* fix caffe net paths

* add origin note to selective search proposals code
* remove outdated nms threshold tuning code

.gitignore

 .nfs*
 feat_cache/*
-graveyard
 *.mat
 bin/*
 selective_search/SelectiveSearchCodeIJCV
+data/caffe_nets
+data/rcnn_models
+data/selective_search_data
+data/window_files

README.md

@@ -6,6 +6,8 @@ Created by Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik at UC
 
 R-CNN is a state-of-the-art visual object detection system that combines bottom-up region proposals with rich features computed by a convolutional neural network. At the time of its release, R-CNN improved the previous best detection performance on PASCAL VOC 2012 by 30% relative, going from 40.9% to 53.3% mean average precision. Unlike the previous best results, R-CNN achieves this performance without using contextual rescoring or an ensemble of feature types.
 
+R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/1311.2524) and will appear in a forthcoming CVPR 2014 paper.
+
 ### Citing R-CNN
 
 If you find R-CNN useful in your research, please consider citing:

nms/nms_tune_threshold.m

-function [best_thresh, best_ap, res] = nms_tune_threshold(cls, testset, year)
-
-conf = voc_config('pascal.year', year, ...
-                  'eval.test_set', testset);
-cachedir = conf.paths.model_dir;                  
-VOCopts  = conf.pascal.VOCopts;
-load([cachedir cls '_boxes_' testset '_' year]);
-
-threshs = 0.1:0.05:0.5;
-best_ap = 0;
-
-res = zeros(length(threshs), 2);
-for i = 1:length(threshs)
-  thresh = threshs(i);
-  ap_auc = compute_at_nms_thresh(cls, boxes, thresh, VOCopts);
-  res(i,:) = [thresh ap_auc];
-  if ap_auc > best_ap
-    best_thresh = thresh;
-    best_ap = ap_auc;
-    fprintf('!!! %s AP = %.3f @ thresh = %.3f\n', cls, best_ap, thresh);
-  end
-end
-
-
-
-function ap_auc = compute_at_nms_thresh(cls, boxes, thresh, VOCopts)
-
-image_ids = textread(sprintf(VOCopts.imgsetpath, VOCopts.testset), '%s');
-% write out detections in PASCAL format and score
-fid = fopen(sprintf(VOCopts.detrespath, 'comp3', cls), 'w');
-for i = 1:length(image_ids);
-  bbox = boxes{i};
-  keep = nms(bbox, thresh);
-  bbox = bbox(keep,:);
-  for j = 1:size(bbox,1)
-    fprintf(fid, '%s %f %d %d %d %d\n', image_ids{i}, bbox(j,end), bbox(j,1:4));
-  end
-end
-fclose(fid);
-
-tic;
-[recall, prec, ap_auc] = x10VOCevaldet(VOCopts, 'comp3', cls, true);
-%ap_auc = xVOCap(recall, prec);

rcnn_cache_pool5_features.m

@@ -7,7 +7,7 @@ ip.addOptional('end', 0, @isscalar);
 ip.addOptional('crop_mode', 'warp', @isstr);
 ip.addOptional('crop_padding', 16, @isscalar);
 ip.addOptional('net_file', ...
-    './external/caffe/snapshots/v1/finetune_voc_2007_trainval_iter_70000', ...
+    './data/caffe_nets/finetune_voc_2007_trainval_iter_70k', ...
     @isstr);
 ip.addOptional('cache_name', ...
     'v1_finetune_voc_2007_trainval_iter_70000', @isstr);

rcnn_demo.m

@@ -18,3 +18,10 @@ dets = rcnn_detect(im, rcnn_model);
 % show top scoring bicycle detection
 showboxes(im, dets{2}(1,:));
 title(sprintf('bicycle conf = %.3f', dets{2}(1,end)));
+
+fprintf('\n> Press any key to see the top scoring person detection.');
+pause;
+
+% show top scoring person detection
+showboxes(im, dets{15}(1,:));
+title(sprintf('person conf = %.3f', dets{15}(1,end)));

rcnn_train.m

@@ -20,7 +20,6 @@ function [rcnn_model, rcnn_k_fold_model] = ...
 
 ip = inputParser;
 ip.addRequired('imdb', @isstruct);
-ip.addParamValue('cache_name',      'none', @isstr);
 ip.addParamValue('svm_C',           10^-3,  @isscalar);
 ip.addParamValue('bias_mult',       10,     @isscalar);
 ip.addParamValue('pos_loss_weight', 2,      @isscalar);
@@ -30,8 +29,11 @@ ip.addParamValue('checkpoint',      0,      @isscalar);
 ip.addParamValue('crop_mode',       'warp', @isstr);
 ip.addParamValue('crop_padding',    16,     @isscalar);
 ip.addParamValue('net_file', ...
-    './external/caffe/snapshots/v1/finetune_voc_2007_trainval_iter_70000', ...
+    './data/caffe_nets/finetune_voc_2007_trainval_iter_70k', ...
     @isstr);
+ip.addParamValue('cache_name', ...
+    'v1_finetune_voc_2007_trainval_iter_70000', @isstr);
+
 
 ip.parse(imdb, varargin{:});
 opts = ip.Results;

selective_search/selective_search_boxes.m

 function boxes = selective_search_boxes(im, fast_mode)
 
+% Based on the demo.m file included in the Selective Search
+% IJCV code.
+
 if ~exist('fast_mode', 'var') || isempty(fast_mode)
   fast_mode = true;
 end

vis/vis_crops.m

 function vis_crops(imdb)
 
-opts.net_file = './external/caffe/snapshots/v1/finetune_voc_2007_trainval_iter_70000';
+opts.net_file     = './data/caffe_nets/finetune_voc_2007_trainval_iter_70k';
 opts.net_def_file = './model-defs/rcnn_batch_256_output_pool5.prototxt';
 
 % load the region of interest database