Higgs | Higgs JavaScript Virtual Machine | Code Editor library

You will experience generation loss when using lossy encoders. However, if your encoding is done well your viewers may not even notice.

x264 does not re-use information from the compressed bitstream of the source (such as motion vectors and frame types). Your compression artifacts present in the source are part of the raster image and are not re-utilized for compression. It's just noise.

Don't overthink it. ffprobe is not going to provide any useful metric to optimally choose a quality.

1. Choose a -crf value. Generally choose the highest value (lowest quality) that provides an acceptable quality. Choose the value by watching the results.
2. Choose the slowest -preset you have patience for.
3. Use this -crf and -preset for the rest of your videos.

See FFmpeg Wiki: H.264 for more info on these options.

...or keep them as WTV and don't bother with re-encoding.

Distribution strategies were introduced as part of TF2 to help distribute training across multiple GPUs, multiple machines or TPUs with minimal code changes. I'd recommend this guide to distributed training for starters.

Specifically creating a model under the TPUStrategy will place the model in a replicated (same weights on each of the cores) manner on the TPU and will keep the replica weights in sync by adding appropriate collective communications (all reducing the gradients). For more information check the API doc on TPUStrategy as well as this intro to TPUs in TF2 colab notebook.

As per the official documentation of Land Marks Classifier,

Inputs are expected to be 3-channel RGB color images of size 321 x 321, scaled to [0, 1].

But from Your Dataset, file format is tfrecord.

When we use Transfer Learning and when we want to reuse the Models either from TF Hub or from tf.keras.applications, our data should be in the predefined-format as mentioned in the documentation.

So, please ensure that your Dataset comprises of Images and resize Image Array to (321,321,3) for the TF Hub Module to work.

The git status you've done shows there are no changes between your file and HEAD (c66c96b1336848803d55fc002942c42c07a701e7). There are changes leading up to 71ef61c, and those changes seem to be on another branch, otherwise git log synthesize.py would show a more recent change that reflects what's in your workspace.

You could always use gitk to get a visual.

Having said that, it's possible there are two of these files. Use find * -name synthesize.py to find out. Just allowing for the possibility that Matt's on the right track, but I lean toward it being multiple branches.

The problem is library incompatibility. This docker container have solved my problem:

https://github.com/Kaggle/docker-python/commit/a6ba32e0bb017a30e079cf8bccab613cd4243a5f

I think you need to replace site with page in your second code snippet, see the handling for variables.

• site: global website (e.g. _config.yml)
• page: current page

Additional I dropped the array index [0].

It seems it is working now. R client version 3.30.0.1 with server version 3.30.0.1. Also tried with Python version 3.30.0.7 and server version 3.30.0.7 and it started working. Marvelous. The problem was caused by a version mismatch between the client and the server, as the python client was updated to 3.30.0.7 while the latest server for docker was 3.30.0.6.

• Avoid calling new and delete explicitly to dynamically allocate memory. You don't need it in combination with STL containers. A container allocates and manages the memory on the heap.
• The for loop is not necessary. For each element you read you overwrite all elements with the last read element.

• You print the first element from the vector for each element you read:

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