Datasets:
Dataset Viewer
text
stringclasses 10
values | videos
sequencelengths 1
1
| __dj__stats__
dict |
|---|---|---|
<__dj__video> A group of children dressed in colorful costumes are participating in an outdoor event where they are decorating pumpkins with bubble wands.
|
[
"./videos/panda/1AiNG23hyUo_7.mp4"
] |
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2
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720
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]
}
|
<__dj__video> a screenshot of an asian screenshot of a game
|
[
"./videos/internvid/RVZUInNpCFU_9_1.mp4"
] |
{
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}
|
<__dj__video> the word 'gynynyt' is in the middle of many different fruits and vegetables
|
[
"./videos/internvid/Ip9TjM5PY9I_1_1.mp4"
] |
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}
|
<__dj__video> a woman in a yellow shirt is on a tennis court
|
[
"./videos/internvid/Per2JwtVUwM_3_1.mp4"
] |
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"video_duration": [
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}
|
<__dj__video> A man with glasses is sitting in a car and eating food.
|
[
"./videos/panda/0p8CO6j9U-M_3.mp4"
] |
{
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}
|
<__dj__video> an image of someone pouring soil into a wheelbarrow
|
[
"./videos/internvid/t692w4byVrw_7_2.mp4"
] |
{
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}
|
<__dj__video> a wedding car with a red ribbon and flowers
|
[
"./videos/internvid/froKkfIPaHA_1_1.mp4"
] |
{
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}
|
<__dj__video> A man dancing with two young girls in a dance studio.
|
[
"./videos/panda/-vPbw02IbRc_3.mp4"
] |
{
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"video_duration": [
4
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"video_frames_text_similarity": [
0.3328181803
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0.0047927517
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}
|
<__dj__video> young people wearing face masks sit in the seats
|
[
"./videos/internvid/ivU8GO4Zyo0_7_1.mp4"
] |
{
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"num_token": 15,
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"perplexity": 2879.6,
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"text_len": 62,
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"video_duration": [
3
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720
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1280
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0.3210753798
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0.7968443036
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0.0001760678
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0.0179399957
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"video_watermark_prob": [
0.9622330666
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}
|
<__dj__video> A man in a blue shirt smiles at the camera while people stand in the background.
|
[
"./videos/panda/-mdIuelE99E_17.mp4"
] |
{
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}
|
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development
Project description
The emergence of large-scale multi-modal generative models has drastically advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models remains challenging due to historically isolated paths of model-centric and data-centric developments, leading to suboptimal outcomes and inefficient resource utilization. In response, we present a novel sandbox suite tailored for integrated data-model co-development. This sandbox provides a comprehensive experimental platform, enabling rapid iteration and insight-driven refinement of both data and models. Our proposed "Probe-Analyze-Refine" workflow, validated through applications on T2V-Turbo and achieve a new state-of-the-art on VBench leaderboard with 1.09% improvement from T2V-Turbo. Our experiment code and model are released at Data-Juicer Sandbox.
Dataset Information
- The whole dataset is available here (About 227.5GB).
- Number of samples: 147,176 (Include videos and keep ~12.09% from the original dataset)
- The original dataset totals 1,217k instances from InternVid (606k), Panda-70M (605k), and MSR-VTT (6k).
Refining Recipe
# global parameters
# global parameters
project_name: 'Data-Juicer-recipes-T2V-optimal'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 4 # number of subprocess to process your dataset
# process schedule
# a list of several process operators with their arguments
process:
- video_nsfw_filter:
hf_nsfw_model: Falconsai/nsfw_image_detection
score_threshold: 0.000195383
frame_sampling_method: uniform
frame_num: 3
reduce_mode: avg
any_or_all: any
mem_required: '1GB'
- video_frames_text_similarity_filter:
hf_clip: openai/clip-vit-base-patch32
min_score: 0.306337
max_score: 1.0
frame_sampling_method: uniform
frame_num: 3
horizontal_flip: false
vertical_flip: false
reduce_mode: avg
any_or_all: any
mem_required: '10GB'
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