yolov7_d2 VS BCNet

Compare yolov7_d2 vs BCNet and see what are their differences.

yolov7_d2

🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥 (by lucasjinreal)
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yolov7_d2 BCNet
4 1
3,129 507
- -
0.0 3.0
5 months ago 10 months ago
Python Python
GNU General Public License v3.0 only MIT License
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yolov7_d2

Posts with mentions or reviews of yolov7_d2. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-29.

BCNet

Posts with mentions or reviews of BCNet. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing yolov7_d2 and BCNet you can also consider the following projects:

yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite

layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis

edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU

mmdetection - OpenMMLab Detection Toolbox and Benchmark

edgetpu - Coral issue tracker (and legacy Edge TPU API source)

yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv4 - Port of YOLOv4 to C# + TensorFlow

medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

yolact - A simple, fully convolutional model for real-time instance segmentation.

PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

CATNet - 🛰️ Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images (TNNLS 2023)

AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.