Datasets:
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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],
"video_ocr_area_ratio": [
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],
"video_watermark_prob": [
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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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"video_watermark_prob": [
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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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],
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"video_watermark_prob": [
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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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"num_token": 17,
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"special_char_ratio": 0.2833333333,
"stopwords_ratio": 0.5,
"text_len": 60,
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"video_frames_aesthetics_score": [
0.4742202759
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"video_aspect_ratios": [
1.7777777778
],
"video_duration": [
4
],
"video_height": [
720
],
"video_width": [
1280
],
"video_frames_text_similarity": [
0.319634378
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"video_motion_score": [
10.1790513992
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0.0001222684
],
"video_ocr_area_ratio": [
0.0214476612
],
"video_watermark_prob": [
0.8281876445
]
} |
<__dj__video> A man with glasses is sitting in a car and eating food. | [
"./videos/panda/0p8CO6j9U-M_3.mp4"
] | {
"alnum_ratio": 0.7246376812,
"char_rep_ratio": 0,
"flagged_words_ratio": 0,
"lang": "en",
"lang_score": 0.923728466,
"num_action": 2,
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"num_token": 19,
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"video_duration": [
17
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1280
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"video_frames_text_similarity": [
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],
"video_watermark_prob": [
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} |
<__dj__video> an image of someone pouring soil into a wheelbarrow | [
"./videos/internvid/t692w4byVrw_7_2.mp4"
] | {
"alnum_ratio": 0.7692307692,
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"lang": "en",
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"word_rep_ratio": 0,
"video_frames_aesthetics_score": [
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"video_aspect_ratios": [
1.7777777778
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"video_duration": [
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720
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1280
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0.0001119684
],
"video_ocr_area_ratio": [
0
],
"video_watermark_prob": [
0.5961615443
]
} |
<__dj__video> a wedding car with a red ribbon and flowers | [
"./videos/internvid/froKkfIPaHA_1_1.mp4"
] | {
"alnum_ratio": 0.7368421053,
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"stopwords_ratio": 0.4,
"text_len": 57,
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"video_frames_aesthetics_score": [
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"video_duration": [
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"video_width": [
1280
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"video_frames_text_similarity": [
0.3092386127
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0.0001391819
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0.0036116536
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"video_watermark_prob": [
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} |
<__dj__video> A man dancing with two young girls in a dance studio. | [
"./videos/panda/-vPbw02IbRc_3.mp4"
] | {
"alnum_ratio": 0.7313432836,
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"video_duration": [
4
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"video_height": [
720
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1280
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"video_frames_text_similarity": [
0.3328181803
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"video_motion_score": [
6.7048120499
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"video_nsfw_score": [
0.0001282161
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"video_ocr_area_ratio": [
0.0047927517
],
"video_watermark_prob": [
0.5738118887
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} |
<__dj__video> young people wearing face masks sit in the seats | [
"./videos/internvid/ivU8GO4Zyo0_7_1.mp4"
] | {
"alnum_ratio": 0.7580645161,
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"lang": "en",
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"num_action": 2,
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"num_token": 15,
"num_words": 10,
"perplexity": 2879.6,
"special_char_ratio": 0.2419354839,
"stopwords_ratio": 0.2,
"text_len": 62,
"word_rep_ratio": 0,
"video_frames_aesthetics_score": [
0.4656786919
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"video_aspect_ratios": [
1.7777777778
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"video_duration": [
3
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720
],
"video_width": [
1280
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0.3210753798
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"video_motion_score": [
0.7968443036
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0.0001760678
],
"video_ocr_area_ratio": [
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"
] | {
"alnum_ratio": 0.7553191489,
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"lang": "en",
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0.4479264021
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"video_aspect_ratios": [
1.7777777778
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"video_duration": [
13
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720
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1280
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0.3299186826
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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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