GitHub topics: object-detection-datasets
coderonion/awesome-yolo-object-detection
🚀🚀🚀 A collection of some awesome public YOLO object detection series projects and the related object detection datasets.
Size: 388 KB - Last synced at: 2 days ago - Pushed at: 29 days ago - Stars: 1,472 - Forks: 202
HaloTrouvaille/YOLO-Multi-Backbones-Attention
Model Compression—YOLOv3 with multi lightweight backbones(ShuffleNetV2 HuaWei GhostNet), attention, prune and quantization
Language: Python - Size: 1.12 MB - Last synced at: 22 days ago - Pushed at: over 4 years ago - Stars: 497 - Forks: 117
k2-gc/object-detection-format-converter
Object Detection Dataset Format Converter
Language: Python - Size: 39.1 KB - Last synced at: 2 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0
E-delweiss/mealtray_dataset
Compagny meal tray photos for object localization (such as YOLO)
Size: 352 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0
NicholasBaraghini/Machine_Learning_For_Computer_Vision_KABLATOR_PRG
Kablator presents itself as a manufacturer of robotic machines for the automatic wiring of electrical panels. The main task of the machine is then to place wires along ducts, with high accuracy, from different orientations and colors. The borders of the duct are characterized by teeth used to keep wires fixed in their position. The main goal of the project is, to use machine learning for computer vision techniques, to detect each tooth of the duct that will be then used to implement feedback that can generate a shift of the end-effector in the plane useful for positioning the robotic head exactly between two consecutive teeth, avoiding impacts and breakages of the teeth. The tooth recognition has to be evaluated in the shortest time possible since the Kablator’s goal is to reduce the wiring times. Therefore it is required a real-time implementation of a object detection algorithm.
Language: Jupyter Notebook - Size: 435 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0
