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.gitattributes

@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 cars_test_annos.mat filter=lfs diff=lfs merge=lfs -text
 cars_train_annos.mat filter=lfs diff=lfs merge=lfs -text
+car_devkit/cars_test_annos.mat filter=lfs diff=lfs merge=lfs -text
+car_devkit/cars_train_annos.mat filter=lfs diff=lfs merge=lfs -text

car_devkit/README.txt

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+This file gives documentation for the cars 196 dataset.
+(http://ai.stanford.edu/~jkrause/cars/car_dataset.html)
+
+----------------------------------------
+Metadata/Annotations
+----------------------------------------
+Descriptions of the files are as follows:
+
+-cars_meta.mat:
+  Contains a cell array of class names, one for each class.
+
+-cars_train_annos.mat:
+  Contains the variable 'annotations', which is a struct array of length
+  num_images and where each element has the fields:
+    bbox_x1: Min x-value of the bounding box, in pixels
+    bbox_x2: Max x-value of the bounding box, in pixels
+    bbox_y1: Min y-value of the bounding box, in pixels
+    bbox_y2: Max y-value of the bounding box, in pixels
+    class: Integral id of the class the image belongs to.
+    fname: Filename of the image within the folder of images.
+
+-cars_test_annos.mat:
+  Same format as 'cars_train_annos.mat', except the class is not provided.
+
+----------------------------------------
+Submission file format
+----------------------------------------
+Files for submission should be .txt files with the class prediction for
+image M on line M. Note that image M corresponds to the Mth annotation in
+the provided annotation file. An example of a file in this format is
+train_perfect_preds.txt
+
+Included in the devkit are a script for evaluating training accuracy,
+eval_train.m. Usage is:
+
+(in MATLAB)
+>> [accuracy, confusion_matrix] = eval_train('train_perfect_preds.txt')
+
+If your training predictions work with this function then your testing
+predictions should be good to go for the evaluation server, assuming
+that they're in the same format as your training predictions.

car_devkit/cars_test_annos.mat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17a7837554437d4b58041cc2fc084ba46ccf315a1b5a3ee1264501ff68ca7c79
+size 155831

car_devkit/cars_train_annos.mat

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1c1973dd828a2964060b0bbc0e423466393fe3001a3f8e303f41fbf22067dfb1
+size 187916

car_devkit/eval_train.m

@@ -0,0 +1,40 @@
+function [accuracy, confusion_matrix] = eval_train(pred_fname)
+% Evaluates training accuracy.
+% Arguments:
+%   pred_fname: Filename of prediction file for training. The required format
+%     is described in the README.
+% Returns:
+%   accuracy: The accuracy on the training set.
+%   confusion_matrix: The confusion matrix on the training set.
+
+accuracy = [];
+confusion_matrix = [];
+
+train_data = load('cars_train_annos.mat');
+train_annos = train_data.annotations;
+train_classes = [train_annos.class];
+unique_classes = unique(train_classes);
+
+try
+  preds = csvread(pred_fname);
+catch err
+  fprintf('Invalid file format for input file %s.', pred_fname);
+  return
+end
+
+
+% Check whether predictions look sane
+if numel(preds) ~= numel(train_classes)
+  fprintf(['Given predictions have length %d but there are %d images ' ...
+    'in the training set.\n'], numel(preds), numel(train_classes));
+  return;
+elseif any(~ismember(preds, unique_classes))
+  bad_ind = find(~ismember(preds, unique_classes), 1);
+  fprintf(['Predicted class for image %d is %d, which is an invalid ' ...
+    'class.\n'], bad_ind, preds(bad_ind));
+  return;
+end
+
+% Evaluate
+accuracy = mean(preds(:) == train_classes(:));
+confusion_matrix = confusionmat(train_classes(:), preds(:));

car_devkit/train_perfect_preds.txt

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