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--- |
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annotations_creators: |
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- found |
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language_creators: |
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- found |
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language: |
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- en |
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license: |
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- mit |
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multilinguality: |
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- monolingual |
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pretty_name: CLIP-Kinetics700 |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- feature-extraction |
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- zero-shot-classification |
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--- |
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# Dataset Card for CLIP-Kinetics70 |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Dataset Preprocessing](#dataset-preprocessing) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Source Data](#source-data) |
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- [Simple Experiments](#dataset-creation) |
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- [Zero-shot Evaluation](#zero-shot) |
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- [Linear-probe Evaluation](#zero-shot) |
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## Dataset Description |
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### Dataset Summary |
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CLIP-Kinetics700 is a compressed version of the Kinetics700 dataset using OpenAI's CLIP model. |
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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. |
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### Dataset Preprocessing |
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[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. |
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## Dataset Structure |
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### Data Format |
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We formatted this as a [WebDataset](https://github.com/webdataset/webdataset) for better data-loading performance when training the models. |
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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). |
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``` |
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CLIP-Kinetics700 |
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├── splits.csv |
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├── ds_00000.tar |
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| ├── vid_00000.npy |
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| ├── vid_00000.txt |
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| ├── vid_00000.json |
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| ├── vid_00001.npy |
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| ├── vid_00001.txt |
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| ├── vid_00001.json |
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| └── ... |
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| ├── vid_10000.npy |
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| ├── vid_10000.txt |
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| ├── vid_10000.json |
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├── ds_00001.tar |
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| ├── vid_10001.npy |
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| ├── vid_10001.txt |
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| ├── vid_10001.json |
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│ ... |
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... |
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``` |
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### Data Fields |
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* vid.npy: the numpy array with the per-frame embeddings. Shape -> (n_frames, 512) |
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* vid.cap: the "caption" of the video. In this case it is the Kinetics700 label. |
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* vid.json: additional metadata - YouTube video ID, start time, end time. |
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### Data Splits |
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* Train - 536489 samples | 54 tar's |
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* Validation - 33966 samples | 4 tar's |
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* Test - 64532 samples | 7 tar's |
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## Dataset Creation |
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### Source Data |
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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. |
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## Simple Experiments |
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Using [this repository](https://github.com/LAION-AI/temporal-embedding-aggregation) we evaluate CLIP-Kinetics700 with the following simple methods: |
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### [Zero-shot Evaluation](https://github.com/LAION-AI/temporal-embedding-aggregation/blob/master/src/evaluation/zero_shot.py) |
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| | Accuracy | |
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| ---------------- | -------- | |
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| Top-1 | 0.31 | |
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| Top-5 | 0.56 | |
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| mean(Top1, Top5) | 0.44 | |
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### [Linear-probe Evaluation](https://github.com/LAION-AI/temporal-embedding-aggregation/blob/master/src/evaluation/linear_probe.py) |
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| | Accuracy | |
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| ---------------- | -------- | |
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| Top-1 | 0.41 | |
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| Top-5 | 0.65 | |
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| mean(Top1, Top5) | 0.53 | |
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