Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Related tags

Deep Learning tensorflow object-detection object-tracking object-tracker deep-sort yolov4 yolov4-deepsort
Overview

yolov4-deepsort

license Open In Colab

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the output of YOLOv4 feed these object detections into Deep SORT (Simple Online and Realtime Tracking with a Deep Association Metric) in order to create a highly accurate object tracker.

Demo of Object Tracker on Persons

Demo of Object Tracker on Cars

Getting Started

To get started, install the proper dependencies either via Anaconda or Pip. I recommend Anaconda route for people using a GPU as it configures CUDA toolkit version for you.

Conda (Recommended)

# Tensorflow CPU
conda env create -f conda-cpu.yml
conda activate yolov4-cpu

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov4-gpu

Pip

(TensorFlow 2 packages require a pip version >19.0.)

# TensorFlow CPU
pip install -r requirements.txt

# TensorFlow GPU
pip install -r requirements-gpu.txt

Nvidia Driver (For GPU, if you are not using Conda Environment and haven't set up CUDA yet)

Make sure to use CUDA Toolkit version 10.1 as it is the proper version for the TensorFlow version used in this repository. https://developer.nvidia.com/cuda-10.1-download-archive-update2

Downloading Official YOLOv4 Pre-trained Weights

Our object tracker uses YOLOv4 to make the object detections, which deep sort then uses to track. There exists an official pre-trained YOLOv4 object detector model that is able to detect 80 classes. For easy demo purposes we will use the pre-trained weights for our tracker. Download pre-trained yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Copy and paste yolov4.weights from your downloads folder into the 'data' folder of this repository.

If you want to use yolov4-tiny.weights, a smaller model that is faster at running detections but less accurate, download file here: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights

Running the Tracker with YOLOv4

To implement the object tracking using YOLOv4, first we convert the .weights into the corresponding TensorFlow model which will be saved to a checkpoints folder. Then all we need to do is run the object_tracker.py script to run our object tracker with YOLOv4, DeepSort and TensorFlow.

# Convert darknet weights to tensorflow model
python save_model.py --model yolov4 

# Run yolov4 deep sort object tracker on video
python object_tracker.py --video ./data/video/test.mp4 --output ./outputs/demo.avi --model yolov4

# Run yolov4 deep sort object tracker on webcam (set video flag to 0)
python object_tracker.py --video 0 --output ./outputs/webcam.avi --model yolov4

The output flag allows you to save the resulting video of the object tracker running so that you can view it again later. Video will be saved to the path that you set. (outputs folder is where it will be if you run the above command!)

If you want to run yolov3 set the model flag to --model yolov3, upload the yolov3.weights to the 'data' folder and adjust the weights flag in above commands. (see all the available command line flags and descriptions of them in a below section)

Running the Tracker with YOLOv4-Tiny

The following commands will allow you to run yolov4-tiny model. Yolov4-tiny allows you to obtain a higher speed (FPS) for the tracker at a slight cost to accuracy. Make sure that you have downloaded the tiny weights file and added it to the 'data' folder in order for commands to work!

# save yolov4-tiny model
python save_model.py --weights ./data/yolov4-tiny.weights --output ./checkpoints/yolov4-tiny-416 --model yolov4 --tiny

# Run yolov4-tiny object tracker
python object_tracker.py --weights ./checkpoints/yolov4-tiny-416 --model yolov4 --video ./data/video/test.mp4 --output ./outputs/tiny.avi --tiny

Resulting Video

As mentioned above, the resulting video will save to wherever you set the --output command line flag path to. I always set it to save to the 'outputs' folder. You can also change the type of video saved by adjusting the --output_format flag, by default it is set to AVI codec which is XVID.

Example video showing tracking of all coco dataset classes:

Filter Classes that are Tracked by Object Tracker

By default the code is setup to track all 80 or so classes from the coco dataset, which is what the pre-trained YOLOv4 model is trained on. However, you can easily adjust a few lines of code in order to track any 1 or combination of the 80 classes. It is super easy to filter only the person class or only the car class which are most common.

To filter a custom selection of classes all you need to do is comment out line 159 and uncomment out line 162 of object_tracker.py Within the list allowed_classes just add whichever classes you want the tracker to track. The classes can be any of the 80 that the model is trained on, see which classes you can track in the file data/classes/coco.names

This example would allow the classes for person and car to be tracked.

Demo of Object Tracker set to only track the class 'person'

Demo of Object Tracker set to only track the class 'car'

Command Line Args Reference

save_model.py:
  --weights: path to weights file
    (default: './data/yolov4.weights')
  --output: path to output
    (default: './checkpoints/yolov4-416')
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'False')
  --input_size: define input size of export model
    (default: 416)
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)
    
 object_tracker.py:
  --video: path to input video (use 0 for webcam)
    (default: './data/video/test.mp4')
  --output: path to output video (remember to set right codec for given format. e.g. XVID for .avi)
    (default: None)
  --output_format: codec used in VideoWriter when saving video to file
    (default: 'XVID)
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './checkpoints/yolov4-416')
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)
  --size: resize images to
    (default: 416)
  --iou: iou threshold
    (default: 0.45)
  --score: confidence threshold
    (default: 0.50)
  --dont_show: dont show video output
    (default: False)
  --info: print detailed info about tracked objects
    (default: False)

References

Huge shoutout goes to hunglc007 and nwojke for creating the backbones of this repository:

Comments
  • Compiled the loaded model, but the compiled metrics have yet to be built

    Compiled the loaded model, but the compiled metrics have yet to be built

    WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. model.compile_metrics will be empty until you train or evaluate the model. W0621 18:01:15.284377 140678384588672 saving_utils.py:319]

    opened by tuanndhust 8
  • cannot connect to X server

    cannot connect to X server

    Hello, when I run "!python object_tracker.py --video ./data/video/cars.mp4 --output ./outputs/tracker.avi --model yolov4" on colab, it prompts "cannot connect to X server", what is the reason and how to solve it? Looking forward to your reply, thank you! image`

    opened by 6Lackiu 5
  • Can this code run on jetson nano-2gb

    Can this code run on jetson nano-2gb

    Well, its a straight up question..It's been days I've been trying to make this code work on nvivdia jetson-nano 2gb and due to some tensorflow issue..its not working..

    So it would be helpful if anyone can straight up tell if that's the case

    opened by xanthan011 5
  • How to get bounding box coordinates and person's ID?

    How to get bounding box coordinates and person's ID?

    Hi, I'm new to Deep sort. It's interesting. How can I get the bounding box coordinates of all objects and person's ID? I would need to cut photo to that object. So for instance, for each person, it would be 4 values for the rectangle: (top left x,top left y,width,height) and person-ID.

    opened by kazuki-can 3
  • yolov4-tiny not working in test.mp4

    yolov4-tiny not working in test.mp4

    I following the readme.md step to run all the file and no any error. when I using yolov4 model and run object_tracker.py it is work. but yolov4-tiny model got nothing bbox.

    opened by alanotmt 2
  • Video has ended or failed, try a different video format!

    Video has ended or failed, try a different video format!

    Each time I run the script, it shows a message at the end of the run cycle "Video has ended or failed, try a different video format!"

    The output file has no object recognition boxes on it, it's just a clone of the input video.

    Has anyone else encountered the issue? Any clues how to go about solving this problem?

    I'm running it on a venv as specified in the readme

    opened by realtechspecs 2
  • Resize image

    Resize image

    Hello, I have a problem with resizing images. Default size is 416 but when I try to set different size it gives me an error: image

    YOLO accept ssizes that are N * 32, am I correct? So for example size 606 should work.

    opened by Arhanel 2
  • Unable to run YOLOv4 or YOLOv4-tiny with batch size > 1

    Unable to run YOLOv4 or YOLOv4-tiny with batch size > 1

    Hey,

    I have been successfully using the YOLOv4 and YOLOv4-tiny for inference with batch size 1 with an input tensor shape of [1, 416, 416, 3], however, if I try to run the model with batch size > 1, I always encounter the following error:

    tensorflow.python.framework.errors_impl.InvalidArgumentError:  Input to reshape is a tensor with 4 values, but the requested shape requires a multiple of 8
         [[node functional_1/tf_op_layer_Reshape_10/Reshape_10 (defined at object_tracker.py:136) ]] [Op:__inference_predict_function_17447]

    This was received using batch size of 2 resulting in input tensor shape of [2, 416, 416, 3], can anyone point me in a right direction with respect to what I am doing wrong or is the model even supposed to work when attempting to run inference with batch size above one?

    Here is some of the code I am using for model loading and inference:

    model = tf.saved_model.load(self.model_weights_path, tags=[tag_constants.SERVING])
inference_function = model.signatures["serving_default"]
tf_batch_data = tf.constant(np.array(image_batch_data))
print(f"Image batch data: {tf.shape(tf_batch_data)}")
tf_pred_bbox = inference_function(tf_batch_data)

    Is there possibly something that I need to change e.g. under core/config.py to make the model work with different batch sizes? I am confused because the input layer according to keras model summary seems to have the batch dimension defined as None which would indicate that the model supports variable batch sizes:

    >>> model.summary()
Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 416, 416, 3) 0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 417, 417, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 208, 208, 32) 864         zero_padding2d[0][0]             
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 208, 208, 32) 128         conv2d[0][0]                     
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu (TensorFl (None, 208, 208, 32) 0           batch_normalization[0][0]        
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 209, 209, 32) 0           tf_op_layer_LeakyRelu[0][0]      
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 104, 104, 64) 18432       zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 104, 104, 64) 256         conv2d_1[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_1 (Tensor (None, 104, 104, 64) 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 104, 104, 64) 36864       tf_op_layer_LeakyRelu_1[0][0]    
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 104, 104, 64) 256         conv2d_2[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_2 (Tensor (None, 104, 104, 64) 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
tf_op_layer_split (TensorFlowOp [(None, 104, 104, 32 0           tf_op_layer_LeakyRelu_2[0][0]    
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_split[0][1]          
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 104, 104, 32) 128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_3 (Tensor (None, 104, 104, 32) 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_LeakyRelu_3[0][0]    
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 104, 104, 32) 128         conv2d_4[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_4 (Tensor (None, 104, 104, 32) 0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
tf_op_layer_concat (TensorFlowO (None, 104, 104, 64) 0           tf_op_layer_LeakyRelu_4[0][0]    
                                                                 tf_op_layer_LeakyRelu_3[0][0]    
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 104, 104, 64) 4096        tf_op_layer_concat[0][0]         
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 104, 104, 64) 256         conv2d_5[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_5 (Tensor (None, 104, 104, 64) 0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
tf_op_layer_concat_1 (TensorFlo (None, 104, 104, 128 0           tf_op_layer_LeakyRelu_2[0][0]    
                                                                 tf_op_layer_LeakyRelu_5[0][0]    
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 52, 52, 128)  0           tf_op_layer_concat_1[0][0]       
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 52, 52, 128)  147456      max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 52, 52, 128)  512         conv2d_6[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_6 (Tensor (None, 52, 52, 128)  0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
tf_op_layer_split_1 (TensorFlow [(None, 52, 52, 64), 0           tf_op_layer_LeakyRelu_6[0][0]    
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_split_1[0][1]        
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 52, 52, 64)   256         conv2d_7[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_7 (Tensor (None, 52, 52, 64)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_LeakyRelu_7[0][0]    
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 52, 52, 64)   256         conv2d_8[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_8 (Tensor (None, 52, 52, 64)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
tf_op_layer_concat_2 (TensorFlo (None, 52, 52, 128)  0           tf_op_layer_LeakyRelu_8[0][0]    
                                                                 tf_op_layer_LeakyRelu_7[0][0]    
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 52, 52, 128)  16384       tf_op_layer_concat_2[0][0]       
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 52, 52, 128)  512         conv2d_9[0][0]                   
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_9 (Tensor (None, 52, 52, 128)  0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
tf_op_layer_concat_3 (TensorFlo (None, 52, 52, 256)  0           tf_op_layer_LeakyRelu_6[0][0]    
                                                                 tf_op_layer_LeakyRelu_9[0][0]    
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 26, 26, 256)  0           tf_op_layer_concat_3[0][0]       
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 26, 26, 256)  589824      max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 26, 26, 256)  1024        conv2d_10[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_10 (Tenso (None, 26, 26, 256)  0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
tf_op_layer_split_2 (TensorFlow [(None, 26, 26, 128) 0           tf_op_layer_LeakyRelu_10[0][0]   
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_split_2[0][1]        
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 26, 26, 128)  512         conv2d_11[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_11 (Tenso (None, 26, 26, 128)  0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_LeakyRelu_11[0][0]   
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 128)  512         conv2d_12[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_12 (Tenso (None, 26, 26, 128)  0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
tf_op_layer_concat_4 (TensorFlo (None, 26, 26, 256)  0           tf_op_layer_LeakyRelu_12[0][0]   
                                                                 tf_op_layer_LeakyRelu_11[0][0]   
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 26, 26, 256)  65536       tf_op_layer_concat_4[0][0]       
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 256)  1024        conv2d_13[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_13 (Tenso (None, 26, 26, 256)  0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
tf_op_layer_concat_5 (TensorFlo (None, 26, 26, 512)  0           tf_op_layer_LeakyRelu_10[0][0]   
                                                                 tf_op_layer_LeakyRelu_13[0][0]   
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 13, 13, 512)  0           tf_op_layer_concat_5[0][0]       
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 13, 13, 512)  2359296     max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 13, 13, 512)  2048        conv2d_14[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_14 (Tenso (None, 13, 13, 512)  0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 13, 13, 256)  131072      tf_op_layer_LeakyRelu_14[0][0]   
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 13, 13, 256)  1024        conv2d_15[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_15 (Tenso (None, 13, 13, 256)  0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 13, 13, 128)  32768       tf_op_layer_LeakyRelu_15[0][0]   
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 13, 13, 128)  512         conv2d_18[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_17 (Tenso (None, 13, 13, 128)  0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
tf_op_layer_ResizeBilinear (Ten (None, 26, 26, 128)  0           tf_op_layer_LeakyRelu_17[0][0]   
__________________________________________________________________________________________________
tf_op_layer_concat_6 (TensorFlo (None, 26, 26, 384)  0           tf_op_layer_ResizeBilinear[0][0] 
                                                                 tf_op_layer_LeakyRelu_13[0][0]   
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 26, 26, 256)  884736      tf_op_layer_concat_6[0][0]       
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 13, 13, 512)  1179648     tf_op_layer_LeakyRelu_15[0][0]   
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 26, 26, 256)  1024        conv2d_19[0][0]                  
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 13, 13, 512)  2048        conv2d_16[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_18 (Tenso (None, 26, 26, 256)  0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
tf_op_layer_LeakyRelu_16 (Tenso (None, 13, 13, 512)  0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 26, 26, 255)  65535       tf_op_layer_LeakyRelu_18[0][0]   
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 13, 13, 255)  130815      tf_op_layer_LeakyRelu_16[0][0]   
__________________________________________________________________________________________________
tf_op_layer_Shape (TensorFlowOp (4,)                 0           conv2d_20[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_Shape_1 (TensorFlow (4,)                 0           conv2d_17[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_strided_slice (Tens ()                   0           tf_op_layer_Shape[0][0]          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1 (Te ()                   0           tf_op_layer_Shape_1[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Reshape/shape (Tens (5,)                 0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Reshape_3/shape (Te (5,)                 0           tf_op_layer_strided_slice_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape (TensorFlow (None, 26, 26, 3, 85 0           conv2d_20[0][0]                  
                                                                 tf_op_layer_Reshape/shape[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Reshape_3 (TensorFl (None, 13, 13, 3, 85 0           conv2d_17[0][0]                  
                                                                 tf_op_layer_Reshape_3/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_split_3 (TensorFlow [(None, 26, 26, 3, 2 0           tf_op_layer_Reshape[0][0]        
__________________________________________________________________________________________________
tf_op_layer_split_4 (TensorFlow [(None, 13, 13, 3, 2 0           tf_op_layer_Reshape_3[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Sigmoid (TensorFlow (None, 26, 26, 3, 2) 0           tf_op_layer_split_3[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Tile/multiples (Ten (5,)                 0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Sigmoid_3 (TensorFl (None, 13, 13, 3, 2) 0           tf_op_layer_split_4[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Tile_1/multiples (T (5,)                 0           tf_op_layer_strided_slice_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul (TensorFlowOpLa (None, 26, 26, 3, 2) 0           tf_op_layer_Sigmoid[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Tile (TensorFlowOpL (None, 26, 26, 3, 2) 0           tf_op_layer_Tile/multiples[0][0] 
__________________________________________________________________________________________________
tf_op_layer_Mul_4 (TensorFlowOp (None, 13, 13, 3, 2) 0           tf_op_layer_Sigmoid_3[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Tile_1 (TensorFlowO (None, 13, 13, 3, 2) 0           tf_op_layer_Tile_1/multiples[0][0
__________________________________________________________________________________________________
tf_op_layer_Sub (TensorFlowOpLa (None, 26, 26, 3, 2) 0           tf_op_layer_Mul[0][0]            
__________________________________________________________________________________________________
tf_op_layer_Cast (TensorFlowOpL (None, 26, 26, 3, 2) 0           tf_op_layer_Tile[0][0]           
__________________________________________________________________________________________________
tf_op_layer_Sub_1 (TensorFlowOp (None, 13, 13, 3, 2) 0           tf_op_layer_Mul_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Cast_1 (TensorFlowO (None, 13, 13, 3, 2) 0           tf_op_layer_Tile_1[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2 (TensorFlowOp (None, 26, 26, 3, 2) 0           tf_op_layer_Sub[0][0]            
                                                                 tf_op_layer_Cast[0][0]           
__________________________________________________________________________________________________
tf_op_layer_Exp (TensorFlowOpLa (None, 26, 26, 3, 2) 0           tf_op_layer_split_3[0][1]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_1 (TensorFlow (None, 13, 13, 3, 2) 0           tf_op_layer_Sub_1[0][0]          
                                                                 tf_op_layer_Cast_1[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Exp_1 (TensorFlowOp (None, 13, 13, 3, 2) 0           tf_op_layer_split_4[0][1]        
__________________________________________________________________________________________________
tf_op_layer_Mul_1 (TensorFlowOp (None, 26, 26, 3, 2) 0           tf_op_layer_AddV2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Mul_2 (TensorFlowOp (None, 26, 26, 3, 2) 0           tf_op_layer_Exp[0][0]            
__________________________________________________________________________________________________
tf_op_layer_Mul_5 (TensorFlowOp (None, 13, 13, 3, 2) 0           tf_op_layer_AddV2_1[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_6 (TensorFlowOp (None, 13, 13, 3, 2) 0           tf_op_layer_Exp_1[0][0]          
__________________________________________________________________________________________________
tf_op_layer_concat_7 (TensorFlo (None, 26, 26, 3, 4) 0           tf_op_layer_Mul_1[0][0]          
                                                                 tf_op_layer_Mul_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Reshape_2/shape (Te (3,)                 0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_concat_8 (TensorFlo (None, 13, 13, 3, 4) 0           tf_op_layer_Mul_5[0][0]          
                                                                 tf_op_layer_Mul_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Reshape_5/shape (Te (3,)                 0           tf_op_layer_strided_slice_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sigmoid_1 (TensorFl (None, 26, 26, 3, 1) 0           tf_op_layer_split_3[0][2]        
__________________________________________________________________________________________________
tf_op_layer_Sigmoid_2 (TensorFl (None, 26, 26, 3, 80 0           tf_op_layer_split_3[0][3]        
__________________________________________________________________________________________________
tf_op_layer_Sigmoid_4 (TensorFl (None, 13, 13, 3, 1) 0           tf_op_layer_split_4[0][2]        
__________________________________________________________________________________________________
tf_op_layer_Sigmoid_5 (TensorFl (None, 13, 13, 3, 80 0           tf_op_layer_split_4[0][3]        
__________________________________________________________________________________________________
tf_op_layer_Reshape_2 (TensorFl (None, None, 4)      0           tf_op_layer_concat_7[0][0]       
                                                                 tf_op_layer_Reshape_2/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_5 (TensorFl (None, None, 4)      0           tf_op_layer_concat_8[0][0]       
                                                                 tf_op_layer_Reshape_5/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_3 (TensorFlowOp (None, 26, 26, 3, 80 0           tf_op_layer_Sigmoid_1[0][0]      
                                                                 tf_op_layer_Sigmoid_2[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_1/shape (Te (3,)                 0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Mul_7 (TensorFlowOp (None, 13, 13, 3, 80 0           tf_op_layer_Sigmoid_4[0][0]      
                                                                 tf_op_layer_Sigmoid_5[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_4/shape (Te (3,)                 0           tf_op_layer_strided_slice_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_concat_9 (TensorFlo (None, None, 4)      0           tf_op_layer_Reshape_2[0][0]      
                                                                 tf_op_layer_Reshape_5[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_1 (TensorFl (None, None, 80)     0           tf_op_layer_Mul_3[0][0]          
                                                                 tf_op_layer_Reshape_1/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_4 (TensorFl (None, None, 80)     0           tf_op_layer_Mul_7[0][0]          
                                                                 tf_op_layer_Reshape_4/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_2 (TensorFlow (3,)                 0           tf_op_layer_concat_9[0][0]       
__________________________________________________________________________________________________
tf_op_layer_concat_10 (TensorFl (None, None, 80)     0           tf_op_layer_Reshape_1[0][0]      
                                                                 tf_op_layer_Reshape_4[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_2 (Te (2,)                 0           tf_op_layer_Shape_2[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Max (TensorFlowOpLa (None, None)         0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Shape_3 (TensorFlow (3,)                 0           tf_op_layer_concat_9[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Prod (TensorFlowOpL ()                   0           tf_op_layer_strided_slice_2[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_4 (TensorFlow (3,)                 0           tf_op_layer_concat_9[0][0]       
__________________________________________________________________________________________________
tf_op_layer_GreaterEqual (Tenso (None, None)         0           tf_op_layer_Max[0][0]            
__________________________________________________________________________________________________
tf_op_layer_strided_slice_3 (Te (0,)                 0           tf_op_layer_Shape_3[0][0]        
__________________________________________________________________________________________________
tf_op_layer_concat_11/values_1  (1,)                 0           tf_op_layer_Prod[0][0]           
__________________________________________________________________________________________________
tf_op_layer_strided_slice_4 (Te (1,)                 0           tf_op_layer_Shape_4[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Reshape_7 (TensorFl (None,)              0           tf_op_layer_GreaterEqual[0][0]   
__________________________________________________________________________________________________
tf_op_layer_concat_11 (TensorFl (2,)                 0           tf_op_layer_strided_slice_3[0][0]
                                                                 tf_op_layer_concat_11/values_1[0]
                                                                 tf_op_layer_strided_slice_4[0][0]
__________________________________________________________________________________________________
tf_op_layer_Where (TensorFlowOp (None, 1)            0           tf_op_layer_Reshape_7[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_6 (TensorFl (None, 4)            0           tf_op_layer_concat_9[0][0]       
                                                                 tf_op_layer_concat_11[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Squeeze (TensorFlow (None,)              0           tf_op_layer_Where[0][0]          
__________________________________________________________________________________________________
tf_op_layer_GatherV2 (TensorFlo (None, 4)            0           tf_op_layer_Reshape_6[0][0]      
                                                                 tf_op_layer_Squeeze[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_8 (TensorFlow (3,)                 0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Shape_9 (TensorFlow (2,)                 0           tf_op_layer_GatherV2[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Shape_5 (TensorFlow (3,)                 0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_8 (Te ()                   0           tf_op_layer_Shape_8[0][0]        
__________________________________________________________________________________________________
tf_op_layer_strided_slice_9 (Te ()                   0           tf_op_layer_Shape_9[0][0]        
__________________________________________________________________________________________________
tf_op_layer_strided_slice_5 (Te (2,)                 0           tf_op_layer_Shape_5[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Reshape_10/shape (T (3,)                 0           tf_op_layer_strided_slice_8[0][0]
                                                                 tf_op_layer_strided_slice_9[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_6 (TensorFlow (3,)                 0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Prod_1 (TensorFlowO ()                   0           tf_op_layer_strided_slice_5[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_7 (TensorFlow (3,)                 0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_10 (TensorF (None, None, None)   0           tf_op_layer_GatherV2[0][0]       
                                                                 tf_op_layer_Reshape_10/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_strided_slice_6 (Te (0,)                 0           tf_op_layer_Shape_6[0][0]        
__________________________________________________________________________________________________
tf_op_layer_concat_12/values_1  (1,)                 0           tf_op_layer_Prod_1[0][0]         
__________________________________________________________________________________________________
tf_op_layer_strided_slice_7 (Te (1,)                 0           tf_op_layer_Shape_7[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Reshape_9 (TensorFl (None,)              0           tf_op_layer_GreaterEqual[0][0]   
__________________________________________________________________________________________________
tf_op_layer_split_5 (TensorFlow [(None, None, 2), (N 0           tf_op_layer_Reshape_10[0][0]     
__________________________________________________________________________________________________
tf_op_layer_concat_12 (TensorFl (2,)                 0           tf_op_layer_strided_slice_6[0][0]
                                                                 tf_op_layer_concat_12/values_1[0]
                                                                 tf_op_layer_strided_slice_7[0][0]
__________________________________________________________________________________________________
tf_op_layer_Where_1 (TensorFlow (None, 1)            0           tf_op_layer_Reshape_9[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_13 (T (None, None, 2)      0           tf_op_layer_split_5[0][1]        
__________________________________________________________________________________________________
tf_op_layer_Reshape_8 (TensorFl (None, 80)           0           tf_op_layer_concat_10[0][0]      
                                                                 tf_op_layer_concat_12[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Squeeze_1 (TensorFl (None,)              0           tf_op_layer_Where_1[0][0]        
__________________________________________________________________________________________________
tf_op_layer_strided_slice_12 (T (None, None, 2)      0           tf_op_layer_split_5[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv (TensorFlow (None, None, 2)      0           tf_op_layer_strided_slice_13[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_2 (TensorFl (None, None, 2)      0           tf_op_layer_strided_slice_13[0][0
__________________________________________________________________________________________________
tf_op_layer_GatherV2_1 (TensorF (None, 80)           0           tf_op_layer_Reshape_8[0][0]      
                                                                 tf_op_layer_Squeeze_1[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Sub_2 (TensorFlowOp (None, None, 2)      0           tf_op_layer_strided_slice_12[0][0
                                                                 tf_op_layer_RealDiv[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_2 (TensorFlow (None, None, 2)      0           tf_op_layer_strided_slice_12[0][0
                                                                 tf_op_layer_RealDiv_2[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Shape_10 (TensorFlo (3,)                 0           tf_op_layer_concat_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Shape_11 (TensorFlo (2,)                 0           tf_op_layer_GatherV2_1[0][0]     
__________________________________________________________________________________________________
tf_op_layer_RealDiv_1 (TensorFl (None, None, 2)      0           tf_op_layer_Sub_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_RealDiv_3 (TensorFl (None, None, 2)      0           tf_op_layer_AddV2_2[0][0]        
__________________________________________________________________________________________________
tf_op_layer_strided_slice_10 (T ()                   0           tf_op_layer_Shape_10[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_11 (T ()                   0           tf_op_layer_Shape_11[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_14 (T (None, None, 1)      0           tf_op_layer_RealDiv_1[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_15 (T (None, None, 1)      0           tf_op_layer_RealDiv_1[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_16 (T (None, None, 1)      0           tf_op_layer_RealDiv_3[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_17 (T (None, None, 1)      0           tf_op_layer_RealDiv_3[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_11/shape (T (3,)                 0           tf_op_layer_strided_slice_10[0][0
                                                                 tf_op_layer_strided_slice_11[0][0
__________________________________________________________________________________________________
tf_op_layer_concat_13 (TensorFl (None, None, 4)      0           tf_op_layer_strided_slice_14[0][0
                                                                 tf_op_layer_strided_slice_15[0][0
                                                                 tf_op_layer_strided_slice_16[0][0
                                                                 tf_op_layer_strided_slice_17[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_11 (TensorF (None, None, None)   0           tf_op_layer_GatherV2_1[0][0]     
                                                                 tf_op_layer_Reshape_11/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_concat_14 (TensorFl (None, None, None)   0           tf_op_layer_concat_13[0][0]      
                                                                 tf_op_layer_Reshape_11[0][0]     
==================================================================================================
Total params: 6,062,814
Trainable params: 6,056,606
Non-trainable params: 6,208
__________________________________________________________________________________________________
    opened by jnissin 1
  • Detection works fine on CPU but generates random boxes (wrong or no detection) on GPU.

    Detection works fine on CPU but generates random boxes (wrong or no detection) on GPU.

    I am using RTX 3070 GPU and CUDA version 10.1 with all specified requirements in the conda environment. An image of results can be found here https://drive.google.com/file/d/1w4Js_2hKDZNICafz3ZY3S1bhYRIc-Yx0/view?usp=sharing

    opened by si-hafiz-abdullah 1
  • Unable to install tensorflow-gpu==2.3.0rc0

    Unable to install tensorflow-gpu==2.3.0rc0

    I get this message while trying to execute conda env create -f conda-gpu.yml. I am able to run the code wieh a newer version of tensorflow-gpu but it will run at least 5 times slower than before I accidentally upgraded to the 2.7.0 version

    Pip subprocess error: ERROR: Could not find a version that satisfies the requirement tensorflow-gpu==2.3.0rc0 (from versions: 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.6.0, 2.6.1, 2.6.2, 2.7.0rc0, 2.7.0rc1, 2.7.0) ERROR: No matching distribution found for tensorflow-gpu==2.3.0rc0

    failed

    CondaEnvException: Pip failed

    opened by AnzeVersnik 1
  • 0.7 FPS

    0.7 FPS

    I do not know exactly why the processing is taking 0.7 FPS. I Tried em 2 diferrent GPUs, the first was a Radeon AMD 520 (2GB) , it is not so good, so I was not surprised. But when I tried in a AMD 570 4GB (what is better than my GPU) the result was the SAME. Did not presented any error. I belive maybe it just works in NVIDIA's GPU.

    opened by Guiflayrom 1
  • downloading tensorflow==2.3.0rc0

    downloading tensorflow==2.3.0rc0

    I fulfilled all the requirements except for tensorflow==2.3.0rc0

    I read the rc0 part means this version is a prerelease which doesn't exist anymore. I should download tensorflow==2.3.0.

    I did download tensorflow==2.3.0 but object_tracker.py still says

    "Package requirement 'tensorflow==2.3.0rc0' is not satisfied

    Any ideas?

    I ran the script and I get no object detection boxes.

    I also tried tensorflow==2.5.0

    opened by tower24 0
  • Bounding boxes not showing when using `tensorflow-gpu`

    Bounding boxes not showing when using `tensorflow-gpu`

    Hi,

    So I've trained a YOLOv4 custom object detector on Colab and then downloaded the yolov4-custom.weights file.

    When I run python save_model.py --weights ./data/yolov4-custom.weights --output ./checkpoints/yolov4-custom --model yolov4 using a tensorflow-gpu env, the ./checkpoints/yolov4-custom/ has the .pb files.

    But when I run python object_tracker.py --video ./data/video/test.mp4 --output ./outputs/result.avi --weights ./checkpoints/yolov4-custom --model yolov4, I don't see any bounding boxes on the video.

    However, when I run the same command using a tensorflow-cpu env, the bounding boxes are drawn and everything works perfectly.

    Am I missing something?

    opened by yassine-rd 4
  • Running at only 0.5 fps, CPU only

    Running at only 0.5 fps, CPU only

    I'm running this DeepSORT implementation on Google Colab and the runtime is about 0.4-0.5fps.

    I'm connected to a GPU but it seems like only the CPU is working. I've tried the following:

    • Uninstall tensorflow, install tensorflow-gpu 2.3.0 (note: 2.3.0rc0 was a pre-release and it's no longer available)
    • Verifying GPU is available before running deepsort
    • Converted my darknet weight to TF (several times)
    • Restarted the runtime

    Any ideas? Seems like this is an ongoing problem and unfortunately renders this implementation unusable until its fixed.

    Update: Running the command "tf.test.gpu_device_name()" should output "/device:GPU:0" meaning there is one GPU connected and available. With TF 2.3.0 installed, there is no output of this command. With the latest version of TF installed, the command is recognized and the expected output appears.

    The issue here is TF version 2.3.0 seems to not compatible with the current version of CUDA (11.2), so the GPU is not used. Can this repo be updated to run on a new version of tensorflow?

    Capture
    opened by mg-12345 2
  • Bounding boxes not observed

    Bounding boxes not observed

    Hi, I tried all 3 models - yolov3, yolov4 and yolo-tiny I downloaded the yolo .weights file, when I run python save_model.py --model yolov4, the ./checkpoints/yolov4-416/ has the .pb files when I run python object_tracker.py --video ./data/video/test.mp4 --output ./outputs/demo.avi --model yolov3 I dont see any bounding boxes

    May I know what is going wrong ??

    opened by Nithin7791 3
  • Only some bounding boxes drawn in output video

    Only some bounding boxes drawn in output video

    Hi, When I run the object_tracker.py file, I get the output video correctly, but I can only visualize some bounding boxes. For example, in the first frames, it counts 13 tracked objects, but the box does not appear, it is not drawn in the video. Nevertheless, in other frames of the same video, the bounding boxes appear. Does anyone know why or could help? Thanks in advance.

    image

    opened by rebeccamart 0
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