DakeQQ / YOLO-Depth-Estimation-for-AndroidLinks
Demonstration of combine YOLO and depth estimation on Android device.
☆59Updated this week
Alternatives and similar repositories for YOLO-Depth-Estimation-for-Android
Users that are interested in YOLO-Depth-Estimation-for-Android are comparing it to the libraries listed below
Sorting:
- mnn yolo demos.☆84Updated last year
- a Android demo of depth_anything_v1 and depth_anything_v2☆68Updated last year
- A simple tutorial of SNPE.☆178Updated 2 years ago
- Learning ncnn with some examples☆70Updated 2 years ago
- an example of segment-anything infer by ncnn☆124Updated 2 years ago
- ☆41Updated last year
- a simple pipline of int8 quantization based on tensorrt.☆69Updated 3 years ago
- c++ version of yolov5 segmentation with ncnn☆81Updated 3 years ago
- SAM and lama inpaint,包含QT的GUI交互界面,实现了交互式可实时显示结果的画点、画框进行SAM,然后通过进行Inpaint,具体操作看readme里的视频。☆50Updated last year
- YOLOv5在高通AI Engine Direct环境下进行QNN量化,CPU推理的项目☆16Updated last year
- Speed up image preprocess with cuda when handle image or tensorrt inference☆79Updated 2 weeks ago
- TensorRT-FastSAM(https://github.com/CASIA-IVA-Lab/FastSAM)☆23Updated last year
- ☆33Updated last year
- ☆38Updated last year
- ☆17Updated last year
- yolov8pose 瑞芯微 rknn 板端 C++部署。☆36Updated last year
- Let's use Qualcomm NPU in Android☆15Updated 9 months ago
- mobilenet-ssd snpe demo☆41Updated 4 years ago
- YOLOv12 Inference Using CPP and ONNX Runtime☆51Updated 8 months ago
- Implementation of YOLOv9 QAT optimized for deployment on TensorRT platforms.☆129Updated 6 months ago
- DragGan in NCNN with c++☆52Updated 2 years ago
- Android demo of LightTrack infer by ncnn☆44Updated 4 years ago
- yolov8s-pose using ncnn inferring!☆43Updated 2 years ago
- rknn-3588部署yolov5,利用线程池实现npu推理加速;Deploying YOLOv5 on RKNN-3588, utilizing a thread pool to achieve NPU inference acceleration.☆79Updated 6 months ago
- 完成轻量化网络FastestDet的算法NCNN部署☆17Updated 3 years ago
- fast deployment for yolo detectors☆18Updated 2 years ago
- ☆67Updated 3 years ago
- Easy Training Official YOLOv11、YOLOv10、YOLOv8、YOLOv7、YOLOv6、YOLOv5 and Prune all_model using Torch-Pruning!☆112Updated 2 months ago
- Try to export the ONNX QDQ model that conforms to the AXERA NPU quantization specification. Currently, only w8a8 is supported.☆11Updated last year
- yolov8seg 瑞芯微 rknn 板端 C++部署,使用平台 rk3588。☆29Updated last year