fasterrcnn-pytorch-training-pipeline
roboflow-100-benchmark
| fasterrcnn-pytorch-training-pipeline | roboflow-100-benchmark | |
|---|---|---|
| 11 | 1 | |
| 173 | 103 | |
| - | - | |
| 6.0 | 10.0 | |
| 15 days ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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fasterrcnn-pytorch-training-pipeline
- A simple library to train more than 20 Faster RCNN models using PyTorch (including ViTDet)
- A Library of Faster RCNN Models with Simple Training Pipeline for Custom Dataset
- PyTorch Faster RCNN Library - Support for transformer detection models.
- PyTorch Faster RCNN Custom Dataset Training Made Easy
- An efficient, powerful, and easy training pipeline for Faster RCNN models in PyTorch
- A Faster RCNN Object Detection Pipeline for Custom Training in PyTorch
- A PyTorch library for easily training Faster RCNN models (even with custom backbones) on custom datasets for object detection.
- A very simple pipeline to train FasterRCNN Object Detection Models (WRITTEN IN PYTORCH)
- A Faster RCNN Object Detection Pipeline for custom datasets using PyTorch - Get started with training in 5 minutes
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.
What are some alternatives?
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
make-sense - Free to use online tool for labelling photos. https://makesense.ai
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
roboflow-100-benchmark - Code for replicating Roboflow 100 benchmark results and programmatically downloading benchmark datasets
notebooks - Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
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
sports - Cool experiments at the intersection of Computer Vision and Sports ā½š
yolov5 - YOLOv5 š in PyTorch > ONNX > CoreML > TFLite
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.