Feature-Extraction-using-AlexNet

A practice of feature extraction using AlexNet.

Sample Outputs

ImageNet Inference

• Run command to see AlexNet (pretrained on ImageNet) out on two sample images of Poodle and Weasle.

  python imagenet_inference.py
  
  

• You should see output like this

   Image 0
   miniature poodle: 0.389
   toy poodle: 0.223
   Bedlington terrier: 0.173
   standard poodle: 0.150
   komondor: 0.026
   
   Image 1
   weasel: 0.331
   polecat, fitch, foulmart, foumart, Mustela putorius: 0.280
   black-footed ferret, ferret, Mustela nigripes: 0.210
   mink: 0.081
   Arctic fox, white fox, Alopex lagopus: 0.027
   
   Time: 0.117 seconds
  

Traffic Sign Inference on AlexNet pretrained on ImageNet

• Run command to see AlexNet (pretrained on ImageNet) out on two sample images of traffic sign stop and traffic sign construction.

  python traffic_sign_inference.py
  
  

• You should see output like this. Note your output will not math this exactly as your initial weights (probably random) will be different.

   Image 0
   screen, CRT screen: 0.051
   digital clock: 0.041
   laptop, laptop computer: 0.030
   balance beam, beam: 0.027
   parallel bars, bars: 0.023
   
   Image 1
   digital watch: 0.395
   digital clock: 0.275
   bottlecap: 0.115
   stopwatch, stop watch: 0.104
   combination lock: 0.086
   
   Time: 0.127 seconds
  

Traffic Sign Inference with AlexNet feature extraction

• Run command to see inference with AlexNet feature extraction on two sample images of traffic sign stop and traffic sign construction.

  python feature_extraction.py
  
  

• You should see output like this. Note your output will not math this exactly as your initial weights (probably random) will be different.

   Image 0
   Dangerous curve to the left: 1.000
   End of no passing: 0.000
   End of speed limit (80km/h): 0.000
   Right-of-way at the next intersection: 0.000
   Speed limit (60km/h): 0.000
   
   Image 1
   Go straight or left: 1.000
   End of no passing: 0.000
   Turn right ahead: 0.000
   Priority road: 0.000
   Traffic signals: 0.000
   
   Time: 0.093 seconds
  

Train AlexNet with Feature Extraction on Traffic Sign Data

Here the last fully connected classification layer of AlexNet is replaced with classification layer for traffic signs. Run following command to start training.

python train_feature_extraction.py

Training AlexNet (even just the final layer!) can take a little while, so if you don't have a GPU, running on a subset of the data is a good alternative. As a point of reference one epoch over the training set takes roughly 53-55 seconds with a GTX 970.