You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: acfTrain.m
    path of file: ./repos/toolbox/detector
    
    the code of the file until where you have to start completion: function detector = acfTrain( varargin )
% Train aggregate channel features object detector.
%
% Train aggregate channel features (ACF) object detector as described in:
%  P. Dollár, R. Appel, S. Belongie and P. Perona
%   "Fast Feature Pyramids for Object Detection", PAMI 2014.
% The ACF detector is fast (30 fps on a single core) and achieves top
% accuracy on rigid object detection. Please see acfReadme.m for details.
%
% Takes a set of parameters opts (described in detail below) and trains a
% detector from start to finish including performing multiple rounds of
% bootstrapping if need be. The return is a struct 'detector' for use with
% acfDetect.m which fully defines a sliding window detector. Training is
% fast (on the INRIA pedestrian dataset training takes ~10 minutes on a
% single core or ~3m using four cores). Taking advantage of parallel
% training requires launching matlabpool (see help for matlabpool). The
% trained detector may be altered in certain ways via acfModify(). Calling
% opts=acfTrain() returns all default options.
%
% (1) Specifying features 
    
function for reading images
%   .imreadp    - [{}] optional custom parameters for imreadf
%   .pLoad      - [..] params for bbGt>bbLoad (see bbGt)
%   .nPos       - [inf] max number of pos windows to sample
%   .nNeg       - [5000] max number of neg windows to sample
%   .nPerNeg    - [25]  max number of neg windows to sample per image
%   .nAccNeg    - [10000] max number of neg windows to accumulate
%   .pJitter    - [{}] params for jittering pos windows (see jitterImage)
%   .winsSave   - [0] if true save cropped windows at each stage to disk
%
% OUTPUTS
%  detector   - trained object detector (modify only via acfModify)
%   .opts       - input parameters used for model training
%   .clf        - learned boosted tree classifier (see adaBoostTrain)
%   .info       - info about channels (see chnsCompute.m)
%
% EXAMPLE
%
% See also acfReadme, acfDetect, acfDemoInria, acfModify, acfTest,
% chnsCompute, chnsPyramid, adaBoostTrain, bbGt, bbNms, jitterImage
%
% Piotr's Computer Vision Matlab Toolbox      Version 3.50
% Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]

% initialize opts struct
opts = initializeOpts( varargin{:} );
if(nargin==0), detector=opts; return; end