ShapeY 0.0.7

Benchmark that tests shape recognition

# ShapeY

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. Find our paper at: https://openreview.net/pdf?id=nN5ZRwsVaxU

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

To install ShapeY, run the following command: ` pip3 install shapey==0.0.6 `

## Step0: Download ShapeY200 dataset Run download.sh to download the dataset. The script automatically unzips the images under data/ShapeY200/.

## Step1: Extract the embedding vectors from your own vision model using our 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.

## Step2: Run macro.py 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.)