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AI Fast Track

@ai_fast_track

20 Nov

FCOS is an an anchor-free object detector.

It was one of first competitors of anchor-based single/two stage object detectors.

Understanding FCOS will help understanding other model inspired by FCOS.

Summary ...👇

📌 FCOS reformulates object detection in a per-pixel prediction fashion

📌 It uses multi-level prediction to improve the recall and resolve the ambiguity resulted from overlapped bounding boxes

📌 It proposes “center-ness” branch, which helps suppress the low-quality detected bounding boxes and improves the overall performance by a large margin

📌 It avoids complex computation such as the intersection-over-union (IoU)

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AI Fast Track

@ai_fast_track

18 Nov

How to create a robustness evaluation dataset?

"Natural Adversarial Objects" (NAO) dataset is a challenging robustness evaluation dataset for models trained on MSCOCO

📌 Models generally perform well on large scale training sets

📌 They generalize on test sets coming from the same distribution

📌 When using NAO dataset, EfficientDet-D7 mAP reduced by 74.5% compared to MSCOCO

📌 Faster RCNN reduced by 36.3% compared to MSCOCO

📌 They evaluated 7 SOTA models, and showed they consistently fail to perform accurately on NAO, comparing to MSCOCO

📌 The drop is present on both in-distribution and out-of-distribution objects

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AI Fast Track

@ai_fast_track

17 Nov

❇VFNet: A very interesting model that isn’t under the radar. You should give it a try :)

VariFocalNet: An IoU-aware Dense Object Detector

🧊 Background:
📌 Accurately ranking candidate detections is crucial for dense object detectors to achieve high performance
...

📌 Prior work uses the classification score or a combination of classification and predicted localization scores (centerness) to rank candidates.

📌 Those 2 scores are still not optimal

🧊 Novelty:
📌 VFNet proposes to learn an IoU-Aware Classification Score (IACS)

📌IACS is used as a joint representation of object presence confidence and localization accuracy using IoU

📌 VFNet introduces the VariFocal Loss

📌 The VariFocal Loss down-weights only negative examples for addressing the class imbalance problem during training

Read 7 tweets

AI Fast Track

@ai_fast_track

16 Nov

@OpenMMLab

📢 The amazing @OpenMMLab just released a new project:

MMFlow: an open-source optical flow toolbox written in Pytorch

OpenMMLab hosts several impressive open-source projects for both academic research and industrial applications.

OpenMMLab covers a wide range of research topics of computer vision, e.g., classification, detection, segmentation and super-resolution.

📌 MMCV: Foundational library for computer vision.

📌 MIM: MIM Installs OpenMMLab Packages.

📌 MMClassification: Image classification toolbox and benchmark.

📌 MMDetection: Detection toolbox and benchmark.

📌 MMDetection3D: Next-generation platform for general 3D object detection.

📌 MMSegmentation: Semantic segmentation toolbox and benchmark.

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AI Fast Track

@ai_fast_track

15 Nov

4 Feature Pyramid Network (FPN) Design you should know:

FPN, PANet, NAS-FPN, and BiFPN

📌 (a) FPN uses a top-down pathway to fuse multi-scale features from level 3 to 7 (P3 - P7);

📌 (b) PANet adds an additional bottom-up pathway on top of FPN;

📌 (c) NAS-FPN uses neural architecture search to ﬁnd an irregular feature network topology and then repeatedly apply the same block;

📌 (d) BiFPN is a bit similar to PANet, adds shortcut fusing, and then repeatedly apply the same block

📝 Some other observations:

📌 The model diagram corresponds to the One-Stage Object Detection Architecture

📌 The FPN illustration is extracted from the EfficientDet paper

📌The (P3-P5) layers are referred as the Convolutional (C3-C5) Layers in other papers

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AI Fast Track

@ai_fast_track

5 Nov

https://twitter.com/ai_fast_track/status/1407333442145206280

🤔 How to increase your Small Object Detection Average Precision APs?

💡 By increasing both image and backbone sizes when training your model:

📌 Increasing both image and backbone sizes in EfficientDet jumped APs by 14+%

📌 Increasing backbone size in RFBNet increased APs

https://twitter.com/ai_fast_track/status/1407333442145206280

📌 Increasing image size from 320 to 608 in PP-YOLO led to 10+% increase in APs

For more tips and tricks to improve small object detection tips & tricks, check out the list I shared in my first tweet.

Benchmarks are extracted from the PP-YOLO paper:

📰 Paper: PP-YOLO: An Effective and Efficient Implementation of Object Detector

PDF: arxiv.org/pdf/2007.12099…

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