ShapeY is a benchmark that tests a vision system's shape recognition capacity. ShapeY currently consists of ~68k images of 200 3D objects taken from ShapeNet. Note that this benchmark is not meant to be used as a training dataset, but rather serves to validate that the visual object recogntion / classification under inspection has developed a capacity to perform well on our benchmarking tasks, which are designed to be hard if the system does not understand shape.

Requirements: Python 3, Cuda version 10.2 (prerequisite for cupy)

To install ShapeY, run the following command:

pip3 install shapey==0.1.7

Run download.sh to download the dataset. The script automatically unzips the images under data/ShapeY200/. Downloading uses gdown, which is google drive command line tool. If it does not work, please just follow the two links down below to download the ShapeY200 / ShapeY200CR datasets.

ShapeY200: https://drive.google.com/uc?id=1arDu0c9hYLHVMiB52j_a-e0gVnyQfuQV

ShapeY200CR: https://drive.google.com/uc?id=1WXpNUVRn6D0F9T3IHruml2DcDCFRsix-

After downloading the two datasets, move each of them to the data/ directory. For example, all of the images for ShapeY200 should be under data/ShapeY200/dataset/.

Implement the function your_feature_output_code in step1_save_feature/your_feature_extraction_code.py. The function should take in the path to the dataset as input and return two lists - one for the image names and another for the corresponding embedding vectors taken from whatever system.

After you have implemented the function, run macro.py to generate the results. macro.py will automatically run the following steps:

1. Compute correlation between all embedding vectors (using step2_compute_feature_correlation/compute_correlation.py)

2. Run benchmark analysis (using step3_benchmark_analysis/get_nn_classification_error_with_exclusion_distance.py)

3. Graph results (top1 object matching error, top1 category matching error, etc.)