mmselfsup
mmocr
| mmselfsup | mmocr | |
|---|---|---|
| 5 | 6 | |
| 3,089 | 4,086 | |
| 0.8% | 1.9% | |
| 5.3 | 4.7 | |
| 11 months ago | 15 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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mmselfsup
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
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Does anyone know how a loss curve like this can happen? Details in comments
For some reason, the loss goes up shaply right at the start and slowly goes back down. I am self-supervised pretraining an image modeling with DenseCL using mmselfsup (https://github.com/open-mmlab/mmselfsup). This shape happened on the Coco-2017 dataset and my custom dataset. As you can see, it happens consistently for different runs. How could the loss increase so sharply and is it indicative of an issue with the training? The loss peaks before the first epoch is finished. Unfortunately, the library does not support validation.
- Defect Detection using RPI
- [D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
- Rebirth! OpenSelfSup is upgraded to MMSelfSup
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?
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
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)
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
CRAFT-pytorch - Official implementation of Character Region Awareness for Text Detection (CRAFT)
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
doctr - docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
barlowtwins - Implementation of Barlow Twins paper
iam-crnn-ctc-recognition - IAM Dataset Handwriting Recognition Using CRNN, CTC Loss, DeepSpeech Beam Search, And KenLM Scorer
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.