YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
Implementation of popular deep learning networks with TensorRT network definition API
PaddlePaddle End-to-End Development Toolkit(『飞桨』深度学习全流程开发工具)
🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"
A PyTorch implementation of the YOLO v3 object detection algorithm
YoloV3 Implemented in Tensorflow 2.0
Accompanying code for Paperspace tutorial series "How to Implement YOLO v3 Object Detector from Scratch"
YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
Scaled-YOLOv4: Scaling Cross Stage Partial Network
🔥🔥🔥YOLOv5, YOLOv6, YOLOv7, YOLOv8, PPYOLOE, YOLOX, YOLOR, YOLOv4, YOLOv3, Transformer, Attention, TOOD and Improved-YOLOv5-YOLOv7... Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀
🙄 Difficult algorithm, Simple code.
MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:
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