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-# scaledYOLOv4
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+# YOLOv4-tiny
+
+This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using Darknet framwork.
+
+## Installation
+
+```
+# create the docker container, you can change the share memory size if you have more.
+nvidia-docker run --name yolov4_csp -it -v your_coco_path/:/coco/ -v your_code_path/:/yolo --shm-size=64g nvcr.io/nvidia/pytorch:20.02-py3
+
+# install opencv
+apt update
+apt install libopencv-dev
+
+# go to code folder
+cd /yolo
+make -j4
+```
+
+## Testing
+
+```
+# download yolov4-tiny.weights and put it in /yolo/weights/ folder.
+./darknet detector valid cfg/coco.data cfg/yolov4-tiny.cfg weights/yolov4-tiny.weights -out yolov4-tiny -gpus 0
+python valcoco.py ./results/yolov4-tiny.json
+```
+
+You will get the results:
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.220
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.421
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.207
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.309
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.214
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.352
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.379
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529
+```
+
+## Training
+
+```
+./darknet detector train cfg/coco.data cfg/yolov4-tiny.cfg -gpus 0 -dont_show
+```
+
+For resume training:
+```
+# assume the checkpoint is stored in ./coco/.
+./darknet detector train cfg/coco.data cfg/yolov4-tiny.cfg coco/yolov4-tiny_last.weights -gpus 0 -dont_show
+```