make_voc_dataset
detectron2
| make_voc_dataset | detectron2 | |
|---|---|---|
| 1 | 49 | |
| 3 | 28,744 | |
| - | 1.3% | |
| 0.0 | 7.6 | |
| over 2 years ago | 10 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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make_voc_dataset
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My first Pypi Package (make-voc-dataset)
Github: https://github.com/Razin-Tailor/make_voc_dataset
detectron2
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Ask HN: How to train an image recognition AI
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
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Instance segmentation of small objects in grainy drone imagery
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
- Openpose alternatives (humanSD & Densepose)
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Probelms with importing tensormask from detectron2.projects
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
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Problems with Lazy Config detectron2 (MViTv2)
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.
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"[D]" Problems with Lazy Config detectron2 (MViTv2)
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.
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AI Real Time (lgd for cn)
Which is built on https://github.com/facebookresearch/detectron2
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List of AI-Models
Click to Learn more...
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good computer vision or deep learning projects in github
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.
- Object Detection using PyTorch: Would you recommend a Framework (Detectron2, MMDetection, ...) or a project from scratch ?
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
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]
rembg - Rembg is a tool to remove images background
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
car-damage-detection - Detectron2 for car damage detection using custom dataset
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
ai-background-remove - Cut out objects and remove backgrounds from pictures with artificial intelligence
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.