> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/ai-blocks/object-detection-custom.md).
# Object Detection - Custom
This function block detects objects in images using custom model files you provide. It lets you load a detector (weights, config, and class list), choose which classes to detect, and control detection sensitivity with a simple slider. The block outputs an annotated image plus structured detection data for further processing.
## 📥 Inputs
`Image Any` The image to be analyzed for object detections.
## 📤 Outputs
`Image Any` Annotated image with detection boxes and labels.
`Object Count` Number of detected objects.
`Object Locations` List of detected object center positions (multiple outputs allowed).
`Object Sizes (w, h)` Width and height for each detected object (multiple outputs allowed).
`Object Class` Class name for each detected object (multiple outputs allowed).
`Rectangles` Bounding rectangle coordinates for each detection (multiple outputs allowed).
## 🕹️ Controls
`Open Weight File` Button to choose the model weights file.
`Open Config File` Button to choose the model configuration file.
`Open Class File` Button to choose a text file listing class names.
`Class Names` Table where available classes are listed and you can enable/disable each class.
`Confidence Threshold %` Slider to set detection confidence sensitivity (higher = stricter).
## ⚙️ Running mechanism
* The block requires three resources to run: a weight file, a config file, and a class list file. Load them using the three file buttons.
* After the files are provided, the detector is initialized and remains ready until you change the files.
* When an image is provided to the input, the block runs the detector on the image and outputs:
* an annotated image with boxes/labels,
* count and positions,
* sizes, classes, and rectangle coordinates for each detection.
* Changing weight or config files triggers reloading of the detector so new models are used for subsequent evaluations.
## ✨ Features
* Load-your-own model support (weights, config, and class list).
* Select which classes to detect via an easy checklist.
* Adjustable confidence threshold with immediate effect.
* Outputs both visual results and structured data (counts, positions, sizes, rectangles).
* Works with multiple detected objects and returns results in list form for downstream blocks.
## 📝 Usage instructions
1. Click `Open Weight File` and select the model weight file.
2. Click `Open Config File` and select the model configuration file.
3. Click `Open Class File` and select the class names file. The class list will populate automatically.
4. Enable only the classes you want to detect in the `Class Names` table.
5. Adjust `Confidence Threshold %` to balance sensitivity vs false positives.
6. Provide the image to the `Image Any` input and run the scenario to get annotated image and detection data.
## 💡 Tips and Tricks
* To preview results interactively, connect the `Image Any` output to the `Show Image` block.
* If input images are very large and detection is slow, insert `Image Resizer` before this block to lower resolution and increase processing speed.
* Limit analysis to a specific area by using `Image ROI` or `Image ROI Select` upstream so the detector focuses only on regions of interest.
* For tracking detections across frames, link this block’s detection outputs to `Object_Detection_Tracker`.
* If you need custom drawing or overlays beyond the built-in annotations, use `Draw Detections` with the detection rectangles and counts provided by this block.
* Save interesting frames with detections using `Image Logger` or `Image Write` / `Record Video` for later review.
* Monitor performance and GPU usage with `GPU Statistics` when running heavier models.
(hint: enable only required classes and increase the confidence threshold to reduce false positives and speed up post-processing)
## 🛠️ Troubleshooting
* Missing model files: Ensure all three files (weight, config, class list) are selected. The block cannot run without them.
* No detections: Try lowering `Confidence Threshold %` or enable more classes in the class table. Also verify the class names file matches the model.
* Too many false detections: Increase `Confidence Threshold %` and enable only the relevant classes to reduce noise.
* Slow performance: Reduce input image size with `Image Resizer` or use smaller models; consider offloading to a GPU if available and monitor with `GPU Statistics`.
* Incorrect class names or mismatched files: Verify the class file corresponds to the loaded model (class order and names must match the model training).
## 🔗 Recommended block combinations
* `Show Image` — Preview annotation output.
* `Image Resizer` — Speed up detection on large images.
* `Image ROI` / `Image ROI Select` — Focus detection on specific areas.
* `Object_Detection_Tracker` — Track detected objects over time.
* `Draw Detections` — Custom visualization using detection rectangles and counts.
* `Image Logger` / `Image Write` / `Record Video` — Save annotated results for audit or later analysis.