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All Projects → mystic123 → Tensorflow Yolo V3

mystic123 / Tensorflow Yolo V3

Licence: apache-2.0
Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)

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python
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deep-learningtensorflowobject-detectionyoloyolov3detector

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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:

  • [x] YOLO v3 architecture
  • [x] Basic working demo
  • [x] 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
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