Supported Data

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Comparing file formats

The following video tutorial explains all the different file formats in Encord.

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Comparison of image file types

* Data is lost during the creation of an image sequence when images of varying dimensions are combined into a single image sequence. For more information about this, please see the section on image sequences below.

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Supported file formats

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Single images

Single images are individual images that are uploaded to Encord as separate files. Each single image constitutes its own data package, hence images will be listed individually.

We support the following file types for single images:

* Due to Chromium-based browser limitations, TIFF files can only be viewed in the label editor using the Safari browser.

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Image groups

Image groups are collections of images that are grouped together into a single data unit. They can contain images of varying orientations and no data is lost in the process of creating an image group.

We support the following file types for image groups:

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Image sequences

Image sequences are collections of images that are grouped together into a single data unit, and annotated in the same way as videos. As a result, image sequences are able to make use of powerful machine learning features such as automated labeling.

We support the following file types for image sequences:

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Creating image sequences

Image sequences can be created when importing files into Encord, or by batching images already sorted in Files into image sequences.

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Videos

Video files can be uploaded onto the Encord platform and annotated frame by frame, enabling you to track elements within the video. Even though frames are treated as individual images when being annotated, the video itself constitutes a single data package.

The following video formats, contains, and codecs are supported.

For optimized performance we recommend using the following video formats:

• .mp4 file with h.265.
• .mp4 with vp9.

File uploads must be less than 1 GB or 100,000 frames (1hr at 30FPS). Please see our guidelines for optimal performance for more details.

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Resizing videos

Videos can be re-sized using ffmpeg to fit the required specifications in the following ways:

• Lowering their resolution.
• Lowering their frame rate.
• Specify a key frame interval.

Follow the steps below to reduce the video file size by adjusting the resolution, as well as the number of frames per second (fps).

1. Download and install ffmpeg.
2. Open Command Line (on Windows) or Terminal (on Mac & Linux).
3. Run the command shown below, substituting the values for your desired resolution as well as fps, followed by the path to the video you’d like to downsize.

ffmpeg -i normal-video.mp4 -c:v libx265 -an -movflags faststart -tune zerolatency -f mp4 -vf "scale=-1:1080,fps=30" </Users/Desktop/normal-video-downsampled.mp4>

The example above resizes normal-video-downsampled.mp4 to 30 fps and a resolution of -1:1080px. This means that the video height is set to 1080px, while the -1 adjusts the width so that the aspect ratio remains the same as in the original.

The following example imports the video by generic key frame intervals using the global variable -g (every 30 frames).

ffmpeg -i normal-video.mp4 -c:v libx265 -an -movflags faststart -tune zerolatency -f mp4 -g 30 </Users/Desktop/normal-video-downsampled.mp4>

The following example imports the video by key frame intervals by specific codec -x264opts keyint= (every 30 frames for codec H264).

ffmpeg -i normal-video.mp4 -an -movflags faststart -tune zerolatency -f  -c:v libx264 -x264opts keyint=30" </Users/Desktop/normal-video-downsampled.mp4>

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Pixel aspect ratio

To ensure accurate labels, only videos with a pixel aspect ratio of 1:1 should be used in Encord.

To check the pixel aspect ratio of your file, run the following ffprobe command, where input-file.mp4 is the full file path to the file you want to output the pixel aspect ratio for. ffprobe refers to pixel aspect ratio as ‘sample aspect ratio (SAR)‘.

ffprobe -v error -select_streams v:0 -show_entries stream=sample_aspect_ratio -of default=noprint_wrappers=1:nokey=1 input-file.mp4

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Converting videos to images

In machine learning, most infrastructures and codebases are tailored for image data rather than video. Therefore, when working with videos, a common prerequisite is to convert videos into a series of images. This process is essential for training ML models that do not directly support video inputs. We outline two approaches for extracting images from videos, each catering to different needs.

Approach 1: Extracting Every Frame as an Image

This method involves decompressing each frame of a video into separate image files. It’s straightforward and useful when you need to analyze or process every frame individually. Navigate to the directory your video is in and run the following command in your Terminal, replacing your_video.mp4 with the name of your video file, and /path/to/output/dir/frame with the path to your output directory.

ffmpeg -i your_video.mp4 -start_number 0 /path/to/output/dir/frame_%d.png -hide_banner

Approach 2: In-Memory Frame Iteration with Python

For scenarios where saving individual image files is unnecessary, you can iterate over video frames directly in memory using Python. This method is efficient for immediate processing or conversion to formats like TFRecords.

import av

# Open the video file
with av.open('your_video.mp4') as container:
    # Loop through streams in the container
    for frame in container.decode(video=0):
        # Process the frame. For example, convert the frame to a PIL Image and show it
        img = frame.to_image()
        img.show()

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DICOM & NIfTI

Encord provides native support for Digital Imaging and Communications in Medicine (DICOM) & Neuroimaging Informatics Technology Initiative (NIfTI) browser rendering and data annotations.

Our Ontologies allow you to create any type of labeling protocol - for example RECIST, which requires measuring the longest diameter of a lesion.

With the DICOM editor, you can:

• Annotate modalities such as CT, X-ray, MRI, , Mammograms, and Retinal Fundoscopy images.
• Label using any annotation type in 2D (with 3D in the works), and seamlessly toggle between axial, coronal, and sagittal views.
• Render 20,000 pixel intensities and set custom window widths & levels.
• Natively display DICOM metadata.
• Track and interpolate objects between slices.
• Reduce manual annotations with automation features.

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Multi-frame DICOM Support

We support multi-frame DICOMs, making it even easier and faster to load data onto our platform! Multi-frame DICOM support can save time and streamline workflows due to a reduced amount of header data repetition.

Simply upload your multi-frame DICOM files like you would any other files - either via the user interface, or using our python SDK.

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Caching DICOM data

Caching allows frequently accessed DICOM files to be stored securely in your local browser memory, allowing them to be retrieved quickly and efficiently. As a result you can expect faster load times when accessing medical imaging data.

This is particularly beneficial for busy healthcare professionals who require quick access to patient records and diagnostic imaging.

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Re-encode your data

Re-encoding your videos means bringing them in line with browser requirements to avoid frame synchronization issues and ensure accurate labels.

Your video needs re-encoding if you see a red warning icon after successfully uploading your video. Hover over the icon to read what issues we have found with the video.

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Re-encode (Automatically Fix)

Automatically fix detected issue re-encoding serves common frame synchronization issues and ensures there is no quality loss during re-encoding. If for example the only issue is that audio is present in the video, auto re-encoding only removes the audio while keeping the video as-is. A notification at the top of your screen appears when re-encoding is complete.

You can perform this action from Files and Datasets:

Files

Datasets

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Re-encode (Force Full)

Force full re-encoding converts the video to a specific frame rate and video codec regardless of why frame synchronization issues are occurring. A full re-encoding takes longer and can lead to issues with existing labels.

You can perform this action from Files and Datasets:

Files

Datasets

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Re-encode locally

Some cases, such as corrupted metadata, might require you to re-encode your data locally before uploading them to the Encord platform.

Use the following ffmpeg command, replacing video.mp4 with the name of the file you want to re-encode, and re-encoded-video.mp4 with the name you want the re-encoded file to have:

ffmpeg -err_detect aggressive -fflags discardcorrupt -i video.mp4 -c:v libx264 -movflags faststart -an -tune zerolatency re-encoded-video.mp4

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Frame Synchronization Issues

To maintain precise label alignment, our servers use FFmpeg to extract video frames individually. This frame-by-frame extraction assigns each label to a specific frame index, ensuring accurate mapping of labels within the video. When a video is embedded in the browser, frame display is managed by the browser itself, and may not consistently match the video’s frame rate.

If we detect any of the following frame synchronization issues after uploading a video your video must be re-encoded.

ffprobe -i $YOUR_FILE -show_streams -select_streams a -loglevel error

ffprobe -show_packets -select_streams v -read_intervals %+3 -of json $YOUR_FILE

    [...]
    "packets": [
        {
            "codec_type": "video",
            "stream_index": 0,
            "pts": -16384,
            "pts_time": "-1.066667",
            "dts": -16896,
            "dts_time": "-1.100000",
            "duration": 512,
            "duration_time": "0.033333",
            "size": "71396",
            "pos": "37476",
            "flags": "KD"
        },
        [...]

You may also see a frame synchronization warning in the Label Editor when trying to open the video task:

For a more detailed check, you can use ffprobe (part of the FFmpeg suite) to inspect your video’s properties. Download FFmpeg here.

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Existing labels before re-encoding

In certain scenarios, you may have pre-existing labels that you want to upload to our platform. If we advise you to re-encode the video, there is a possibility that your new labels might become misplaced. This situation can arise if:

• The labels were created incorrectly before, because of frame synchronization issues on other platforms
• You have labels for a variable frame rate video and now the frame rate has changed

If you upload labels and need to re-encode your video, we strongly encourage you to double-check the accuracy of your pre-existing labels after re-encoding the video.

If you believe that your labels are misaligned, please do reach out to the Encord team, so we can help you with the uploading process.

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Fixing cloud storage buffering issues

A number of factors determine how fast remote content is rendered and displayed in a web browser:

• Geographical distance from host bucket.
• Local internet speed.
• Use of a Virtual Private Network (VPN).
• The bitrate of the video file being requested.

While these factors are outside of Encord’s influence, we have put together a set of common issues when loading data from private cloud, along with possible solutions to improve your user experience.

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Geographical distance from host

The physical distance between you and the server hosting your data will affect how quickly content can be accessed. If, for example, an Amazon S3 bucket is hosted in the ‘EU (London) eu-west-2’ region, then users from India, or Brazil might experience latency issues due to data having to travel a long way.

The solution is to make sure that users always connect to their closest server, reducing the time it takes for data to travel from one place to another - however, setting this up differs between different cloud storage providers.

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Local internet speed

Test your internet connection to make sure it is fast enough to download your video from cloud storage without buffering. Consistently low internet speeds might mean your provider isn’t providing you with high enough internet speeds to load large quantities of data from your cloud storage.

You can see the speed at which a video is being downloaded at the bottom of the ‘editor pane’ in the ‘label editor’ on Encord’s platform. It also displays the download speed required to display a video without buffering.

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VPNs

VPNs can slow down your connection by requiring the data travel through the VPN’s server before arriving to you.

We recommend you turn off your VPN when using Encord’s platform if you are experiencing buffering issues.

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High bitrates in videos

Video content itself varies widely in terms of frames rate, compression, definition and size. The most important factor when determining how quickly a remote video will load in the browser is the bitrate. The higher the bitrate, the more data will need to be loaded per second of video.

Lowering a video’s bitrate will reduce the time it takes to load from your cloud storage. This can be achieved using tools such as FFmpeg. Please see our section on resizing videos for information on how this can be achieved.

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Supported browsers

We recommend the Google Chrome or Brave web-browsers when using Encord.

Additionally, we recommend disabling hardware acceleration in Chrome. Hardware acceleration can introduce another layer of uncertainty to video rendering in the browser, potentially leading to unexpected behavior. By disabling it, you can ensure a more stable and consistent experience when working with videos on our platform.

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HEVC (High Efficiency Video Coding)/H265 Support

HEVC (High Efficiency Video Coding), also known as H265, requires at least the following hardware specs for a good experience:

• 4GB of RAM
• Mac: 2016 (or later) or any M series
• Windows: AMD Ryzen or Intel 7th gen