fix a bug in track.py

add syncbn

Update README.md

Update README.md

cl

Update README.md

Update README.md

Update README.md

README.md

@@ -1,8 +1,18 @@
 # Towards-Realtime-MOT
+**NOTE:** Still in progress, will update constantly, thank you for your attention!
 
 ## Introduction
+This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
 
-## Installation
+We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
+
+## Requirements
+* Python 3.6
+* [Pytorch](https://pytorch.org) >= 1.0.1
+* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
+* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
+* python-opencv
+* ffmpeg (Optional, used in the video demo)
 
 ## Video Demo
 
@@ -16,3 +26,5 @@
 
 ## Train with custom datasets
 
+## Acknowledgement
+A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!

models.py

@@ -185,7 +185,7 @@ class YOLOLayer(nn.Module):
             #loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
             #       (self.s_r + self.s_c + self.s_id)
             #loss *= 0.5
-            loss = 1*lbox + 1*lconf + 1*lid
+            loss = 0*lbox + 0*lconf + 1*lid
 
             return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
 

syncbn

@@ -1 +0,0 @@
-Subproject commit 265a7059ebbd20c27a81c3d74d43773779fe70d7

test.py

@@ -55,7 +55,6 @@ def test(
     outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
         [], [], [], [], [], [], [], [], []
     AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
-    coco91class = coco80_to_coco91_class()
     for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
         t = time.time()
         output = model(imgs.cuda())

track.py

@@ -38,7 +38,8 @@ def write_results(filename, results, data_type):
 
 
 def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
-    mkdir_if_missing(save_dir)
+    if save_dir:
+        mkdir_if_missing(save_dir)
     tracker = JDETracker(opt, frame_rate=frame_rate)
     timer = Timer()
     results = []