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• Assumption: Training sub images and corresponding yolov4 annotation formatted text files were generated
• Decide on cross validation folds and generate train.txt and val.txt using
  • To refer yolo helper methods for data preparation
• Generate data distribution across training and validation for each folds, fold-wise vehicle counts, single or multi class-wise distribution using methods in
  • To refer yolo helper methods for data visualization
  • To refer yolo helper methods for analysis of training and evaluation:
• The above plots on data help understand the noise and ground truth distribution between folds, whether single class or multi-class. This will help in analysis for evaluation.
• Use single obj/data folder to store all train image, annotation file pairs.
• For validation images: save all validation images to each of separate folders since –save_labels creates imagename.txt files during Test evaluation step.
• Create obj_foldnumber.data for each of cross validation folds such that each includes corresponding train_foldnumber.txt and val_foldnumber.txt
• Update obj.names to class names that needs detection
• Generate anchors specific to each of train_fold you would like to train.:
  • ./darknet detector calc_anchors cross_validation_folds/anchors/cfg/obj2.data -num_of_clusters 9 -width 768 -height 768

• Clone To clone Darknet repository, click here:
• Download Yolov4-custom.cfg from Yolov4 custom configuration, click here:
• Download weights file i.e. yolov4.conv.137 from Yolov4 weights for custom object detection, click here:
• Update width and height to each of training image size
• Update max_batches one for first 1000 iterations
• And another max_batches value for after 1000 iterations – should be rounded to closest to Total number of training images.
• Update classes = total number of classes for required for object detection. Use classes =1
• Update filters = (num of classes + 5) * 3, filters = 18 (for 1 class)
• Create obj.data, obj.names, backup folder (to save results).
• In obj.data, generate training images paths saved to train.txt and validation image paths saved to val.txt.
• Update obj.names path in obj.data and backup folder path.
• Generate anchors for obj.data configuration (consisting of train and validation text files with image paths)
  • ./darknet detector calc_anchors /home/sakoju/evaluate/cross_validation_folds/anchors/cfg/obj2.data -num_of_clusters 9 -width 768 -height 768
  • Update the anchors results saved (in darknet/ folder) to yolov4-custom.cfg
• For first 1000 iterations- use max_batches = 1000
• Update detector.c -> line 381 and as follows to save weights every 600 iterations. For “this” training specific case, we save weights every 600 iterations
  • 
• Execute training for above configurations:
  • ./darknet detector train cfg/obj.data cfg/yolov4-custom.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map -gpus 0,1,2,3 | tee log.txt
• Copy weights generated for 600 iterations to a separate folder and re-train using this weights.
• Then for > 1000 iterations, by using saved weights file from backup folder from step 9, train with max_batches from step 5
  • ./darknet detector train cfg/obj.data cfg/yolov4-custom.cfg new_weights/yolov4_600.weights -dont_show -mjpeg_port 8090 -map -gpus 0,1,2,3 | tee log.txt

• Training results are stored in log.txt in /darknet folder
• Using log_parser.py, by giving input source folder containing log.txt and target folder to save plots, https://github.com/sushmaakoju/yolo/blob/main/src/yolo_plots.py
• Generate Avg loss plots over iterations

• For each of obj_foldnumber.data, map evaluation needs to be conducted or depending which obj.data or train, validation set you used.
• Update or use the bash script to run evaluations:
  • Refer evaluation mAP scripts
• Map Evaluation commands used:
  • -dont_show (don’t show loss/results window)
  • -ext_output (save coordinates detected )
  • < cross_validation_folds/training/val3.txt > (input validation images)
  • map_results3.txt (output log file to store command line output)
• Update or use the back script to run test evaluations:
  • Refer evaluation test scripts
• Test evaluation commands use:
  • -thresh 0.25 (threshold for ioU)
  • -dont_show (don’t show loss/results window)
  • -save_labels (saves the labels as imagename.txt)
  • < cross_validation_folds/training/val7.txt (input validation images)

  • test_results7.txt (output log file to store command line output)

• number of classes = 1
• max_batches could be 2000 * number of classes, but should satisfy follwoing two conditions:
  • max_batches greater than or equal to total # of training images and
  • max_batches greater than or equal to 6000
• For the "example" train set: image group 2 and validation : image group 1:
• For this example small batch of training group 2, Total # of images in training set : 64426.
• batch size = 64.
• One epoch  requires number of train images / batch size => 64426/64 = ~1007 iterations.
• one single epoch does full pass on a complete dataset with ~1007 iterations. so max_batches / iterations = total # of epochs. 
• so max batches should atleast be 64426 which meets all 2 conditions.
• Update steps count which will be 80% and 90% of max_batches
• The anchor boxes are the “Prior” boxes to the training giving an intuition to look for objects that fit those sizes.

• ./darknet detector calc_anchors cross_validation_folds/anchors/cfg/obj2.data -num_of_clusters 9 -width 768 -height 768

• ./darknet detector map cross_validation_folds/training/cfg/obj9.data cross_validation_folds/training/cfg/yolov4.cfg pre-trained-weights/yolov4.weights -dont_show -ext_output < cross_validation_folds/training/val9.txt > map_results9.txt

• ./darknet detector test cross_validation_folds/training/cfg/obj8.data cross_validation_folds/training/cfg/yolov4.cfg pre-trained-weights/yolov4.weights -dont_show -save_labels < cross_validation_folds/training/val8.txt > map_results8.txt

• How to train to detect custom objects for Yolov4
• Refer YOLOv4: Optimal Speed and Accuracy of Object Detection
• @misc{bochkovskiy2020yolov4, title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, year={2020}, eprint={2004.10934}, archivePrefix={arXiv},primaryClass={cs.CV}}