User guide for the AI Labeling, Video Segmentation, Video Splitting, and Fine Tuning Studios in INFINIWORKFLOW.
INFINIWORKFLOW provides four tightly integrated studios for dataset creation and machine learning preparation: the AI Labeling Studio and Video Segmentation Studio for building datasets, the Video Splitting Studio for breaking up long or high-speed sequences, and the Fine Tuning Studio for training models on the result.
The AI Labeling Studio is a specialized annotation environment for compiling high-quality ML dataset corpora. It combines an interactive vector/raster edit canvas, sequential frame propagation, and automated checkpoint management for pixel-accurate labeling.
Annotate frames by clicking the Edit button on an extracted frame directory. Once in the viewer, select your active class, use a path tool (such as Add Rectangle or polygon points), and click and drag to define your shape. Switch classes as needed, and save your progress.
To keep your dataset's annotation status fully visible, the AI Labeling Studio adds a live, auto-updating Summary & Report column to the results list (right panel):
Apple, Banana), so you don't need to open a frame to see what's already annotated.
AUTO badge, marking them as machine-generated candidates pending review.
Example: the Summary & Report column - a normal frame, an automatically tracked one, and one flagged invalid
After compiling labeled sequences, use the Fine Tuning controls to compile native models (e.g. YOLO, UNet) based on your custom annotations.
The Video Segmentation Studio is the central control room for managing raw media sources, converting video into discrete frames, and preparing frame sequences. Extraction and preprocessing run asynchronously in the background, so large jobs don't block the interface.
Your video corpus is managed inside an interactive spreadsheet overview, providing clear indicators for thumbnail preview, sequence filename, and lifecycle actions:
Example: the video corpus table - a raw file awaiting processing, a processed sequence, and one of its preprocessed variations, each with its own set of actions
Each video entry displays a set of actions depending on whether it's a raw file, a processed sequence, or a preprocessed variation:
| Icon | Action | Function & Backend Flow |
|---|---|---|
| Open Workspace | Opens the frame-by-frame canvas editor for an already processed video directory. | |
| Process Video | Launches frame extraction for a raw video file. Videos longer than 1024 frames redirect automatically to the Video Splitting Studio to define target boundaries first. | |
| Re-Extract / Refresh | Clears the previous frames and re-runs extraction - useful when the source file changes or extraction parameters are adjusted. | |
| Create Variation (Option B) | Creates a lightweight copy of the entry, carrying over its annotations. If a copy already exists, you're prompted to Regenerate Variations instead, which resets and overwrites the existing folder rather than cluttering the directory with duplicates. | |
| Apply Preprocessing Workflow | Opens the variations setup interface to configure image preprocessing nodes that synthetically expand your dataset's variance. | |
| Rename | Safely renames directories, database entries, and dependent checkpoints across the filesystem. | |
| Delete | Permanently deletes extracted frames, caches, and database configurations. |
To keep labeling pipelines interactive, long-sequence frame propagation using SAM2 can be offloaded to a background thread directly from the annotation editor:
To expand your annotations synthetically, the Variations Panel offers single-click duplication and bulk propagation across the workspace:
- Copy directory intact on disk, letting you reset your augmentation pipeline without losing your annotations.
Task 2 of 5) and completion percentage in real time.
To fast-track pipelines involving large video libraries, the Video Segmentation Studio integrates Batch Processing:
Example: the Batch Preprocessing dialog, configured for a four-way parallel run with distribution enabled
To customize frame handling and receive remote alerts during long batch runs, the Video Studio Settings modal provides the following options:
0 - 1000, default: 1) to filter out redundant, static, or identical frames during video processing, reducing annotation workload by eliminating duplicate image sequences.
assets/<export name> - useful when you want trained weights kept alongside the project rather than under the shared assets tree.
The Video Splitting Studio is a dedicated interface for managing long or high-speed video sequences. Because annotating thousands of frames linearly is impractical, the Splitter lets you clip precise frame spans and compile them into shorter, manageable chunks.
You don't need to launch the Splitter manually. Inside the Video Segmentation Studio, clicking Process on any raw video file with more than 1024 frames automatically opens the full-screen Video Splitting Studio interface. (The Splitter's own default output chunk size, 240 frames, is a separate, independently adjustable setting - see Custom Chunking Size below.)
clip_1_split.mp4, clip_2_split.mp4).
Fine Tuning provides a web-native interface to train, validate, and optimize state-of-the-art machine learning models directly on your custom-labeled datasets. Rather than exporting your data and writing boilerplate training scripts, you can start training in one click, monitor metrics in real time, and deploy the resulting weights immediately within your workspace.
Before starting training, make sure your annotated dataset meets the system's requirements. Training is gated by the following validation checks:
class_0: cell, class_1: nucleus) to generate a compatible YAML configuration.
Before clicking Train Now, the panel exposes the following fields (each is remembered between sessions, so you only need to set them once per project):
10 to 90 (default 80) - the remainder is held out for validation, so you can monitor for overfitting.
50). There's no enforced maximum - more epochs generally means more training time and a higher risk of overfitting a small dataset.
auto, SGD, Adam, or AdamW. auto lets the training framework pick based on your dataset and model.
640). This is independent of the 1024px resolution the labeling editor's SAM-based tracking uses - see the Performance tip below.
For a model to generalize well, it should train on a variety of clips. The Fine Tuning Studio makes it easy to compile a multi-video dataset:
When you start a project-wide, multi-sequence training run in batch mode, the selection dialog turns into a standalone training setup panel:
1 to 100) - if a batch run crashes or is interrupted partway through, it resumes from the last completed stage instead of restarting training from scratch.
Example: the Batch Training dialog - epoch staging, the standalone/distribute options, and the launch button
Once you trigger the run, the dashboard switches to a live telemetry and performance view:
Example: the live training monitor - progress, current metrics, and the streaming log
When training concludes, your optimized model is packaged and made available for immediate production:
best.pt) along with evaluation metrics tables.
Need to run training on a dedicated GPU cluster or your own local machine? The platform provides full transparency and exportability: