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add the densenet yolov3 		4 years ago
.gitignore Add weights converting script. 5 years ago
CODE_OF_CONDUCT.md Create CODE_OF_CONDUCT.md 5 years ago
LICENSE Create LICENSE 5 years ago
README.md Update README.md 4 years ago
batch_detect.py detecting scrips 4 years ago
convert_weights.py Add yolov3-spp support 4 years ago
convert_weights_pb.py add the densenet yolov3 4 years ago
demo.py Add yolov3-spp support 4 years ago
dense_yolov3_v1.py add the densenet yolov3 4 years ago
detect.py detecting scrips 4 years ago
get_video_frames.py Add files via upload 4 years ago
utils.py Implemented 'resize' and 'draw bbox' with cv2. 4 years ago
video_detect.py detecting scrips 4 years ago
yolo_v3.py Add some densenet element to construct dense yolo 4 years ago
yolo_v3_tiny.py Fix conflicts 5 years ago
yolov3-dense.cfg add the densenet yolov3 4 years ago
yolov3_tiny_3l.py yolov3 tiny, 9 anchors. 4 years ago

README.md

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:

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
    9. '--keep_aspect_ratio'
      1. keep the images' aspect ratio while resizing.
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