fix README

README.md

@@ -0,0 +1,112 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+pretty_name: CLIP-Kinetics700
+size_categories:
+- 100K<n<1M
+task_categories:
+- feature-extraction
+- zero-shot-classification
+---
+
+# Dataset Card for CLIP-Kinetics70
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Source Data](#source-data)
+- [Simple Experiments](#dataset-creation)
+  - [Zero-shot Evaluation](#zero-shot)
+  - [Linear-probe Evaluation](#zero-shot)
+  
+## Dataset Description
+### Dataset Summary
+
+CLIP-Kinetics700 is a compressed version of the Kinetics700 dataset using OpenAI's CLIP model.
+
+The original dataset is ~700 GB making it difficult to use and hold in memory on one machine. By downsampling each video to 1 FPS and encoding the frames using CLIP we we're able to compress the dataset to ~8 GB making it very memory-friendly and easy to use.
+
+### Dataset Preprocessing
+
+[clip-video-encode](https://github.com/iejMac/clip-video-encode) is a tool you can use to easily and efficiently compute CLIP embeddings from video frames. We used it to generate the embeddings for this dataset.
+
+## Dataset Structure
+
+### Data Format
+
+We formatted this as a [WebDataset](https://github.com/webdataset/webdataset) for better data-loading performance when training the models.
+Each split contains a list of tar files each with 10000 data samples. This format can be read and used easily using the EmbeddingWebDatasetReader from [clip-video-encode](https://github.com/iejMac/clip-video-encode).
+
+```
+CLIP-Kinetics700
+ ├── splits.csv
+ ├── ds_00000.tar
+ |     ├── vid_00000.npy
+ |     ├── vid_00000.txt
+ |     ├── vid_00000.json
+ |     ├── vid_00001.npy
+ |     ├── vid_00001.txt
+ |     ├── vid_00001.json
+ |     └── ...
+ |     ├── vid_10000.npy
+ |     ├── vid_10000.txt
+ |     ├── vid_10000.json
+ ├── ds_00001.tar
+ |     ├── vid_10001.npy
+ |     ├── vid_10001.txt
+ |     ├── vid_10001.json
+ │     ...
+ ...
+```
+
+
+### Data Fields
+* vid.npy: the numpy array with the per-frame embeddings. Shape -> (n_frames, 512)
+* vid.cap: the "caption" of the video. In this case it is the Kinetics700 label.
+* vid.json: additional metadata - YouTube video ID, start time, end time.
+
+### Data Splits
+* Train - 536489 samples | 54 tar's
+* Validation - 33966 samples | 4 tar's
+* Test - 64532 samples | 7 tar's
+
+## Dataset Creation
+
+### Source Data
+
+Data was sourced from DeepMind's [Kinetics700](https://www.deepmind.com/open-source/kinetics) dataset and downloaded using [this](https://github.com/cvdfoundation/kinetics-dataset) convenient repository.
+
+## Simple Experiments
+
+Using [this repository](https://github.com/LAION-AI/temporal-embedding-aggregation) we evaluate CLIP-Kinetics700 with the following simple methods:
+
+### [Zero-shot Evaluation](https://github.com/LAION-AI/temporal-embedding-aggregation/blob/master/src/evaluation/zero_shot.py)
+
+|                  | Accuracy |
+| ---------------- | -------- |
+|      Top-1       |   0.22   |
+|      Top-5       |   0.43   |
+| mean(Top1, Top5) |   0.33   |
+
+### [Linear-probe Evaluation](https://github.com/LAION-AI/temporal-embedding-aggregation/blob/master/src/evaluation/linear_probe.py)
+
+|                  | Accuracy |
+| ---------------- | -------- |
+|      Top-1       |   0.41   |
+|      Top-5       |   0.65   |
+| mean(Top1, Top5) |   0.53   |