Object detection Puns

Hi r/MachineLearning,

https://github.com/jacobgil/pytorch-grad-cam is a project that has a comprehensive collection of Pixel Attribution Methods for PyTorch (like the package name grad-cam that was the original algorithm implemented).

Class Activation Maps can help diagnose properties about the model predictions, like "where does the model see a cat in the image".

After many requests I added support for Object Detection and Semantic Segmentation, and wanted to share this with you.

Here you can find detailed notebook tutorials about this:

• A tutorial on using Class Activation Maps for Object Detection
• A tutorial on using Class Activation Maps for Semantic Segmentation

Computing the CAM for object detection

Computing the CAM for semantic segmentation

The problem

Class Activation Maps are usually researched and applied for classification models.

A repeating request in this repository, and also in some object detection projects, was to add support for grad-cam for object detection.

One challenge with this, is that object detection frameworks typically don't output tensors you can back-propagate through to compute gradients.

They typically output dictionaries with bounding boxes, labels, etc, after a lot of processing, and don't expose any way to compute gradients with respect to those detections.

If you want to compute CAMs for them, you typically have to dive into the code of these object detection packages and create solutions that work only with them.

There was no "generic" tool that just works and can be adapted to new object detection models.

The solution - gradient free methods

Some Class Activation Map methods don't depend on computing the gradients. Examples of these:

• EigenCAM, computes PCA on the activations and returns the first principle component.
  It's very fast since it requires a single forward pass, but it doesn't have good enou

Hi everyone,

I am currently working on my bachelor thesis in the field of object detection. I have chosen the Yolov5 model from "https://github.com/ultralytics/yolov5". Looking for a dataset for autonomous driving I found NuScenes and Waymo, but in Waymo I have problems converting the TFRecords files to .yaml files. Does anyone know of an approach?

Does anyone knows of any other good datasets in the area of Autonomous Driving? They should also be optimally convertible to .yaml files.

Greetings

GT_King0895

Hello, I am a newbie in drones and UAVs. I have been training deep learning models to recognize objects in the air. In order to actually deploy it, I was wondering if I could get some suggestions about possible cameras that I can stream and a GPU unit that would allow me to deploy the trained model. Some ideas that I came across:

1. Nvidia Jetson Tx1/Tx2 and nano
2. USB 3.0 Cameras
3. Go pros.

Any suggestions would be great!

Preparing dataset for training object detection model is a time-consuming task.

Generating synthetic dataset is much faster and easier, so it can save a lot of time for data scientists and ML engineers.

I've created a detailed tutorial on how to do that with Python and published it here:

https://medium.com/@alexppppp/how-to-create-synthetic-dataset-for-computer-vision-object-detection-fd8ab2fa5249

Or, you can skip the tutorial and use the script from here:

https://github.com/alexppppp/synthetic-dataset-object-detection

Take a look at an example of synthetic scene here:

https://preview.redd.it/lzr920vntlc81.png?width=2053&format=png&auto=webp&s=a39442e63eb02eeb2897530323d0a92e81041122

I hope the script will be useful for you! Also I will appreciate any feedback and ideas how to improve it)

Does shinobi support using something like this for OD? https://coral.ai/products/accelerator ?

Hello everyone,

I have successfully trained my model using the TensorFlow Object Detection API and wanted to evaluate it on it. I used the following site as a guide: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html

Link for the code "model_main_tf2.py": https://github.com/tensorflow/models/blob/master/research/object_detection/model_main_tf2.py

After running the script "model_main_tf2.py", I received the following error message:

-> INFO:tensorflow:Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn -> I1220 17:06:56.024288 140351537808192 checkpoint_utils.py:140] Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn -> INFO:tensorflow:Found new checkpoint at models/my_ssd_resnet50_v1_fpn/ckpt-2 -> I1220 17:06:56.024974 140351537808192 checkpoint_utils.py:149] Found new checkpoint at models/my_ssd_resnet50_v1_fpn/ckpt-2 -> 2021-12-20 17:06:56.098253: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) -> /home/ameisemuhammed/anaconda3/envs/tensorflow/lib/python3.9/site-packages/keras/backend.py:401: UserWarning: \tf.keras.backend.set_learning_phase\ is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `call` method of your layer or model. -> warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and ' -> 2021-12-20 17:07:08.993353: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8204 -> Traceback (most recent call last): -> File "/home/ameisemuhammed/TensorFlow/workspace/training_demo/model_main_tf2.py", line 114, in <module> tf.compat.v1.app.run() -> File "/home/ameisemuhammed/anaconda3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) -> File "/home/ameisemuhammed/anaconda3/envs/tensorflow/lib/python3.9/site-packages/absl/app.py", line 303, in run _run_main(main, args) -> File "/home/ameisemuhammed/anaconda3/envs/tensorflow/lib/python3.9/site-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) -> File "/home/am

I have built an object detector that is detecting birds at my bird feeder. It is trained only for birds in general, and without any regard for species. In order to classify my data I am running a classification model on another computer with the data retrieved from the database of the detections.

I am wondering if this is counterintuitive? Should I be trying to train my object detection model (YOLOv4 running on a Jetson Nano) to also perform the object detections with respect to species, or am I on point with developing a classifier on a more powerful PC to handle the species classification?

Hi everyone,

So, just a month ago, we were shocked by the plagiarism alarm:

>the article “Momentum residual neural networks” by Michael Sander, Pierre Ablin, Mathieu Blondel and Gabriel Peyré, published at the ICML conference in 2021, hereafter referred to as “Paper A”, has been plagiarized by the paper “m-RevNet: Deep Reversible Neural Networks with Momentum” by Duo Li and Shang-Hua Gao, accepted for publication at the ICCV conference, hereinafter referred to as “Paper B”.

Today, I found out that our paper (still in conference review) is also severely plagiarized by: "Minghao Gao, Hailun Zhang (1), Yige Yan (2) ((1) Beijing Institute of Technology, (2) Hohai University)

• Our paper: Improve Object Detection by Label Assignment Distillation. https://arxiv.org/abs/2108.10520
• Their paper: Label Assignment Distillation for Object Detection, https://arxiv.org/abs/2109.07843

Our paper was first submitted to the conference on Jun 9 2021, and we upload to Arxiv on Aug 24 2021. We show the proof of plagiarism in our Open Github: https://github.com/cybercore-co-ltd/CoLAD_paper/blob/master/PlagiarismClaim/README.md

Updated: The issue is resolved. Thanks all for your help, especially zyl1024 and Jianfeng Wang wjfwzzc (the Author of original NIPS version draft). We want to close this post, and go back to our normal work. Hope this can serve as a reference should you encounter this problem in the future.

Updated 2: The official emails between me and Jianfeng Wang can be found at:

https://github.com/cybercore-co-ltd/CoLAD_paper/blob/master/PlagiarismClaim/ConfirmLetter.pdf

Best Regard !!!

If you have any material about object detection it would be very apricated if you could share.

As a school project I'm trying to make my own model for object detection, and everywhere I go I see prebuilt models but without any explanations.

cap = cv2.VideoCapture(0)
while cap.isOpened():
    ret, frame = cap.read()
    
    # Make detections 
    results = model(frame)
    
    cv2.imshow('YOLO', np.squeeze(results.render()))
    
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows() 

when i run this code on colab

no camera feed pops up but the code gets executed

when i run this same code on jupyter lab i get an error saying camera index not found i am using the inbuild camera on my laptop. also i have installed yolov5 locally on my system and when i run this command python detect.py --source 0 --weights yolov5l.pt after i am in the yolo folder the camera feed pops up how do i open the feed through jupyter lab or even colab

in this link https://www.youtube.com/watch?v=tFNJGim3FXw&list=PLP9aE5E-jrzBqEEyECxawP67e5HAXKE7m&index=61 this person executes the above piece of code and the camera feed pops up i have tried all possible ways i used the code used in colab for starting camera feed on colab but how do i do it with openCV please help me out

Hi I would like to develop an app that would detect an object, display said object's name as a text and then a 2nd text that translates that name in another language. Basically it would be a real world AR overlay way of learning what certain objects in your surrounding would be called in another language. I thought it would be a good real world way to learn a new language. Can anyone help me outline the steps I would need to take to bring my idea to fruition? Any help is useful, thank you!

Hey guys, I am looking for some insight into choosing an algorithm for object detection. YOLOR has a higher box mAP, so I want to go with that. Any other algorithms that you would suggest? I have used mmdetection for building the Cascade-rcnn model, but just found out that YOLOR performs better on paper. Any suggestions? The model will be deployed in an industry setting so accuracy and robustness is the main objective. Thank you so much for the help!

I’m working on building a moving device of sorts, and I want to use tensor flow or vuforia and a camera to detect different things like a person, an animal, a chair, things like that. It would be looking down from approximately 6’ high, so what would be the best kind of camera(s) to use in this situation? The board I’m working with is an Intel Galileo 2, but I do have an Arduino mega as well as 3 Arduino Unos if I need them. Also, what language should I use for this project? I was thinking Python but I don’t know how well that integrates with vuforia or tensor flow. And which should I use, or should I utilize parts from both vuforia and tensor flow?

This project is still in the early stages, so it’s mainly just an idea, but soon I’m hoping to make it a reality, and with help from y’all, that will hopefully come sooner than later!

Thanks for the assistance, and I’ll probably be making more posts here again.

I'm trying to run object detection on pdf documents to recognize the signature position.

Do you know any pretrained model that can recognize signatures?

Hello guys

I have to do object detection without using any pre trained models. It is very difficult to do it as I don't understand neural architecture. As rcnn requires a pre trained cnn so I trained a cnn network in cifar-10 but I don't understand how to plug it in rcnn architecture. So guys can somebody guide me with explanation or a link for reference. Thanks a lot for reading and helping