip(links, folders):
        if not os.path.exists(folder):
            os.makedirs(folder)
        image_urls = str(urllib.request.urlopen(link).read())
        
        
        for i in image_urls.split('\\n'):
            try:                
                urllib.request.urlretrieve(i, folder+"/"+str(pic_num)+".jpg")
                img = cv2.imread(folder+"/"+str(pic_num)+".jpg")                         
                
                # Do preprocessing if you want
                if img is not None:
                    // do more stuff here if you want
                    cv2.imwrite(folder+"/"+str(pic_num)+".jpg",img)
                    pic_num += 1

            except Exception as e:
                    print(str(e))  

Next I have my main method to drive the code

def main():

    links = [ 
    
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n01318894', \
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n03405725', \
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n07942152', \
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n00021265', \
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n07697537', \
        'http://image-net.org/api/text/imagenet.synset.geturls?wnid=n12400720'
       ]

       paths = ['pets', 'furniture', 'people', 'food',  'hotdog','jackfruit']

    
    
    store_raw_images(paths, links)

You can also use shell script:

Save all the liks to jack.txt and the script to download it

awk '{print "" $0;}' jack.txt | xargs -l1 wget

Delete all smaller files, unavailable images and junk files

find . -name "*.jpg" -size -10k -delete

Now just wait for it to download all of those jackfruits!!!

Step 3: Cleaning the data

At this point we have collected our data now we just need to clean it up a bit. If you take a look at the data you will probably notice that there are some garbage images that we need to remove, images that look like one of the following

To do this let write some more scripts to do this work for us, first we just need to get a copy of the images that we want to remove and place them in a folder called ‘invalid’.

def removeInvalid(dirPaths):
    for dirPath in dirPaths:
        for img in os.listdir(dirPath):
            for invalid in os.listdir('invalid'):
                try:
                    current_image_path = str(dirPath)+'/'+str(img)
                    invalid = cv2.imread('invalid/'+str(invalid))
                    question = cv2.imread(current_image_path)
                    if invalid.shape == question.shape and not(np.bitwise_xor(invalid,question).any()):
                        os.remove(current_image_path)
                        break

                except Exception as e:
                    print(str(e))

Next I made two folders called "jackfruit" and "not-jackfruit" and placed the 'food', 'furniture', 'pets', 'people' folders in the "not-jackfruit" foldder.

Step 4: Choosing the model.

The retrain script can retrain either Inception V3 model or a MobileNet. In this exercise, we will use a MobileNet. The principal difference is that Inception V3 is optimized for accuracy, while the MobileNets are optimized to be small and efficient, at the cost of some accuracy.

Inception V3 has a first-choice accuracy of 78% on ImageNet, but is the model is 85MB, and requires many times more processing than even the largest MobileNet configuration, which achieves 70.5% accuracy, with just a 19MB download.

Pick the following configuration options:

Input image resolution: 128,160,192, or 224px. Unsurprisingly, feeding in a higher resolution image takes more processing time, but results in better classification accuracy. We recommend 224 as an initial setting. The relative size of the model as a fraction of the largest MobileNet: 1.0, 0.75, 0.50, or 0.25. We recommend 0.5 as an initial setting. The smaller models run significantly faster, at a cost of accuracy. With the recommended settings, it typically takes only a couple of minutes to retrain on a laptop. You will pass the settings inside Linux shell variables. Set those shell variables as follows:

IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

Other models: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md

To start Tensorboard

tensorboard --logdir tf_files/training_summaries &

Step 5: Train The Neural Net

The model we choose is a mobileNet

python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=500 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --architecture="${ARCHITECTURE}" \
  --image_dir=images/

The first retraining command iterates only 500 times. You can very likely get improved results (i.e. higher accuracy) by training for longer. To get this improvement, remove the parameter --how_many_training_steps to use the default 4,000 iterations.

Step 6: Using the retrained model

The retraining script writes data to the following two files:

tf_files/retrained_graph.pb, which contains a version of the selected network with a final layer retrained on your categories. tf_files/retrained_labels.txt, which is a text file containing labels

To train the network we split our data into a tranining set and a test set

Step 7: The Results

I have added a pretrined model, just use the below code without training. Dont forget to set the architecture from step 4.

python -m scripts.label_image \
    --graph=tf_files/retrained_graph.pb  \
    --image=images/test.jpg

We are getting good accuracy, All the best with JackFruit Hunting.

License:

MIT

To-Do

• Create a Flask app to read the image and print result.