mmtracking
mmocr
| mmtracking | mmocr | |
|---|---|---|
| 7 | 6 | |
| 3,382 | 4,086 | |
| 1.6% | 1.9% | |
| 1.5 | 4.7 | |
| 8 months ago | 12 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mmtracking
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Tracking sets of Keypoints by Person
I suggest to try the top down approach with the https://openmmlab.com/ open source package. The openmmlab provides multiple algorithms, datasets and pretrained models for various computer vision tasks. Start with mmpose video demo that integrates detection and pose estimation. You can add later tracking with https://github.com/open-mmlab/mmtracking to track the poses in time.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMTracking: OpenMMLab video perception toolbox and benchmark.
- [P]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- [p]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking
- MMTracking have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- MMTracking Supports Quasi-Dense Similarity Learning for Multiple Object Tracking
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Help combining custom detector (yolo) with a tracker.
Implementations exist, like https://github.com/open-mmlab/mmtracking
mmocr
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Show HN: BetterOCR combines and corrects multiple OCR engines with an LLM
Yup! But I'm still exploring options. (any recommendations would be welcomed!) Here are some candidates I'm considering:
- https://github.com/mindee/doctr
- https://github.com/open-mmlab/mmocr
- https://github.com/PaddlePaddle/PaddleOCR (honestly I don't know Mandarin so I'm a bit stuck)
- https://github.com/clovaai/donut - While it's primarily an "OCR-free document understanding transformer," I think it's worth experimenting with. Think I can sort this out by letting the LLM reason through it multiple times (although this will impact performance)
- yesterday got a suggestion to consider https://github.com/kakaobrain/pororo - I don't think development is still active but the results are pretty great on Korean text
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
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[P]Modern open-source OCR capabilities and which model to choose
Link: https://github.com/open-mmlab/mmocr
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Text Classification Library for a Quick Baseline
For more text classification baselines (CRNN, NRTR, RubustScanner, SAR, SegOCR), checkout https://github.com/open-mmlab/mmocr They are reproducible, customizable.
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[N] MMOCR: A Toolbox for Text Detection, Recognition, and Understanding Based on PyTorch
We just released https://github.com/open-mmlab/mmocr, a new member in OpenMMLab https://openmmlab.com/. This first release supports
- OCR Baselines Based on PyTorch
What are some alternatives?
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
CRAFT-pytorch - Official implementation of Character Region Awareness for Text Detection (CRAFT)
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
doctr - docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
iam-crnn-ctc-recognition - IAM Dataset Handwriting Recognition Using CRNN, CTC Loss, DeepSpeech Beam Search, And KenLM Scorer
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.