LOGO检测-基于DETR目标检测算法实现的LOGO标志检测识别-附流程教程+项目源码-优质项目实战.zip

**DE⫶TR**: End-to-End Object Detection with Transformers ======== [](https://opensource.fb.com/support-ukraine) PyTorch training code and pretrained models for **DETR** (**DE**tection **TR**ansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining **42 AP** on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch.  **What it is**. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient. **About the code**. We believe that object detection should not be more difficult than classification, and should not require complex libraries for training and inference. DETR is very simple to implement and experiment with, and we provide a [standalone Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_demo.ipynb) showing how to do inference with DETR in only a few lines of PyTorch code. Training code follows this idea - it is not a library, but simply a [main.py](main.py) importing model and criterion definitions with standard training loops. Additionnally, we provide a Detectron2 wrapper in the d2/ folder. See the readme there for more information. For details see [End-to-End Object Detection with Transformers](https://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. # Model Zoo We provide baseline DETR and DETR-DC5 models, and plan to include more in future. AP is computed on COCO 2017 val5k, and inference time is over the first 100 val5k COCO images, with torchscript transformer. <table> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>backbone</th> <th>schedule</th> <th>inf_time</th> <th>box AP</th> <th>url</th> <th>size</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>DETR</td> <td>R50</td> <td>500</td> <td>0.036</td> <td>42.0</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r50_log.txt">logs</a></td> <td>159Mb</td> </tr> <tr> <th>1</th> <td>DETR-DC5</td> <td>R50</td> <td>500</td> <td>0.083</td> <td>43.3</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r50-dc5_log.txt">logs</a></td> <td>159Mb</td> </tr> <tr> <th>2</th> <td>DETR</td> <td>R101</td> <td>500</td> <td>0.050</td> <td>43.5</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r101_log.txt">logs</a></td> <td>232Mb</td> </tr> <tr> <th>3</th> <td>DETR-DC5</td> <td>R101</td> <td>500</td> <td>0.097</td> <td>44.9</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r101-dc5_log.txt">logs</a></td> <td>232Mb</td> </tr> </tbody> </table> COCO val5k evaluation results can be found in this [gist](https://gist.github.com/szagoruyko/9c9ebb8455610958f7deaa27845d7918). The models are also available via torch hub, to load DETR R50 with pretrained weights simply do: ```python model = torch.hub.load('facebookresearch/detr:main', 'detr_resnet50', pretrained=True) ``` COCO panoptic val5k models: <table> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>backbone</th> <th>box AP</th> <th>segm AP</th> <th>PQ</th> <th>url</th> <th>size</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>DETR</td> <td>R50</td> <td>38.8</td> <td>31.1</td> <td>43.4</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-panoptic-00ce5173.pth">download</a></td> <td>165Mb</td> </tr> <tr> <th>1</th> <td>DETR-DC5</td> <td>R50</td> <td>40.2</td> <td>31.9</td> <td>44.6</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-panoptic-da08f1b1.pth">download</a></td> <td>165Mb</td> </tr> <tr> <th>2</th> <td>DETR</td> <td>R101</td> <td>40.1</td> <td>33</td> <td>45.1</td> <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth">download</a></td> <td>237Mb</td> </tr> </tbody> </table> Checkout our [panoptic colab](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/DETR_panoptic.ipynb) to see how to use and visualize DETR's panoptic segmentation prediction. # Notebooks We provide a few notebooks in colab to help you get a grasp on DETR: * [DETR's hands on Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb): Shows how to load a model from hub, generate predictions, then visualize the attention of the model (similar to the figures of the paper) * [Standalone Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_demo.ipynb): In this notebook, we demonstrate how to implement a simplified version of DETR from the grounds up in 50 lines of Python, then visualize the predictions. It is a good starting point if you want to gain better understanding the architecture and poke around before diving in the codebase. * [Panoptic Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/DETR_panoptic.ipynb): Demonstrates how to use DETR for panoptic segmentation and plot the predictions. # Usage - Object detection There are no extra compiled components in DETR and package dependencies are minimal, so the code is very simple to use. We provide instructions how to install dependencies via conda. First, clone the repository locally: ``` git clone https://github.com/facebookresearch/detr.git ``` Then, install PyTorch 1.5+ and torchvision 0.6+: ``` conda install -c pytorch pytorch torchvision ``` Install pycocotools (for evaluation on COCO) and scipy (for training): ``` conda install cython scipy pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' ``` That's it, should be good to train and evaluate detection models. (optional) to work with panoptic install panopticapi: ``` pip install git+https://github.com/cocodataset/panopticapi.git ``` ## Data preparation Download and extract COCO 2017 train and val images with annotations from [http://cocodataset.org](http://cocodataset.org/#download). We expect the directory structure to be the following: ``` path/to/coco/ annotations/ # annotation json files train2017/ # train images val2017/ # val images ``` ## Training To train baseline DETR on a single node with 8 gpus for 300 epochs run: ``` python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --coco_path /path/to/coco ``` A single epoch takes 28 minutes, so 300 epoch training takes a