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AI Fast Track

@ai_fast_track

4 Nov

🥇 FCOS3D won the 1st place out of all the vision-only methods in the nuScenes 3D Detection Challenge of NeurIPS 2020.

Here is a brief description:

📌 FCOS3D is a monocular 3D object detector

📌 It’s an anchor-free model based on FCOS (2D) counterpart

📌 It replaces the FCOS regression branch by 6 branches

📌 The center-ness is redeﬁned with a 2D Gaussian distribution based on the 3D-center

📌 The authors showed some failure cases, mainly focused on the detection of large objects and occluded objects.

⏹ Source code and models are shared in the MMDetection3D repo:
github.com/open-mmlab/mmd…

⏹ MMDetection3D also has many other 3D detection models:

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AI Fast Track

@ai_fast_track

2 Nov

YOLO Real-Time (YOLO-ReT) architecture targets edge devices.

It achieves 68.75 mAP on Pascal VOC and 34.91 mAP on COCO using MobileNetV2×0.75 backbone.

Here is a brief description of the YOLO-ReT 👇

Both model accuracy and execution time (Frame Per Second) are crucial when deploying a model on edge device. YOLO-ReT is based on these 2 ideas:

⏹ Backbone Truncation: Only 60% of the backbone is initialised with pretrained weights. Using all the weights harms model accuracy

⏹ Raw Feature Collection and Redistribution (RFCR):

📌 Fuse {C2, C3, C4} into C5 layer (fused feature map)

📌 Discard last CNN layers

📌 Pass the fused feature map through a 5x5 Mobile Convolution block (MBConv)

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AI Fast Track

@ai_fast_track

27 Oct

✨Common Object Detector Architecture you should be familiar with:

📌 Common object detectors are divided into One-Stage Detectors (OSD), and Two-Stage Detectors (TSD)

📌 Both OSD and TSD can be either anchor-based (relying on anchor boxes) or anchor-free

📌 OSD use the whole feature maps to predict bounding boxes/labels: Dense Prediction

📌 TSD have an extra step hence two-stage: extracting proposals (regions of interest)

📌 Proposals are used to extract feature map regions to predict bounding boxes/labels: Sparse Prediction

📌 TSD don't use the whole feature map for prediction

📌 TSD (e.g. Faster R-CNN) used to be more accurate than STD (e.g. SSD, YOLO, etc.)

📌 STD (e.g. EfficientDet, RetinaNet, VFNet, YOLOX, etc.) recently show better results than TSD

📌 STD are faster than TSD

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AI Fast Track

@ai_fast_track

25 Oct

🧐7 things you should know about the Focal Loss:

📌 It was introduced in the RetinaNet paper to address the foreground-background class imbalance encountered during training of dense detectors (one-stage detectors)
...

📌 It’s derived from the cross-entropy loss such that it down-weights the loss assigned to well-classiﬁed examples. It's used in the classification head.

📌 It’s used in many one-stage object detection models: EfficientDet, FCOS, VFNet, and many other models

📌 It can also be used in two-stage object detection models: e.g. Sparse R-CNN

📌 It crashes losses associated to easy examples: for a confidence score of 0.9, the focal loss is 100 times smaller than the cross-entropy loss (see figure here above)

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AI Fast Track

@ai_fast_track

21 Oct

🥇YOLOX beat YOLOv5. They won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021).

I briefly explain how they created it, here below👇

YOLOX will be soon supported in our IceVision Framework (Repo: github.com/airctic/IceVis…)

Considering YOLOv4 and YOLOv5 might be a little bit over-optimized for the anchor-based pipeline, they choose YOLOv3 as their starting point.

They transformed YOLOv3 into YOLOX by using the following techniques:

🟦 Decoupled head,
🟦 Anchor-free architecture (like FCOS), and

🟦 Advanced label assigning strategy (SimOTA)

They also used some other training strategies such as: adding EMA weights updating, cosine LR schedule, IoU loss, and IoU-aware branch.

pdf: arxiv.org/pdf/2107.08430…
abs: arxiv.org/abs/2107.08430
repo: github.com/Megvii-BaseDet…

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AI Fast Track

@ai_fast_track

20 Oct

@NVIDIAAI

Here is a cool approach for effective labeling from @NVIDIAAI

📰Active Learning for Deep Object Detection via Probabilistic Modeling

Object detection labeling is both time consuming and expensive. Active learning to the rescue:
🧵

🟧They use mixture density networks to estimate, in one forward pass of a single model, both localization and classification uncertainties

🟧 They leverage them in the scoring function for active learning.

🟧 Then, those data points with the top-K scores are sent for labeling

🟧 They use SSD pytorch for their implementation

📰Paper: Active Learning for Deep Object Detection via Probabilistic Modeling

PDF: arxiv.org/abs/2103.16130
Abs: arxiv.org/pdf/2103.16130…
Repo: github.com/NVlabs/AL-MDN

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