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README.md

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 license: mit
+tags:
+- vsd
+pretty_name: VSD Fashion
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+
+# VSD Fashion Dataset
+
+## Description
+A dataset for visual similarity tasks, that includes various zero-shot and trainable tasks from the VSD Paper.
+Visual similarity measures the perceptual agreement between two objects based on their visual appearance. Two objects can be similar or dissimilar based on their color, shape, size, pattern, utility, and more. In fact, all of these factors and many others take part in determining the degree of visual similarity between two objects with varying importance. Therefore, defining the perceived visual simi- larity based on these factors is challenging. Nonetheless, learning visual similarities is a key building block for many practical utilities such as search, recommendations, etc.
+
+- **Repository: https://github.com/vsd-benchmark/vsd** 
+- **Paper: [TBD]** 
+- **Leaderboard: https://vsd-benchmark.github.io/vsd/** 
+
+### Dataset Summary
+Visual similarity measures the perceptual agreement between two objects based on their visual appearance. Two objects can be similar or dissimilar based on their color, shape, size, pattern, utility, and more. In fact, all of these factors and many others take part in determining the degree of visual similarity between two objects with varying importance. Therefore, defining the perceived visual simi- larity based on these factors is challenging. Nonetheless, learning visual similarities is a key building block for many practical utilities such as search, recommendations, etc.
+
+### Supported Tasks and Leaderboards
+The dataset contains several tasks (displayed in the leaderboard site):
+
+1. VSD Fashion Dataset
+  a. In Catalog Retrieval
+    1. Zero Shot Retrieval Task. (**in_catalog_retrieval_zero_shot**)
+    2. Open Catalog Training Retrieval Task - Same queries appear in train and test. (**in_catalog_open_catalog**)
+    3. Closed Catalog Training Retrieval Task - Queries in train and test do not intersect. (**in_catalog_closed_catalog**)
+  b. Consumer-Catalog (Wild) Retrieval
+    1. Zero Shot Retrieval Task catalog_wild_zero_shot (**catalog_wild_zero_shot**)
+  
+  More information about each task can be found in the leaderboard and the paper.
+
+### Usage
+
+#### Dataset Images
+The dataset itself contains only annotations, and is based on DeepFashion dataset.
+
+The DeepFashion dataset can be downloaded from [here](https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) and extracted into a folder named 'img'.
+
+The In-shop Clothes Retrieval Benchmark and Consumer-to-shop Clothes Retrieval Benchmark should be downloaded.
+There should be six folders in ```datasets/img``` after extraction:
+
+```
+  datasets/img/CLOTHING - Consumer-to-shop Clothes Retrieval Benchmark
+  datasets/img/DRESSES - Consumer-to-shop Clothes Retrieval Benchmark
+  datasets/img/TOPS - Consumer-to-shop Clothes Retrieval Benchmark
+  datasets/img/TROUSERS - Consumer-to-shop Clothes Retrieval Benchmark
+  datasets/img/MEN - In-shop Clothes Retrieval Benchmark
+  datasets/img/WOMEN - In-shop Clothes Retrieval Benchmark
+```
+
+#### HuggingFace datasets api
+In order to load the dataset annotations through HuggingFace hub, choose a task and run
+```python
+  ds = load_dataset('vsd-benchmark/vsd-fashion', 'your_chosen_task', image_folder='./image_folder')
+```
+
+Where 'your_chosen_task' should be one of the tasks mentioned above, and './image_folder' should be the path that contains the 'img' folder.
+
+Note that the zero-shot tasks only have a 'test' set.
+
+#### Using the annotations directly
+Instead of using 'datasets' loader, you can download the annotation files directly using:
+```python
+    dataset_builder = load_dataset_builder('vsd-benchmark/vsd-fashion', task, image_folder=image_folder)
+    splits = dataset_builder._split_generators(None)
+```
+
+### License
+
+VSD Fashion is licensed under MIT License. The released models can be used for commercial purposes free of charge.
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]