youtube-8m | Starter code for working with the YouTube-8M dataset | Machine Learning library
kandi X-RAY | youtube-8m Summary
kandi X-RAY | youtube-8m Summary
This repo contains starter code for training and evaluating machine learning models over the YouTube-8M dataset. This is the starter code for our 3rd Youtube8M Video Understanding Challenge on Kaggle and part of the International Conference on Computer Vision (ICCV) 2019 selected workshop session. The code gives an end-to-end working example for reading the dataset, training a TensorFlow model, and evaluating the performance of the model.
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Top functions reviewed by kandi - BETA
- Run the model
- Returns the heap at the given index
- Calculate gap between predictions and predicted predictions
- Calculate the average precision of predictions
- Find a class by its name
- Run inference
- Format a sequence of predictions
- Extracts segmentation from the given dataset
- Get input tensors
- Creates a model
- Return a list of mapped classes
- Get epoch info
- Evaluate the model flags
- Retrieves a list of feature names and sizes
- Read a TFRecord reader
- Convert to a CSV row
- Get a reader for YT8M frame features
- Quantize features
- Start the parameter server
- Get the epoch info
- Prepare a TFRecordReader from a file
- Retrieves the list of feature names and sizes
- Extract features from an image
- Reads segment predictions from a file
- Builds inputs and outputs
- Generator that yields frames from a video file
- Reads labels from given pattern
- Find a class by name
youtube-8m Key Features
youtube-8m Examples and Code Snippets
Lines of Code : 71License : Permissive (MIT)
Copy链接: https://pan.baidu.com/s/1-t5Hb2bpUOdQmLFp9Kg1kw 提取码: di43
curl data.yt8m.org/download.py | partition=2/frame/train mirror=us python
curl data.yt8m.org/download.py | partition=2/frame/validate mirror=us python
curl data.yt8m.org/download.py | par JOB_TO_EVAL=yt8m_train_video_level_logistic_model
JOB_NAME=yt8m_eval_$(date +%Y%m%d_%H%M%S); gcloud --verbosity=debug ml-engine jobs \
submit training $JOB_NAME \
--package-path=youtube-8m --module-name=youtube-8m.eval \
--staging-bucket=$BUCKET_NAME BUCKET_NAME=gs://${USER}_yt8m_train_bucket
# (One Time) Create a storage bucket to store training logs and checkpoints.
gsutil mb -l us-east1 $BUCKET_NAME
# Submit the training job.
JOB_NAME=yt8m_train_$(date +%Y%m%d_%H%M%S); gcloud --verbosity=debug Community Discussions
Trending Discussions on youtube-8m
QUESTION
I am getting started with Google's Audioset. While the dataset is extensive, I find the information with regards to the audio feature extraction very vague. The website mentions
128-dimensional audio features extracted at 1Hz. The audio features were extracted using a VGG-inspired acoustic model described in Hershey et. al., trained on a preliminary version of YouTube-8M. The features were PCA-ed and quantized to be compatible with the audio features provided with YouTube-8M. They are stored as TensorFlow Record files.
Within the paper, the authors discuss using mel spectrograms on 960 ms chunks to get a 96x64 representation. It is then unclear to me how they get to the 1x128 format representation used in the Audioset. Does anyone know more about this??
...ANSWER
Answered 2018-Aug-13 at 08:52They use the 96*64 data as input for a modified VGG network.The last layer of VGG is FC-128, so its output will be 1*128, and that is the reason.
The architecture of VGG can be found here: https://github.com/tensorflow/models/blob/master/research/audioset/vggish_slim.py
QUESTION
The Youtube-8m download webpage provides the following curl instructions:
...ANSWER
Answered 2017-Nov-02 at 15:50That script is intended to run in a *nix (Unix or linux or ...) environment.
Do you have the bash for windows installed? If so, that is the quick solution, just run the script/cmds in that environment (and make sure that which python returns the correct /path/to/preferred/version_of/python).
To explain/expand on what that code does, *nix allows setting env vars specific to the command being run at the end of the line. An alternate way to "say" the same thing as the code you have included in *nix is
QUESTION
As denoted here youtube-8m tf-records are saved with the format comes at the end of my question.I write a code to extract features. but there is a problem. the code can read all elements in features successfully but it is not able to read feature_lists. in fact, the example does not include features_list at all and I get an error while I try to access it. How can I read the feauures_list. I attach Data format, My code and the output :
...ANSWER
Answered 2017-Sep-14 at 12:23Instead of
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