Detic
mmdetection
Detic | mmdetection | |
---|---|---|
11 | 23 | |
1,784 | 28,036 | |
1.8% | 2.2% | |
1.9 | 8.4 | |
2 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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Detic
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Autodistill: A new way to create CV models
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
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[P] Image search with localization and open-vocabulary reranking.
For localisation at search time I ended up using OWL-ViT. This worked really well. I did not try Detic or CLIPseg but would be interested to hear if anyone else has tried these?
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training object detector using classified images?
git clone https://github.com/facebookresearch/Detic cd Detic pip install -r requirements python demo.py --config-file configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml --input desk.jpg --output out.jpg --vocabulary lvis --opts MODEL.WEIGHTS models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth
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[P] Any object detection library
You might want to take a look at DETIC : https://github.com/facebookresearch/Detic (Open Vocabulary Object Detection, trained on thousands of classes)
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[P] Awesome Image Segmentation Project Based on Deep Learning (5.6k star)
Are there any open-label segmentation model included in this repo, like Detic or LSeg?
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[R] CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory + Code + Robot demo
We made this using pretty recent advances in web-data pretrained models like Detic and LSeg for detection, CLIP for visual queries, and Sentence BERT for semantic queries. Our "database" is really a neural field (Instant NGP) that maps from 3D coordinates to a high dimensional embedding vector in the same representation space as CLIP and SBERT.
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[P] Using OpenAI's CLIP repository as a support, I was able to create a software to detect anything in an image at its original resolution!
Is it similar to the open vocabulary detic?
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Researchers at Meta and the University of Texas at Austin Propose โDeticโ: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision
Code for https://arxiv.org/abs/2201.02605 found: https://github.com/facebookresearch/Detic
- Detecting Twenty-thousand Classes using Image-level Supervision
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[R] Detecting Twenty-thousand Classes using Image-level Supervision
github: https://github.com/facebookresearch/Detic
mmdetection
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Semantic segementation
When I look for benchmarks I always start here https://paperswithcode.com/task/instance-segmentation/codeless it has the lists of datasets to measure models accross lots o papers. Many are very specific models with low support or community but it gives you a good idea of โโthe state of the art. It also lists repositories related to good community. https://github.com/open-mmlab/mmdetection seems very active and the one that is being used the most, you could use the models that it has integrated in its model zoo, within the same repository. It has the benchmarks to compare those same models and some of them are from 2022
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How to Convert Model Mask into Polygon and save JSON?
MODEL: https://github.com/open-mmlab/mmdetection
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Object Detection Model for Custom Dataset Training?
Would it make sense to work with OpenMMLab (https://github.com/open-mmlab/mmdetection) or Pytorch-image-models (https://github.com/rwightman/pytorch-image-models#models) since they offer a variety of models?
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[P] Image search with localization and open-vocabulary reranking.
I wanted to have a few choices getting localization into image search (index and search time). I immediately thought of using a region proposal network (rpn) from mask-rcnn to create patches that can also be indexed and searched (and add the localisation). I figured it might be somewhat agnostic to classes. I did not want to use mmdetection or detectron2 due to their dependencies and just getting the rpn was not worth it. I was encouraged by the PyTorch native implementations of detection/segmentation models but ended up finding yolox the best.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMDetection: OpenMMLab detection toolbox and benchmark.
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Removing the bounding box generated by OnnxRuntime segmentation
I have a semantic segmentation model trained using the mmdetection repo. Then it is converted to the ONNX format using the mmdeploy repo.
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Keras vs Tensorflow vs Pytorch for a Final year Project
E.g. If you consider it an object detection problem it is: detect and localise all the pedestrians in a frame, and classify them by their (intended) action. IMO the easiest way to do this would be with mmdetection, which is built on top of pytorch. Just label your dataset, build a config, and boom you have a model. Inference with that model in only a few lines of code, you won't really need to learn too much to get started.
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DeepSort with PyTorch(support yolo series)
MMDetection
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[D] Pre-trained networks and batch normalization
For example, in mmdetection, they expose options in their config & implementation to freeze batch norm layers in backbones and in this config, norm_eval is set to True meaning to freeze tracking of batch norm stats, while the ResNet backbone is frozen up to the 1st stage. Example of their backbone implementation can be found here.
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Config files in plain Python
MMDetection uses config Python scripting. It's easier to define nn.Module objects other than writing class name in a json config file
What are some alternatives?
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
FasterRCNN - Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.
yolov5 - YOLOv5 ๐ in PyTorch > ONNX > CoreML > TFLite
ultralytics - NEW - YOLOv8 ๐ in PyTorch > ONNX > OpenVINO > CoreML > TFLite
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
clipseg - This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots