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Top 23 Jupyter Notebook object-detection Projects
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Yet-Another-EfficientDet-Pytorch
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
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super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
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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.
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simple-faster-rcnn-pytorch
A simplified implemention of Faster R-CNN that replicate performance from origin paper
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SaaSHub
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saliency
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
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pix2seq
Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion) (by google-research)
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maxvit
[ECCV 2022] Official repository for "MaxViT: Multi-Axis Vision Transformer". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling...
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SipMask
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
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roboflow-100-benchmark
Code for replicating Roboflow 100 benchmark results and programmatically downloading benchmark datasets
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vision-camera-realtime-object-detection
VisionCamera Frame Processor Plugin to detect objects using TensorFlow Lite Task Vision
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SaaSHub
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Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
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
> Their SKILL tool involves a set of algorithms that make the process go much faster, they said, because the agents learn at the same time in parallel. Their research showed if 102 agents each learn one task and then share, the amount of time needed is reduced by a factor of 101.5 after accounting for the necessary communications and knowledge consolidation among agents.
This is a really interesting idea. It's like the reverse of knowledge distillation (which I've been thinking about a lot[1]) where you have one giant model that knows a lot about a lot & you use that model to train smaller, faster models that know a lot about a little.
Instead, you if you could train a lot of models that know a lot about a little (which is a lot less computationally intensive because the problem space is so confined) and combine them into a generalized model, that'd be hugely beneficial.
Unfortunately, after a bit of digging into the paper & Github repo[2], this doesn't seem to be what's happening at all.
> The code will learn 102 small and separte heads(either a linear head or a linear head with a task bias) for each tasks respectively in order. This step can be parallized on multiple GPUS with one task per GPU. The heads will be saved in the weight folder. After that, the code will learn a task mapper(Either using GMMC or Mahalanobis) to distinguish image task-wisely. Then, all images will be evaluated in the same time without a task label.
So the knowledge isn't being combined (and the agents aren't learning from each other) into a generalized model. They're just training a bunch of independent models for specific tasks & adding a model-selection step that maps an image to the most relevant "expert". My guess is you could do the same thing using CLIP vectors as the routing method to supervised models trained on specific datasets (we found that datasets largely live in distinct regions of CLIP-space[3]).
[1] https://github.com/autodistill/autodistill
[2] https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learnin...
[3] https://www.rf100.org
Project mention: A simple library to train more than 20 Faster RCNN models using PyTorch (including ViTDet) | /r/deeplearning | 2023-06-07
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
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Exploring the relationships between deprivation and litter on the streets of Glasgow
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Framework for machine learning?
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Mini PC for AI
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What are some USB devices worth using in a Home Lab Environment?
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Is a PCIe x1 enough for light ML tasks
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A note from our sponsor - InfluxDB
www.influxdata.com | 12 May 2024
Index
What are some of the best open-source object-detection projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | automl | 6,157 |
| 2 | YOLOv6 | 5,549 |
| 3 | Yet-Another-EfficientDet-Pytorch | 5,183 |
| 4 | super-gradients | 4,343 |
| 5 | notebooks | 4,185 |
| 6 | simple-faster-rcnn-pytorch | 3,891 |
| 7 | OwnPhotos | 2,741 |
| 8 | yolov3-tf2 | 2,509 |
| 9 | saliency | 931 |
| 10 | pix2seq | 817 |
| 11 | Entity | 668 |
| 12 | TrainYourOwnYOLO | 639 |
| 13 | LLVIP | 591 |
| 14 | TACO | 557 |
| 15 | sports | 441 |
| 16 | maxvit | 421 |
| 17 | SipMask | 333 |
| 18 | roboflow-100-benchmark | 226 |
| 19 | HugsVision | 188 |
| 20 | fasterrcnn-pytorch-training-pipeline | 173 |
| 21 | fashionpedia-api | 152 |
| 22 | auto_annotate | 148 |
| 23 | vision-camera-realtime-object-detection | 92 |
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