| This file gives documentation for the cars 196 dataset. | |
| (http://ai.stanford.edu/~jkrause/cars/car_dataset.html) | |
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| Metadata/Annotations | |
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| 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. | |
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| Submission file format | |
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| 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. | |