roboflow-100-benchmark
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
| roboflow-100-benchmark | Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning | |
|---|---|---|
| 1 | 8 | |
| 103 | 57 | |
| - | - | |
| 10.0 | 3.6 | |
| over 1 year ago | about 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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roboflow-100-benchmark
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Roboflow 100: A New Object Detection Benchmark
Thanks for sharing @jonbaer! Iām one of the co-founders of Roboflow. Some additional resources and context:
* Blog Post: https://blog.roboflow.com/roboflow-100/
* Paper: https://arxiv.org/abs/2211.13523
* Github: https://github.com/roboflow-ai/roboflow-100-benchmark
At Roboflow, we've seen users fine-tune hundreds of thousands of computer vision models on custom datasets.
We observed that there's a huge disconnect between the types of tasks people are actually trying to perform in the wild and the types of datasets researchers are benchmarking their models on.
Datasets like MS COCO (with hundreds of thousands of images of common objects) are often used in research to compare models' performance, but then those models are used to find galaxies, look at microscope images, or detect manufacturing defects in the wild (often trained on small datasets containing only a few hundred examples). This leads to big discrepancies in models' stated and real-world performance.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN based on the BDD100K dataset
- [P] Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Goethe University Frankfurt Germany (Fall 2020)
What are some alternatives?
make-sense - Free to use online tool for labelling photos. https://makesense.ai
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
roboflow-100-benchmark - Code for replicating Roboflow 100 benchmark results and programmatically downloading benchmark datasets
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
yolov5 - YOLOv5 š in PyTorch > ONNX > CoreML > TFLite
lama - š¦ LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
NYU-DLSP20 - NYU Deep Learning Spring 2020
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)