Generate detections from frozen inference graph

README.md

@@ -65,17 +65,23 @@ The following example generates these features from standard MOT challenge
 detections. Again, we assume resources have been extracted to the repository
 root directory and MOT16 data is in `./MOT16`:
 ```
-python generate_detections.py
-    --model=resources/networks/mars-small128.ckpt \
+python tools/generate_detections.py
+    --model=resources/networks/mars-small128.pb \
     --mot_dir=./MOT16/train \
     --output_dir=./resources/detections/MOT16_train
 ```
-For each sequence of the MOT16 dataset, the output is stored as separate binary
-file in NumPy native format. Each file contains an array of shape `Nx138`,
-where N is the number of detections in the corresponding MOT sequence.
-The first 10 columns of this array contain the raw MOT detection copied over
-from the input file. The remaining 128 columns store the appearance descriptor.
-The files generated by this command can be used as input for the
+The model has been generated with TensorFlow 1.5. If you run into
+incompatibility, re-export the frozen inference graph to obtain a new
+`mars-small128.pb` that is compatible with your version:
+```
+python tools/freeze_model.py
+```
+The ``generate_detections.py`` stores for each sequence of the MOT16 dataset
+a separate binary file in NumPy native format. Each file contains an array of
+shape `Nx138`, where N is the number of detections in the corresponding MOT
+sequence. The first 10 columns of this array contain the raw MOT detection
+copied over from the input file. The remaining 128 columns store the appearance
+descriptor. The files generated by this command can be used as input for the
 `deep_sort_app.py`.
 
 ## Highlevel overview of source files
@@ -106,17 +112,17 @@ files. These can be computed from MOTChallenge detections using
 If you find this repo useful in your research, please consider citing the following papers:
 
     @inproceedings{Wojke2017simple,
-      title={Simple Online and Realtime Tracking with a Deep Association Metric},
       author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
       booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
+      title={Simple Online and Realtime Tracking with a Deep Association Metric},
       year={2017},
       pages={3645--3649}
     }
 
     @inproceedings{Bewley2016_sort,
       author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
       booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
-      title={Simple online and realtime tracking},
+      title={Simple Online and Realtime Tracking},
       year={2016},
       pages={3464-3468},
       doi={10.1109/ICIP.2016.7533003}