⭐ Welcome to the Ikomia API Notebooks Collection! Explore our wide range of Jupyter notebooks for computer vision tasks, including object detection, segmentation, classification, and more.
| Notebook | Description |
|---|---|
| Make a simple workflow | Learn how to use the Ikomia API to easily prototype some Computer Vision workflows |
| Ikomia SCALE welcome on board | Shows how to get started with Ikomia SCALE, a SaaS platform for deploying Ikomia workflows on dedicated endpoints |
| Ikomia SCALE - Deploy FLUX.1 | End-to-end example for deploying text-to-image models on dedicated endpoints |
| Notebook | Description |
|---|---|
| Run Camera Stream Processing | Show how run video stream for real-time processing using YOLOv7 as an example |
| Run Face Detection and Blur | Shown how to create a workflow for face detection with Kornia and blurring with OpenCV |
| Use Detectron2 Object Detection | Shows how to use Detecton 2 for object detection |
| Use MMDetection | Shows how to run the MMDetection toolkit |
| Run Grounding Dino | Shows how to Grounding Dino for Zero-Shot object detection |
| Train YOLOR construction safety dataset | Shows how to train to fine-tune a YOLOR model on a safety construction dataset |
| Train YOLO v7 | Shows how to train to fine-tune a YOLOv7 model on aerial airplane dataset |
| Train YOLO v9 | Shows how to train to fine-tune a YOLOv9 model on a basketball dataset |
| Use YOLO-World | Shows how to use YOLO-World for zero-shot object detection |
| Train YOLO v10 | Shows how to train to fine-tune a YOLOv10 model on a Chess pieces dataset |
| Use OWLv2 | Shows how to use OWLv2 for zero-shot object detection |
| Notebook | Description |
|---|---|
| Run DeepLabPlus | Shows how to run DeepLabV3 + for semantic segmentation |
| Run Mask R-CNN | Shows how to run Mask R-CNN for segmentation |
| Run MnasNet | Shows how to run MnasNet for instance segmentation |
| Run YOLACT | Shows how to run YOLACT for instance segmentation |
| Run SparseInst | Shows how to run SparseInst for instance segmentation |
| Run YOLOP v2 | Shows how to run YOLOPv2 road, lane segmentation and vehicule detection |
| Use MMSegmentation | Shows how to use the MMsegmentation toolkit |
| Use MobileSAM | Shows how to run the Faster Segment Anything Model |
| Train YOLO v8 segmentation | Shows how to fine-tuned YOLOv8 instance segmentation on a coral dataset |
| Use YOLO v9 & SAM | Shows how to create a workflow using object detection with YOLOv9 - object filtering by class - and segmentation with mobile_sam or SAM |
| Use SAM2 | Shows how to run SAM2 |
| Notebook | Description |
|---|---|
| Run Faster R-CNN | |
| Run ResNext | |
| Train Classification Model |
| Notebook | Description |
|---|---|
| Run Neural Style Transfer | |
| Run Kandinsky | |
| Run Stable Diffusion | |
| Use SAM and SD inpaint | |
| Run Stable Cascade | |
| Run and deploy Face inpainting |
| Notebook | Description |
|---|---|
| Run MODNet | |
| Run P3M |
| Notebook | Description |
|---|---|
| Run SwinIR super resolution |
| Notebook | Description |
|---|---|
| Use ByteTrack | |
| Use DeepSORT |
| Notebook | Description |
|---|---|
| Run YOLO v8 pose |
| Notebook | Description |
|---|---|
| Use MMOCR | |
| Run and deploy MMOCR |
| Notebook | Description |
|---|---|
| Use Canny | |
| Use Google Cloud Vision API | |
| Use Depth Anything | |
| Use Florence-2 |