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.gitattributes

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README.md

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----
-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   |
-
-
-
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