How sasha071201/tensorflow-lite-object-detection works
This repository is a small utility toolkit around a TensorFlow Lite object-detection workflow. It does not contain a full application or model codebase; instead, it appears to focus on preparing data and evaluating detection results for an external training/inference pipeline.
1. Repository purpose
The repo is centered on dataset preparation and evaluation for object detection, likely as support code for TensorFlow / TensorFlow Lite models.
2. Main boundary: data-prep utilities only
All executable logic is under util_scripts/. There are no model definitions, serving components, UI layers, or deployment manifests in the tree provided.
3. Input data boundary
The scripts likely consume a labeled image dataset plus annotation metadata. The exact annotation format is not shown, but the presence of CSV and TFRecord generators suggests the source data is first normalized into tabular form.
4. Dataset splitting subsystem
util_scripts/train_val_test_split.py is the partitioning step. Its role is to divide the dataset into training, validation, and test subsets, which is the first stage in the pipeline and a clear architectural boundary between raw data and model-ready data.
5. Annotation normalization subsystem
util_scripts/create_csv.py likely converts annotation data into a CSV representation. In an object-detection pipeline, this usually serves as an intermediate canonical format that is easier to validate, inspect, and transform.
6. TFRecord generation subsystem
util_scripts/create_tfrecord.py is the TensorFlow-specific packaging step. TFRecord is the input format commonly used by TensorFlow Object Detection workflows, so this script is the handoff point from generic dataset metadata into TensorFlow training input.
7. Evaluation subsystem
util_scripts/calculate_map_cartucho.py appears to compute mAP-style detection metrics, probably using the “Cartucho” implementation or a variant of it. This suggests a separate evaluation path from training, used to score predictions against ground truth.
8. End-to-end data flow
The likely flow is: raw images + annotations -> split into train/val/test -> transform annotations to CSV -> convert to TFRecord -> train/evaluate object detector externally -> compute detection metrics with the mAP script.
9. External runtime assumptions
Because the repo is named for TensorFlow Lite, these utilities probably support a broader TensorFlow/TFLite object-detection pipeline, but the actual model training, export to TFLite, and runtime inference are not present here.
10. Architectural role of the scripts
Each script is a one-purpose batch utility rather than a service or library module. The repository is therefore best diagrammed as a preprocessing and evaluation layer feeding an external ML training pipeline.
11. Likely integration points
The key integration boundary is file-based: images, annotations, CSVs, TFRecords, and prediction outputs. There is no evidence of network APIs, databases, queues, or long-running workers in the provided tree.
12. What to show in an architecture diagram
A diagram should emphasize the linear dataset pipeline, the TensorFlow-specific TFRecord boundary, and the separate evaluation step for mAP. It should also mark the absence of training/inference code in this repo, since those likely live elsewhere.
Main entry points: calculate_map_cartucho.py Calculate mAP · train_val_test_split.py Split data · create_csv.py Create CSV · create_tfrecord.py Create TFRecord
Components 11
Utility scripts
Batch preprocessing and evaluation scripts in this repository.
- Split data · Divides the dataset into train, validation, and test subsets.
util_scripts/train_val_test_split.py - Create CSV · Converts annotation data into a CSV intermediate format.
util_scripts/create_csv.py - Create TFRecord · Packages CSV or annotation data into TensorFlow TFRecord files.
util_scripts/create_tfrecord.py - Calculate mAP · Computes detection evaluation metrics using a Cartucho-style mAP workflow.
util_scripts/calculate_map_cartucho.py
External pipeline
Inputs and outputs that live outside this repo, including datasets and model training/inference.
- Raw dataset · Labeled images plus annotation metadata consumed by the utilities.
- Train/val/test · Partitioned dataset subsets produced by the split step.
- Annotation CSV · Tabular intermediate representation of detection annotations.
- TFRecords · TensorFlow-ready serialized records used by downstream training.
- Trainer · Training/export workflow that is not implemented in this repository.
- Predictions · Model detections produced by the external pipeline for evaluation.
- mAP report · Detection quality metrics computed from predictions and ground truth.
Connections 11
- Raw dataset → Split data: input · Dataset and annotations are fed into the splitting step.
- Split data → Train/val/test: outputs · Produces train, validation, and test partitions.
- Train/val/test → Create CSV: annotate · Split data is normalized into CSV form.
- Create CSV → Annotation CSV: writes · Emits a canonical CSV annotation dataset.
- Annotation CSV → Create TFRecord: feed · CSV annotations are converted into TensorFlow records.
- Create TFRecord → TFRecords: writes · Produces TFRecord files for training input.
- TFRecords → Trainer: train · Downstream training consumes TFRecords outside this repo.
- Trainer → Predictions: infer · Trained model outputs detections for evaluation.
- Raw dataset → Calculate mAP: ground truth · Ground truth annotations are used when scoring detections.
- Predictions → Calculate mAP: predictions · Detection outputs are evaluated against the ground truth.
- Calculate mAP → mAP report: reports · Produces mAP-style evaluation metrics and summaries.