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---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- 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 | 0.31 |
| Top-5 | 0.56 |
| mean(Top1, Top5) | 0.44 |
### [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 |
|