fasterrcnn-pytorch-training-pipeline
notebooks
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| fasterrcnn-pytorch-training-pipeline | notebooks | |
|---|---|---|
| 11 | 19 | |
| 169 | 4,134 | |
| - | 7.4% | |
| 6.0 | 8.5 | |
| 3 days ago | 11 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| 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
notebooks
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Supervision: Reusable Computer Vision
Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).
We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.
[1] https://github.com/roboflow/inference
[2] https://github.com/roboflow/notebooks
[3] https://inference.roboflow.com/foundation/about/
[4] https://docs.roboflow.com/deploy/hosted-api
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Roboflow Notebooks: 30+ tutorials on using SOTA models and vision techniques
We (the Roboflow open source team) actively write open source Google Colab notebooks showing how to use new SOTA models. Our library covers SAM, CLIP, Detectron2, YOLOv8, RTMDet, DINOv2, and more. These notebooks helped me cross the chasm from "how do I use X model?" to being able to both write and understand inference code.
- Notebooks: How to tutorials for computer vision models and techniques
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Training Instance Segmentation models on custom dataset
Here's an open source SegFormer notebook and guide: https://github.com/roboflow/notebooks/blob/main/notebooks/train-segformer-segmentation-on-custom-data.ipynb
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[Advice request] How on earth am I supposed to break into machine learning research as an undergraduate?
Great ways to get some experience in general ML: * https://kaggle.com/learn to up your skill-set, practice a bit, and improve breadth of knowledge in topics like deep learning and computer vision * https://huggingface.co/learn free NLP courses that will really beef up your skillset * https://madewithml.com - robust tutorials for the end-to-end deep learning MLOps process * https://roboflow.com/learn - intro course material and some advanced topics in computer vision; tutorial walkthroughs for model training: https://github.com/roboflow/notebooks
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Generate Synthetic Computer Vision Data with Stable Diffusion Image-to-Image
Repo: https://github.com/roboflow/notebooks/blob/main/notebooks/sa...
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Rich Jupyter Notebook Diffs on GitHub... Finally.
Here are the notebooks I spend day and night refining: https://github.com/roboflow/notebooks
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Tools for object detection on satellite images
You’ll just need to have labeled solar panel images, and pick a model architecture and tutorial to train with: https://github.com/roboflow/notebooks
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[OC] Football Players Tracking with YOLOv5 + ByteTrack + OpenCV
dataset: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/dataset/4 code: https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb video: https://youtu.be/QCG8QMhga9k
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Should I get a Google Coral USB Accelerator for my RPI4 or should I just buy a Nvidia Jetson Nano?
Have fun! Great field. Just also try out the first few OpenCV tutorials, and train a few custom model to deploy to see what you think. Here’s a ton of free open source notebooks: https://github.com/roboflow/notebooks
What are some alternatives?
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
roboflow-100-benchmark - Code for replicating Roboflow 100 benchmark results and programmatically downloading benchmark datasets
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
sports - Cool experiments at the intersection of Computer Vision and Sports ⚽🏃
glami-1m - The largest multilingual image-text classification dataset. It contains fashion products.
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.
make-sense - Free to use online tool for labelling photos. https://makesense.ai
uav-detection - Drone / Unmanned Aerial Vehicle (UAV) Detection is a very safety critical project. It takes in Infrared (IR) video streams and detects drones in it with high accuracy.
timm-flutter-pytorch-lite-blogpost - PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter.