→ We learn them. They are regular weights of the network (use backpropagation). How do we know how many filters in each layer? → Hyperparameter of the network (try and see what works best). Source: https://cs231n.github.io/understanding-cnn/
the Jingdong Zhongmei private hospital in Yanjiao, China's Hebei Province (AP Photo/Andy Wong) Hsieh et al., “Drone-based Object Counting by Spatially Regularized Regional Proposal Networks”, ICCV 2017. Source: Pinterest
stage prediction of object classes and bounding boxes. Examples: • You Only Look Once (YOLO, YOLOv2, YOLOv3) • Single Shot MultiBox Detector (SSD) Two stages: 1. Generate candidate locations using some algorithm. 2. Adjustment of bounding boxes and classification. Examples: • R-CNN, Fast R-CNN, Faster R-CNN
• Use it to propose interesting regions worth exploring. Associate an objectness score to them. • Classify regions. Discard those that are background (ie. keep good scores only) Learn how to further adjust for each class of object.
2. Define fixed-size reference box (called anchor). 3. Find “closest” GT box. 4. Predict the “objectness” of the region. 5. Learn how to modify the anchor (in relative terms, ie. “double its width”). 6. Repeat for every spatial position.
to get feature map. 2. Run feature map through RPN convolutional layers (3x3, 1x1 & 1x1) a. Obtain objectness and box regression scores for each anchor type and spatial position. b. Use regression scores to adjust each anchor. 3. Sort proposals by objectness score. 4. Apply NMS to remove redundant proposals. Result Set of proposals with associated objectness scores
crucial implementation details, such as shapes and types. • Comments have hints to help you. ◦ We can help you too, don’t be shy and ask! :D Priorities: 1. Make it work (whatever it takes!). 2. Implement it with vectorized numpy. 3. Implement it in pure TensorFlow. a. Can compile and run in GPU. b. You would have to do this for a real implementation.
--help Usage: lumi [OPTIONS] COMMAND [ARGS]... Options: -h, --help Show this message and exit. Commands: checkpoint Groups of commands to manage checkpoints cloud Groups of commands to train models in the cloud dataset Groups of commands to manage datasets eval Evaluate trained (or training) models predict Obtain a model's predictions server Groups of commands to serve models train Train models
# Create tfrecords for optimizing data consumption. $ lumi train --config pascal-fasterrcnn.yml # Hours of training... $ tensorboard --logdir jobs/ # On another GPU/Machine/CPU. $ lumi eval --config pascal-fasterrcnn.yml # Checks for new checkpoints and writes logs. # Finally. $ lumi server web --config pascal-fasterrcnn.yml # Looks for checkpoint and loads it into a simple frontend/json API server. Building a toolkit