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mediapipe/docs/solutions/youtube_8m.md at master · google-ai-edge/mediapipe · GitHub
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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2"> Search syntax tips </a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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extraction graph"},{"level":2,"text":"Model Inference for YouTube-8M Challenge","anchor":"model-inference-for-youtube-8m-challenge","htmlText":"Model Inference for YouTube-8M Challenge"},{"level":3,"text":"Steps to run the YouTube-8M model inference graph with Web Interface","anchor":"steps-to-run-the-youtube-8m-model-inference-graph-with-web-interface","htmlText":"Steps to run the YouTube-8M model inference graph with Web Interface"},{"level":3,"text":"Steps to run the YouTube-8M model inference graph with a local video","anchor":"steps-to-run-the-youtube-8m-model-inference-graph-with-a-local-video","htmlText":"Steps to run the YouTube-8M model inference graph with a local 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itemprop=\"text\"\u003e\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003elayout\u003c/th\u003e\n \u003cth\u003etarget\u003c/th\u003e\n \u003cth\u003etitle\u003c/th\u003e\n \u003cth\u003eparent\u003c/th\u003e\n \u003cth\u003enav_order\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cdiv dir=\"auto\"\u003eforward\u003c/div\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cdiv dir=\"auto\"\u003e\u003ca href=\"https://developers.google.com/mediapipe/solutions/guide#legacy\" rel=\"nofollow\"\u003ehttps://developers.google.com/mediapipe/solutions/guide#legacy\u003c/a\u003e\u003c/div\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cdiv dir=\"auto\"\u003eYouTube-8M Feature Extraction and Model Inference\u003c/div\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cdiv dir=\"auto\"\u003eMediaPipe Legacy Solutions\u003c/div\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cdiv dir=\"auto\"\u003e16\u003c/div\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eYouTube-8M Feature Extraction and Model Inference\u003c/h1\u003e\u003ca id=\"user-content-youtube-8m-feature-extraction-and-model-inference\" class=\"anchor\" aria-label=\"Permalink: YouTube-8M Feature Extraction and Model Inference\" href=\"#youtube-8m-feature-extraction-and-model-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e{: .no_toc }\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003e\n Table of contents\n \u003c/summary\u003e\n {: .text-delta }\n1. TOC\n{:toc}\n\u003c/details\u003e\n---\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAttention:\u003c/strong\u003e \u003cem\u003eThank you for your interest in MediaPipe Solutions.\nWe have ended support for this MediaPipe Legacy Solution as of March 1, 2023.\nFor more information, see the\n\u003ca href=\"https://developers.google.com/mediapipe/solutions/guide#legacy\" rel=\"nofollow\"\u003eMediaPipe Solutions\u003c/a\u003e\nsite.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003eMediaPipe is a useful and general framework for media processing that can assist\nwith research, development, and deployment of ML models. This example focuses on\nmodel development by demonstrating how to prepare training data and do model\ninference for the YouTube-8M Challenge.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExtracting Video Features for YouTube-8M Challenge\u003c/h2\u003e\u003ca id=\"user-content-extracting-video-features-for-youtube-8m-challenge\" class=\"anchor\" aria-label=\"Permalink: Extracting Video Features for YouTube-8M Challenge\" href=\"#extracting-video-features-for-youtube-8m-challenge\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.kaggle.com/c/youtube8m-2019\" rel=\"nofollow\"\u003eYoutube-8M Challenge\u003c/a\u003e is an annual\nvideo classification challenge hosted by Google. Over the last two years, the\nfirst two challenges have collectively drawn 1000+ teams from 60+ countries to\nfurther advance large-scale video understanding research. In addition to the\nfeature extraction Python code released in the\n\u003ca href=\"https://github.com/google/youtube-8m/tree/master/feature_extractor\"\u003egoogle/youtube-8m\u003c/a\u003e\nrepo, we release a MediaPipe based feature extraction pipeline that can extract\nboth video and audio features from a local video. The MediaPipe based pipeline\nutilizes two machine learning models,\n\u003ca href=\"https://github.com/tensorflow/models/tree/master/research/inception\"\u003eInception v3\u003c/a\u003e\nand\n\u003ca href=\"https://github.com/tensorflow/models/tree/master/research/audioset/vggish\"\u003eVGGish\u003c/a\u003e,\nto extract features from video and audio respectively.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo visualize the\n\u003ca href=\"https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/feature_extraction.pbtxt\"\u003egraph\u003c/a\u003e,\ncopy the text specification of the graph and paste it into\n\u003ca href=\"https://viz.mediapipe.dev/\" rel=\"nofollow\"\u003eMediaPipe Visualizer\u003c/a\u003e. The feature extraction\npipeline is highly customizable. You are welcome to add new calculators or use\nyour own machine learning models to extract more advanced features from the\nvideos.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSteps to run the YouTube-8M feature extraction graph\u003c/h3\u003e\u003ca id=\"user-content-steps-to-run-the-youtube-8m-feature-extraction-graph\" class=\"anchor\" aria-label=\"Permalink: Steps to run the YouTube-8M feature extraction graph\" href=\"#steps-to-run-the-youtube-8m-feature-extraction-graph\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCheckout the repository and follow\n\u003ca href=\"https://github.com/google/mediapipe/blob/master/mediapipe/docs/install.md\"\u003ethe installation instructions\u003c/a\u003e\nto set up MediaPipe.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/google/mediapipe.git\ncd mediapipe\"\u003e\u003cpre\u003egit clone https://github.com/google/mediapipe.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e mediapipe\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDownload the PCA and model data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir /tmp/mediapipe\ncd /tmp/mediapipe\ncurl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb\ncurl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz\ntar -xvf /tmp/mediapipe/inception-2015-12-05.tgz\"\u003e\u003cpre\u003emkdir /tmp/mediapipe\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /tmp/mediapipe\ncurl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb\ncurl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb\ncurl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz\ntar -xvf /tmp/mediapipe/inception-2015-12-05.tgz\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGet the VGGish frozen graph.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: To run step 3 and step 4, you must have Python 2.7 or 3.5+ installed\nwith the TensorFlow 1.14+ package installed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# cd to the root directory of the MediaPipe repo\ncd -\n\npip3 install tf_slim\npython -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cd to the root directory of the MediaPipe repo\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e -\n\npip3 install tf_slim\npython -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGenerate a MediaSequence metadata from the input video.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: the output file is /tmp/mediapipe/metadata.pb\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# change clip_end_time_sec to match the length of your video.\npython -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \\\n --path_to_input_video=/absolute/path/to/the/local/video/file \\\n --clip_end_time_sec=120\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e change clip_end_time_sec to match the length of your video.\u003c/span\u003e\npython -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \\\n --path_to_input_video=/absolute/path/to/the/local/video/file \\\n --clip_end_time_sec=120\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRun the MediaPipe binary to extract the features.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"bazel build -c opt --linkopt=-s \\\n --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \\\n mediapipe/examples/desktop/youtube8m:extract_yt8m_features\n\nGLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \\\n --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \\\n --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb \\\n --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb\"\u003e\u003cpre\u003ebazel build -c opt --linkopt=-s \\\n --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \\\n mediapipe/examples/desktop/youtube8m:extract_yt8m_features\n\nGLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \\\n --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \\\n --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb \\\n --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e[Optional] Read the features.pb in Python.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"import tensorflow as tf\n\nsequence_example = open('/tmp/mediapipe/features.pb', 'rb').read()\nprint(tf.train.SequenceExample.FromString(sequence_example))\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eimport tensorflow as tf\n\nsequence_example = open('/tmp/mediapipe/features.pb', 'rb').read()\nprint(tf.train.SequenceExample.FromString(sequence_example))\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Inference for YouTube-8M Challenge\u003c/h2\u003e\u003ca id=\"user-content-model-inference-for-youtube-8m-challenge\" class=\"anchor\" aria-label=\"Permalink: Model Inference for YouTube-8M Challenge\" href=\"#model-inference-for-youtube-8m-challenge\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMediaPipe can help you do model inference for YouTube-8M Challenge with both\nlocal videos and the YouTube-8M dataset. To visualize\n\u003ca href=\"https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt\"\u003ethe graph for local videos\u003c/a\u003e\nand\n\u003ca href=\"https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt\"\u003ethe graph for the YouTube-8M dataset\u003c/a\u003e,\ncopy the text specification of the graph and paste it into\n\u003ca href=\"https://viz.mediapipe.dev/\" rel=\"nofollow\"\u003eMediaPipe Visualizer\u003c/a\u003e. We use the baseline model\n\u003ca href=\"https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view\" rel=\"nofollow\"\u003e(model card)\u003c/a\u003e\nin our example. But, the model inference pipeline is highly customizable. You\nare welcome to add new calculators or use your own machine learning models to do\nthe inference for both local videos and the dataset\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSteps to run the YouTube-8M model inference graph with Web Interface\u003c/h3\u003e\u003ca id=\"user-content-steps-to-run-the-youtube-8m-model-inference-graph-with-web-interface\" class=\"anchor\" aria-label=\"Permalink: Steps to run the YouTube-8M model inference graph with Web Interface\" href=\"#steps-to-run-the-youtube-8m-model-inference-graph-with-web-interface\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCopy the baseline model\n\u003ca href=\"https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view\" rel=\"nofollow\"\u003e(model card)\u003c/a\u003e\nto local.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz\n\ntar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe\"\u003e\u003cpre\u003ecurl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz\n\ntar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuild the inference binary.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \\\n mediapipe/examples/desktop/youtube8m:model_inference\"\u003e\u003cpre\u003ebazel build -c opt --define=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eMEDIAPIPE_DISABLE_GPU=1\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e --linkopt=-s \\\n mediapipe/examples/desktop/youtube8m:model_inference\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRun the python web server.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: pip3 install absl-py\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python mediapipe/examples/desktop/youtube8m/viewer/server.py --root `pwd`\"\u003e\u003cpre\u003epython mediapipe/examples/desktop/youtube8m/viewer/server.py --root \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNavigate to localhost:8008 in a web browser.\n\u003ca href=\"https://drive.google.com/file/d/19GSvdAAuAlACpBhHOaqMWZ_9p8bLUYKh/view?usp=sharing\" rel=\"nofollow\"\u003eHere\u003c/a\u003e\nis a demo video showing the steps to use this web application. Also please\nread\n\u003ca href=\"https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/examples/desktop/youtube8m/README.md\"\u003eyoutube8m/README.md\u003c/a\u003e\nif you prefer to run the underlying model_inference binary in command line.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSteps to run the YouTube-8M model inference graph with a local video\u003c/h3\u003e\u003ca id=\"user-content-steps-to-run-the-youtube-8m-model-inference-graph-with-a-local-video\" class=\"anchor\" aria-label=\"Permalink: Steps to run the YouTube-8M model inference graph with a local video\" href=\"#steps-to-run-the-youtube-8m-model-inference-graph-with-a-local-video\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eMake sure you have the features.pb from the feature extraction pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCopy the baseline model\n\u003ca href=\"https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view\" rel=\"nofollow\"\u003e(model card)\u003c/a\u003e\nto local.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz\n\ntar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe\"\u003e\u003cpre\u003ecurl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz\n\ntar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuild and run the inference binary.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \\\n mediapipe/examples/desktop/youtube8m:model_inference\n\n# segment_size is the number of seconds window of frames.\n# overlap is the number of seconds adjacent segments share.\nGLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \\\n --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \\\n --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4\"\u003e\u003cpre\u003ebazel build -c opt --define=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eMEDIAPIPE_DISABLE_GPU=1\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e --linkopt=-s \\\n mediapipe/examples/desktop/youtube8m:model_inference\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e segment_size is the number of seconds window of frames.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e overlap is the number of seconds adjacent segments share.\u003c/span\u003e\nGLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \\\n --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \\\n --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eView the annotated video.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/article\u003e","renderedFileInfo":null,"shortPath":null,"symbolsEnabled":true,"tabSize":8,"topBannersInfo":{"overridingGlobalFundingFile":false,"globalPreferredFundingPath":null,"showInvalidCitationWarning":false,"citationHelpUrl":"https://docs.github.com/github/creating-cloning-and-archiving-repositories/creating-a-repository-on-github/about-citation-files","actionsOnboardingTip":null},"truncated":false,"viewable":true,"workflowRedirectUrl":null,"symbols":{"timed_out":false,"not_analyzed":false,"symbols":[{"name":"YouTube-8M Feature Extraction and Model Inference","kind":"section_1","ident_start":204,"ident_end":253,"extent_start":202,"extent_end":8174,"fully_qualified_name":"YouTube-8M Feature Extraction and Model 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video","ident_utf16":{"start":{"line_number":176,"utf16_col":4},"end":{"line_number":176,"utf16_col":72}},"extent_utf16":{"start":{"line_number":176,"utf16_col":0},"end":{"line_number":204,"utf16_col":0}}}]}},"copilotInfo":null,"copilotAccessAllowed":false,"csrf_tokens":{"/google-ai-edge/mediapipe/branches":{"post":"f2NDttoncTIfbhZKgKMuiD3pSyouONDFys0TZ2BjgM5cP9mCuflJgNGrj3PfOLHi5Z-srr8MH4UZe8IccgXgVg"},"/repos/preferences":{"post":"h9nChsIk7IXzjGzW2WrypYA_BIiLPsCLg9SI3zjkgQfbM1dq3zqQ720aPPoNNjQ8cUK6Y_z5V1EL8qDsRgYuTg"}}},"title":"mediapipe/docs/solutions/youtube_8m.md at master · 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rel="nofollow">https://developers.google.com/mediapipe/solutions/guide#legacy</a></div></td> <td><div dir="auto">YouTube-8M Feature Extraction and Model Inference</div></td> <td><div dir="auto">MediaPipe Legacy Solutions</div></td> <td><div dir="auto">16</div></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">YouTube-8M Feature Extraction and Model Inference</h1><a id="user-content-youtube-8m-feature-extraction-and-model-inference" class="anchor" aria-label="Permalink: YouTube-8M Feature Extraction and Model Inference" href="#youtube-8m-feature-extraction-and-model-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">{: .no_toc }</p> <details> <summary> Table of contents </summary> {: .text-delta } 1. TOC {:toc} </details> --- <p dir="auto"><strong>Attention:</strong> <em>Thank you for your interest in MediaPipe Solutions. We have ended support for this MediaPipe Legacy Solution as of March 1, 2023. For more information, see the <a href="https://developers.google.com/mediapipe/solutions/guide#legacy" rel="nofollow">MediaPipe Solutions</a> site.</em></p> <hr> <p dir="auto">MediaPipe is a useful and general framework for media processing that can assist with research, development, and deployment of ML models. This example focuses on model development by demonstrating how to prepare training data and do model inference for the YouTube-8M Challenge.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Extracting Video Features for YouTube-8M Challenge</h2><a id="user-content-extracting-video-features-for-youtube-8m-challenge" class="anchor" aria-label="Permalink: Extracting Video Features for YouTube-8M Challenge" href="#extracting-video-features-for-youtube-8m-challenge"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://www.kaggle.com/c/youtube8m-2019" rel="nofollow">Youtube-8M Challenge</a> is an annual video classification challenge hosted by Google. Over the last two years, the first two challenges have collectively drawn 1000+ teams from 60+ countries to further advance large-scale video understanding research. In addition to the feature extraction Python code released in the <a href="https://github.com/google/youtube-8m/tree/master/feature_extractor">google/youtube-8m</a> repo, we release a MediaPipe based feature extraction pipeline that can extract both video and audio features from a local video. The MediaPipe based pipeline utilizes two machine learning models, <a href="https://github.com/tensorflow/models/tree/master/research/inception">Inception v3</a> and <a href="https://github.com/tensorflow/models/tree/master/research/audioset/vggish">VGGish</a>, to extract features from video and audio respectively.</p> <p dir="auto">To visualize the <a href="https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/feature_extraction.pbtxt">graph</a>, copy the text specification of the graph and paste it into <a href="https://viz.mediapipe.dev/" rel="nofollow">MediaPipe Visualizer</a>. The feature extraction pipeline is highly customizable. You are welcome to add new calculators or use your own machine learning models to extract more advanced features from the videos.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Steps to run the YouTube-8M feature extraction graph</h3><a id="user-content-steps-to-run-the-youtube-8m-feature-extraction-graph" class="anchor" aria-label="Permalink: Steps to run the YouTube-8M feature extraction graph" href="#steps-to-run-the-youtube-8m-feature-extraction-graph"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Checkout the repository and follow <a href="https://github.com/google/mediapipe/blob/master/mediapipe/docs/install.md">the installation instructions</a> to set up MediaPipe.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/google/mediapipe.git cd mediapipe"><pre>git clone https://github.com/google/mediapipe.git <span class="pl-c1">cd</span> mediapipe</pre></div> </li> <li> <p dir="auto">Download the PCA and model data.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir /tmp/mediapipe cd /tmp/mediapipe curl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb curl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz tar -xvf /tmp/mediapipe/inception-2015-12-05.tgz"><pre>mkdir /tmp/mediapipe <span class="pl-c1">cd</span> /tmp/mediapipe curl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb curl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb curl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz tar -xvf /tmp/mediapipe/inception-2015-12-05.tgz</pre></div> </li> <li> <p dir="auto">Get the VGGish frozen graph.</p> <p dir="auto">Note: To run step 3 and step 4, you must have Python 2.7 or 3.5+ installed with the TensorFlow 1.14+ package installed.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# cd to the root directory of the MediaPipe repo cd - pip3 install tf_slim python -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph"><pre><span class="pl-c"><span class="pl-c">#</span> cd to the root directory of the MediaPipe repo</span> <span class="pl-c1">cd</span> - pip3 install tf_slim python -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph</pre></div> </li> <li> <p dir="auto">Generate a MediaSequence metadata from the input video.</p> <p dir="auto">Note: the output file is /tmp/mediapipe/metadata.pb</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# change clip_end_time_sec to match the length of your video. python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \ --path_to_input_video=/absolute/path/to/the/local/video/file \ --clip_end_time_sec=120"><pre><span class="pl-c"><span class="pl-c">#</span> change clip_end_time_sec to match the length of your video.</span> python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \ --path_to_input_video=/absolute/path/to/the/local/video/file \ --clip_end_time_sec=120</pre></div> </li> <li> <p dir="auto">Run the MediaPipe binary to extract the features.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="bazel build -c opt --linkopt=-s \ --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \ mediapipe/examples/desktop/youtube8m:extract_yt8m_features GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \ --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \ --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb \ --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb"><pre>bazel build -c opt --linkopt=-s \ --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \ mediapipe/examples/desktop/youtube8m:extract_yt8m_features GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \ --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \ --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb \ --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb</pre></div> </li> <li> <p dir="auto">[Optional] Read the features.pb in Python.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import tensorflow as tf sequence_example = open('/tmp/mediapipe/features.pb', 'rb').read() print(tf.train.SequenceExample.FromString(sequence_example))"><pre class="notranslate"><code>import tensorflow as tf sequence_example = open('/tmp/mediapipe/features.pb', 'rb').read() print(tf.train.SequenceExample.FromString(sequence_example)) </code></pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Inference for YouTube-8M Challenge</h2><a id="user-content-model-inference-for-youtube-8m-challenge" class="anchor" aria-label="Permalink: Model Inference for YouTube-8M Challenge" href="#model-inference-for-youtube-8m-challenge"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">MediaPipe can help you do model inference for YouTube-8M Challenge with both local videos and the YouTube-8M dataset. To visualize <a href="https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt">the graph for local videos</a> and <a href="https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt">the graph for the YouTube-8M dataset</a>, copy the text specification of the graph and paste it into <a href="https://viz.mediapipe.dev/" rel="nofollow">MediaPipe Visualizer</a>. We use the baseline model <a href="https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view" rel="nofollow">(model card)</a> in our example. But, the model inference pipeline is highly customizable. You are welcome to add new calculators or use your own machine learning models to do the inference for both local videos and the dataset</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Steps to run the YouTube-8M model inference graph with Web Interface</h3><a id="user-content-steps-to-run-the-youtube-8m-model-inference-graph-with-web-interface" class="anchor" aria-label="Permalink: Steps to run the YouTube-8M model inference graph with Web Interface" href="#steps-to-run-the-youtube-8m-model-inference-graph-with-web-interface"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Copy the baseline model <a href="https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view" rel="nofollow">(model card)</a> to local.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe"><pre>curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe</pre></div> </li> <li> <p dir="auto">Build the inference binary.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \ mediapipe/examples/desktop/youtube8m:model_inference"><pre>bazel build -c opt --define=<span class="pl-s"><span class="pl-pds">'</span>MEDIAPIPE_DISABLE_GPU=1<span class="pl-pds">'</span></span> --linkopt=-s \ mediapipe/examples/desktop/youtube8m:model_inference</pre></div> </li> <li> <p dir="auto">Run the python web server.</p> <p dir="auto">Note: pip3 install absl-py</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python mediapipe/examples/desktop/youtube8m/viewer/server.py --root `pwd`"><pre>python mediapipe/examples/desktop/youtube8m/viewer/server.py --root <span class="pl-s"><span class="pl-pds">`</span>pwd<span class="pl-pds">`</span></span></pre></div> <p dir="auto">Navigate to localhost:8008 in a web browser. <a href="https://drive.google.com/file/d/19GSvdAAuAlACpBhHOaqMWZ_9p8bLUYKh/view?usp=sharing" rel="nofollow">Here</a> is a demo video showing the steps to use this web application. Also please read <a href="https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/examples/desktop/youtube8m/README.md">youtube8m/README.md</a> if you prefer to run the underlying model_inference binary in command line.</p> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Steps to run the YouTube-8M model inference graph with a local video</h3><a id="user-content-steps-to-run-the-youtube-8m-model-inference-graph-with-a-local-video" class="anchor" aria-label="Permalink: Steps to run the YouTube-8M model inference graph with a local video" href="#steps-to-run-the-youtube-8m-model-inference-graph-with-a-local-video"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Make sure you have the features.pb from the feature extraction pipeline.</p> </li> <li> <p dir="auto">Copy the baseline model <a href="https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view" rel="nofollow">(model card)</a> to local.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe"><pre>curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe</pre></div> </li> <li> <p dir="auto">Build and run the inference binary.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \ mediapipe/examples/desktop/youtube8m:model_inference # segment_size is the number of seconds window of frames. # overlap is the number of seconds adjacent segments share. GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \ --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \ --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4"><pre>bazel build -c opt --define=<span class="pl-s"><span class="pl-pds">'</span>MEDIAPIPE_DISABLE_GPU=1<span class="pl-pds">'</span></span> --linkopt=-s \ mediapipe/examples/desktop/youtube8m:model_inference <span class="pl-c"><span class="pl-c">#</span> segment_size is the number of seconds window of frames.</span> <span class="pl-c"><span class="pl-c">#</span> overlap is the number of seconds adjacent segments share.</span> GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \ --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \ --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4</pre></div> </li> <li> <p dir="auto">View the annotated video.</p> </li> </ol> </article></div><button hidden=""></button></section></div></div></div> <!-- --> <!-- --> </div></div></div><div class="Box-sc-g0xbh4-0"></div></div></div></div></div><div id="find-result-marks-container" class="Box-sc-g0xbh4-0 cCoXib"></div><button hidden="" data-testid="" data-hotkey-scope="read-only-cursor-text-area"></button><button hidden=""></button></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-app> </turbo-frame> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex 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hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>