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tensorflow-yolo-v3

Implementation of YOLO v3 object detector in Tensorflow (TF-Slim). Full tutorial can be found here.

Tested on Python 3.5, Tensorflow 1.11.0 on Ubuntu 16.04.

Todo list:

• YOLO v3 architecture
• Basic working demo
• Weights converter (util for exporting loaded COCO weights as TF checkpoint)
• Training pipeline
• More backends

How to run the demo:

To run demo type this in the command line:

1. Download COCO class names file: wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
2. Download and convert model weights:
   1. Download binary file with desired weights:
      1. Full weights: wget https://pjreddie.com/media/files/yolov3.weights
      2. Tiny weights: wget https://pjreddie.com/media/files/yolov3-tiny.weights
      3. SPP weights: wget https://pjreddie.com/media/files/yolov3-spp.weights
   2. Run python ./convert_weights.py and python ./convert_weights_pb.py
3. Run python ./demo.py --input_img <path-to-image> --output_img <name-of-output-image> --frozen_model <path-to-frozen-model>

####Optional Flags

1. convert_weights:
   1. --class_names
      1. Path to the class names file
   2. --weights_file
      1. Path to the desired weights file
   3. --data_format
      1. NCHW (gpu only) or NHWC
   4. --tiny
      1. Use yolov3-tiny
   5. --spp
      1. Use yolov3-spp
   6. --ckpt_file
      1. Output checkpoint file
2. convert_weights_pb.py:
   1. --class_names 1. Path to the class names file
   2. --weights_file
      1. Path to the desired weights file
   3. --data_format
      1. NCHW (gpu only) or NHWC
   4. --tiny
      1. Use yolov3-tiny
   5. --spp
      1. Use yolov3-spp
   6. --output_graph
      1. Location to write the output .pb graph to
3. demo.py
   1. --class_names
      1. Path to the class names file
   2. --weights_file
      1. Path to the desired weights file
   3. --data_format
      1. NCHW (gpu only) or NHWC
   4. --ckpt_file
      1. Path to the checkpoint file
   5. --frozen_model
      1. Path to the frozen model
   6. --conf_threshold
      1. Desired confidence threshold
   7. --iou_threshold
      1. Desired iou threshold
   8. --gpu_memory_fraction
      1. Fraction of gpu memory to work with