ssd_keras
zero-shot-object-tracking
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| ssd_keras | zero-shot-object-tracking | |
|---|---|---|
| 4 | 10 | |
| 1,846 | 349 | |
| - | 1.1% | |
| 0.0 | 0.6 | |
| about 2 years ago | 10 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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ssd_keras
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Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
In Tensorflow/ Keras when running the code from https://github.com/pierluigiferrari/ssd_keras, use the estimator: ssd300_evaluation. I received this error.
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Shared weights between different implementations
Yeah, the order of axes was different between those 2. Another guy used https://github.com/pierluigiferrari/ssd_keras https://github.com/uhfband/keras2caffe/blob/master/keras2caffe/convert.py probably not much actual use but maybe some more reassurance?
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Simplest way to deploy Keras NN model into C++?
Don't know about simplest, but we either used caffe or tensorrt, it is maybe a bit difficult to use but I'd actually say simple fast GPU inference is what it's geared towards. There is a keras -> caffe converter https://github.com/pierluigiferrari/ssd_keras here, I think. Caffe is a c++ lib, typical, with dependencies and all. I've never heard anything of tensorflow running on c++. But with tensorrt you should get an "artifact" that you'd load, no matter where it comes from
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ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Help?
Tensorflow V1 Keras code (original repo): Github Repo
zero-shot-object-tracking
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How to Track Flying Objects?
I’ve seen a bunch of drone-detection computer vision projects. Usually they’re detecting dromes from other drones though (Eg for autonomous racing[1] or drone-defense).
A challenge with doing it from the ground is that the drones will be quite small relative to the size of the image. With sufficient compute and several cameras a tiling-based approach[2] should work!
If you want to do unique-identification you’ll also need object tracking[3].
This is exactly the type of project Roboflow (our startup) is built to empower! Happy to chat/help further (Eg we might be able to help source a good dataset to start from). And if it’s for non-commercial use it should be completely free.
[1] https://blog.roboflow.com/drone-computer-vision-autopilot/
[2] https://blog.roboflow.com/detect-small-objects/
[3] https://blog.roboflow.com/zero-shot-object-tracking/
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Object tracking in videos?
We use CLIP for object tracking with pretty good results (with no second model train required). https://blog.roboflow.com/zero-shot-object-tracking/
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Hacker News top posts: Aug 28, 2021
Zero Shot Object Tracking\ (4 comments)
- Need help in camera selection
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Zero Shot Object Tracking
It uses an object detection model (in our example code[1], we used one from Roboflow Universe[2] but you should be able to use any object detection model) and then sends a crop of each detected box to CLIP to get the feature vector that Deep SORT uses to differentiate between and track instances across frames.
[1] https://github.com/roboflow-ai/zero-shot-object-tracking
[2] https://universe.roboflow.com
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[P] Zero-Shot Object Tracking with CLIP and Deep SORT
Repo: https://github.com/roboflow-ai/zero-shot-object-tracking
- Zero-Shot Object Tracking with CLIP and Deep SORT
- Show HN: Zero-Shot Object Tracking
What are some alternatives?
layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
cppflow - Run TensorFlow models in C++ without installation and without Bazel
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
a-PyTorch-Tutorial-to-Object-Detection - SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
SSD-pytorch - SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x