detectron2 VS models

I don’t do AI professionally but as a hobby, so this may not be the best way. But the way you described, it seems the user maybe taking the picture a bit further away and there may be other objects in the frame. So you may want to look into some sort of segmentation or have bounding box. This could help the user make sure they are looking at documents for the correct machine.

I think something like detectron2 [1] could help. It is Apache2 license, so commercial friendly. That said the pre-trained weights may not be used for commercial purposes, so you’ll want to check on that.

[1] https://github.com/facebookresearch/detectron2

And not enough true positives either. Add more augmentations in the config. Also make sure the config is set correctly, so that Detectron2 isn't skipping background images: https://github.com/facebookresearch/detectron2/issues/80

I followed the setup of https://github.com/facebookresearch/detectron2/tree/main/projects/TensorMask. But still I can not import it. As I can with from detectron2.projects import point_rend easily from PointRend projects

I have to use this config file with the dataloader which is in https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/common/coco_loader.py. I figured that i can use cfg.dataloader.train.dataset.names = "my_dataset_train" for this.

I want to use this config file https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/mask_rcnn_mvitv2_t_3x.py like the beneath typical way I use a yaml config file. But giving so many errors one after another that, I even failed to count at this point.

Which is built on https://github.com/facebookresearch/detectron2

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Detectron2 (GitHub: https://github.com/facebookresearch/detectron2) is a Facebook AI Research library with state-of-the-art object detection and segmentation algorithms in PyTorch.

I am using SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50) from TF model zoo .

I spent some time looking for an article that explains object detection, but it seems that there are a lot of articles out there that are not very helpful. Some of these articles focus on specific things like mAP or UoI, but without the broader context, they are not very useful. The main issue with these articles is that they either don't provide any code, or they give examples that are not very helpful, like terminal commands to download a framework and train a model. I started from this link https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md, but it id not very useful. What I really need is a comprehensive explanation of how object detection works, along with working code that I can use to see the results for myself. I know that there are many different approaches to object localization, such as one-stage or two-stage detection, Faster R-CNN, or SSD, but I don't really care which approach will be described. I just need a starting point with clear explanations and working code that I can run.

TensorFlow Models (GitHub: https://github.com/tensorflow/models) is a collection of diverse TensorFlow-based ML and DL models for tasks like image classification, object detection, and text classification.

Assuming you're talking about leveraging the device's the device's Tensor Processing unit for machine learning then there then you're in luck because Google designed the TPU to work extremely well with the machine learning solutions developed by Google such as easy to use SDKs, robust runtimes and APIs ( e.g. - which you probably aren't going to need to touch). If you're a researcher there's plenty of lower level stuff floating about - but developers would be, again, better off staying away from it.

Thank you so that worked and I was able to install it 😅. But when I try to run the test script as mentioned here, I get an error ModuleNotFoundError: No module named 'object_detection'. Am I doing something wrong, I’m using a conda environment and I have tensorflow-macos and tensorflow-metal plug-in installed in the same environment as tf-models.

I've noticed while implementing tensorflow object detection API for a client that they have deprecated the repo and will not be updating it: https://github.com/tensorflow/models/tree/master/research/object_detection

I implore you, download a model from Tensorflow’s model repo and try training it on your conventional GPU. See how much your memory bandwidth and memory count will severely bottleneck performance, in addition see how long it takes to get any decent results.