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COPYRIGHT

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+Copyright (c) 2015 Places365
+
+Permission is hereby granted, free of charge, to any person
+obtaining a copy of this software and associated documentation
+files (the "Software"), to deal in the Software without
+restriction, including without limitation the rights to use,
+copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+Neither name of copyright holders nor the names of its contributors
+may be used to endorse or promote products derived from this software
+without specific prior written permission.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+

README.md

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+=================================================
+Introduction
+=================================================
+
+This is the documentation of the Places365 challenge 2016 development kit.
+
+Table of contents:
+  1. Overview of challenge dataset
+  2. Challenge details
+    2.1 Image list and annotations
+    2.2 Submission format
+    2.3 Evaluation routines
+
+Please contact Bolei Zhou (bzhou@csail.mit.edu) for questions, comments,
+or bug reports.
+
+Note: Please first download the image list and annotations for Places365-Standard[http://places2.csail.mit.edu/challenge2016/filelist_places365-standard.tar] and the image list and annotations for Place365-challenge[http://places2.csail.mit.edu/challenge2016/filelist_places365-challenge.tar], and decompress the files in the data folder.
+
+=================================================
+1. Overview of challenge dataset
+=================================================
+
+There are three types of image data for this competition: training
+data from Places365-Challenge (TRAINING), validation data specific to this
+competition (VALIDATION), and test data specific to this competition
+(TEST).  There is no overlap in the three sources of data: TRAINING,
+VALIDATION, and TEST.  All three sets of data contain images of 365
+categories of scenes.
+
+                 Number of images
+
+    Dataset      TRAIN      VALIDATION     TEST
+   -----------------------------------------------
+    Places365    8,026,628      36,500      328,500
+
+Every image in training, validation and test sets has a single
+image-level label specifying the presence of one scene category
+
+Challenge database statistics:
+
+  Training:
+
+    - 8,097,967 images, with between 3068 and 40,000 per category
+
+  Validation:
+
+    - 36,500 images, with 50 images per category
+
+  Test:
+
+    - 328,500 images, with 900 images per category
+
+Packaging details:
+
+
+The 3 sets of images (training, validation and test) are available as 
+3 tar archives. All images are in JPEG format. We provide both the original 
+images and images resized to 256*256 for download.
+
+
+=================================================
+2. Challenge details
+=================================================
+
+The 365 scene categories used in the challenge dataset are part of the 
+larger Places2 dataset.
+
+All the class names and ids are available in:
+    data/categories_places365.txt,
+
+where each line contains the scene category name followed by its id
+(an integer between 0 and 364).
+
+The difference betweee Places365-challenge and Places365-standard is 
+that there are ~6.2million more extra images in Places365-challenge 
+compared to Places365-standard. The first 5000 images (or less) per 
+category in Places365-challenge belong to the Places365-standard.
+
+---------------------------
+2.1.1 Training data
+---------------------------
+
+Each image is considered as belonging to a particular scene category. 
+See [1] for more details of the collection and labeling strategy.
+
+The training images may be downloaded as a single tar archive. 
+Within it there is a tar file for each alphabet from 'a.tar' to
+'z.tar'. Note that there are 24 such files as there are no scene
+category names beginning with 'q' and 'x' in our database.
+
+After untarring all of the above files, the directory structure should
+look similar to the following:
+     a/abbey/00000000.jpg
+     a/abbey/00000001.jpg
+           ...
+     z/zen_garden/00009067.jpg      
+     z/zen_garden/00009068.jpg
+
+In general, each leaf folder contains one scene category. Note that there 
+are some categories that are fine-grained, e.g., s/swimming_pool/indoor
+and s/swimming_pool/outdoor. The complete list of training images and their 
+mapping to scene category ids is available in:
+     data/places365_train_challenge.txt
+
+All images are in JPEG format. We also include the data/places365_train_standard.txt
+here, you don't need to use it.
+
+
+-----------------------------
+2.1.2 Validation data
+-----------------------------
+
+There are a total of 36,500 validation images. They are named as
+
+      Places365_val_00000001.jpg
+      Places365_val_00000002.jpg
+      ...
+      Places365_val_00036499.jpg
+      Places365_val_00036500.jpg
+
+There are 50 validation images for each scene category.
+
+The classification ground truth of the validation images is in 
+    data/places365_val.txt,
+
+where each line contains one image filename and its corresponding scene
+category label (from 0 to 364).
+
+-----------------------
+2.1.3 Test data
+-----------------------
+
+There are a total of 328,500 test images. The test files are named as
+
+      Places365_test_00000001.jpg
+      Places365_test_00000002.jpg
+      ...
+      Places365_test_00328499.jpg
+      Places365_test_00328500.jpg
+
+There are 900 test images for each scene category. The ground truth 
+annotations will not be released.
+
+
+*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
+2.2 Submission format
+*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
+
+The submission of results on test data will consist of a text file
+with one line per image, in the alphabetical order of the image file
+names, i.e. from Places365_test_00000001.jpg to
+Places365_test_00328500.jpg. Each line contains up to 5 detected
+scenes, sorted by confidence in descending order. 
+
+The format is as follows:
+
+   <filename> <label(1)> <label(2)> <label(3)> <label(4)> <label(5)>
+
+The predicted labels are the scene categories ( integers between 0 and
+364 ).  The number of labels per line must be exactly equal to 5, or it
+would lead to an error. The filename is the same as mentioned above,
+e.g., 'Places365_test_00000001.jpg' and so on.
+
+Example file on the validation data is 
+
+  evaluation/demo.val.pred.txt    
+
+
+*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
+2.3 Evaluation routines
+*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
+
+The Matlab routine for evaluating the submission is
+
+./evaluation/eval_cls.m  
+
+To see an example of using the routines, start Matlab
+in the 'evaluation/' folder and type
+       demo_eval_cls;
+
+and you will see something similar to the following output:
+
+PLACES365 SCENE CLASSIFICATION TASK
+pred_file: demo.val.pred.txt
+ground_truth_file: ../data/places365_val.txt
+# guesses vs cls error
+    1.0000    0.9974
+    2.0000    0.9944
+    3.0000    0.9920
+    4.0000    0.9893
+    5.0000    0.9867
+
+In this demo, we take top i ( i=1...5) predictions (and ignore the
+rest) from your result file and plot the error as a function of the
+number of guesses. 
+
+Only the error with 5 guesses will be used to determine the winner.
+
+(The demo.val.pred.txt used here is a synthetic result.)
+
+
+====================================================================
+References
+====================================================================
+
+[1] B. Zhou, A. Khosla, A. Lapedriza, A. Torralba and A. Oliva
+Places: An Image Database for Deep Scene Understanding
+Arxiv, 2015 (PDF coming soon)
+http://places2.csail.mit.edu
+