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@ai_fast_track

4 Dec

@fastdotai

💡 Prop Tip to 🚀 fast track your object detection training

All @fastdotai users use this method for free!

It's fine_tune()

We noticed an important increase in the COCO Metric when we train object detection models using this method even with the simple default values

Fine tuning has 2 phases:

🧊 Phase 1: Freeze your model's backbone and neck. Train your model for a couple of epochs (e.g. 1 to 5)

📈 Phase 2: Unfreeze the whole model. Train for higher number of periods (depending on your dataset, and how hard/easy your data is)

Phase 1 is a kind of warm-up for your model head.

Here is an example showing how to call the fine_tune() method.

In this case, we freeze both the backbone and the neck for 1 epoch in phase 1, and train for 20 epochs in phase 2.

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AI Fast Track (33/45)

@ai_fast_track

30 Nov

🎉🎊🥳Day 30/30: Labeling data and training object detection models are time consuming and expensive.

Here is a Survey of Self-Supervised and Few-Shot Object Detection (FSOD). Those technique aim at alleviating those issues.

The authors have categorized, reviewed, and compared several few-shot and self-supervised object detection methods

📌 FSOD is about training a model on novel (unseen) object classes with little data. It still requires prior training on many labeled data of base (seen) classes

📌 Self-Supervised Learning (SSL) methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection

📌 Combining few-shot and self-supervised object detection is a promising research direction

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AI Fast Track (33/45)

@ai_fast_track

26 Nov

Day 26/30: General Gaussian Heatmap Labeling is Arbitrary-Oriented Object Detection AOOD

📌 It uses an anchor-free object-adaptation label assignment strategy to deﬁne positive candidates based on 2D GH, reﬂecting shape and direction features of arbitrary-oriented objects.

📌 GGHL improves the AOOD performance with low parameter-tuning and time costs.

📌 It is also applicable to most AOOD methods to improve their performance including lightweight models.

📰 Paper: A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection

abs: arxiv.org/abs/2109.12848…
pdf: arxiv.org/pdf/2109.12848…

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AI Fast Track (33/45)

@ai_fast_track

24 Nov

Day 24/30: 🥇 EfficientDet is a very popular object detection model for a good reason!

Let’s see why

📌 EfficientDet achieved State-Of-The-Art (SOTA) accuracy while reducing both the size of parameters, and the FLOPS, when it was released. It’s still a very good contender.

📌 Before introducing EfficientDet, models were getting impressively big to achieve SOTA results

❓ The authors asked the following question:
Is it possible to build a scalable detection architecture with both higher accuracy and better efficiency across # resource constraints?

So, they systematically studied neural network architecture design choices for object detection, and proposed several key optimizations to improve efficiency:

1- A weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multiscale feature fusion

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AI Fast Track (33/45)

@ai_fast_track

23 Nov

@ai_fast_track

I’m at Day 23 of my 30 posts (on Object Detection) in 30 days challenge

I gathered 12 visual summaries on OD Modeling 🎁

A lot of people find those posts helpful, follow @ai_fast_track to catch the upcoming posts, and give this tweet a quick retweet 🙏

Summary of summaries👇

https://twitter.com/ai_fast_track/status/1453368771285032971

1- Common Object Detector Architecture you should be familiar with:

https://twitter.com/ai_fast_track/status/1453368771285032971

https://twitter.com/ai_fast_track/status/1460297658472574982

2- Four Feature Pyramid Network (FPN) Designs you should know:

https://twitter.com/ai_fast_track/status/1460297658472574982

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@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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