GoogLeNet for Image Classification

Requirements

Implementation Details

For testing the pre-trained model

For training from scratch on CIFAR-10

Usage

ImageNet Classification

Preparation

Run

Go to examples/ and put test image in folder DATA_PATH, then run the script:

python inception_pretrained.py --im_name PART_OF_IMAGE_NAME

Train the network on CIFAR-10

Preparation

python inception_cifar.py --train \
  --lr LEARNING_RATE \
  --bsize BATCH_SIZE \
  --keep_prob KEEP_PROB_OF_DROPOUT \
  --maxepoch MAX_TRAINING_EPOCH

Evaluate the model

Go to examples/ and put the pre-trained model in SAVE_PATH. Then run the script:

python inception_cifar.py --eval \
  --load PRE_TRAINED_MODEL_ID

Results

Image classification using pre-trained model

Data Source Image Result
COCO 1: probability: 1.00, label: brown bear, bruin, Ursus arctos
2: probability: 0.00, label: ice bear, polar bear
3: probability: 0.00, label: hyena, hyaena
4: probability: 0.00, label: chow, chow chow
5: probability: 0.00, label: American black bear, black bear
COCO 1: probability: 0.79, label: street sign
2: probability: 0.06, label: traffic light, traffic signal, stoplight
3: probability: 0.03, label: parking meter
4: probability: 0.02, label: mailbox, letter box
5: probability: 0.01, label: balloon
COCO 1: probability: 0.94, label: trolleybus, trolley coach
2: probability: 0.05, label: passenger car, coach, carriage
3: probability: 0.00, label: fire engine, fire truck
4: probability: 0.00, label: streetcar, tram, tramcar, trolley
5: probability: 0.00, label: minibus
COCO 1: probability: 0.35, label: burrito
2: probability: 0.17, label: potpie
3: probability: 0.14, label: mashed potato
4: probability: 0.10, label: plate
5: probability: 0.03, label: pizza, pizza pie
ImageNet 1: probability: 1.00, label: goldfish, Carassius auratus
2: probability: 0.00, label: rock beauty, Holocanthus tricolor
3: probability: 0.00, label: puffer, pufferfish, blowfish, globefish
4: probability: 0.00, label: tench, Tinca tinca
5: probability: 0.00, label: anemone fish
Self Collection 1: probability: 0.32, label: Egyptian cat
2: probability: 0.30, label: tabby, tabby cat
3: probability: 0.05, label: tiger cat
4: probability: 0.02, label: mouse, computer mouse
5: probability: 0.02, label: paper towel
Self Collection 1: probability: 1.00, label: streetcar, tram, tramcar, trolley, trolley car
2: probability: 0.00, label: passenger car, coach, carriage
3: probability: 0.00, label: trolleybus, trolley coach, trackless trolley
4: probability: 0.00, label: electric locomotive
5: probability: 0.00, label: freight car

Train the network from scratch on CIFAR-10

learning curve for training set

train_lc

learning curve for testing set

valid_lc

Author

Qian Ge