Mmdetection Download Coco Dataset ##TOP##

Public datasets like Pascal VOC or mirror and COCO are available from official websites or mirrors. Note: In the detection task, Pascal VOC 2012 is an extension of Pascal VOC 2007 without overlap, and we usually use them together.It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to $MMDETECTION/data as below.If your folder structure is different, you may need to change the corresponding paths in config files.

We provide a script to download datasets such as COCO, you can run python tools/misc/download_dataset.py --dataset-name coco2017 to download COCO dataset.For users in China, more datasets can be downloaded from the opensource dataset platform: OpenDataLab.

Mmdetection Download Coco Dataset

Download 🔥 https://shurll.com/2y2Pv9 🔥

There are two types of annotations for COCO semantic segmentation, which differ mainly in the definition of category names, so there are two ways to handle them. The first is to directly use the stuffthingmaps dataset, and the second is to use the panoptic dataset.

To train efficiently and conveniently for users, we need to remove 12 unannotated classes before starting training or evaluation. The names of these 12 classes are: street sign, hat, shoe, eye glasses, plate, mirror, window, desk, door, blender, hair brush. The category information that can be used for training and evaluation can be found in mmdet/datasets/coco_semantic.py.

You can use tools/dataset_converters/coco_stuff164k.py to convert the downloaded stuffthingmaps to a dataset that can be directly used for training and evaluation. The directory structure of the converted dataset is as follows:

The number of categories in the semantic segmentation dataset generated through panoptic annotation will be less than that generated using the stuffthingmaps dataset. First, you need to prepare the panoptic segmentation annotations, and then use the following script to complete the conversion.

By using OpenDataLab, researchers can obtain free formatted datasets in various fields. Through the search function of the platform, researchers may address the dataset they look for quickly and easily. Using the formatted datasets from the platform, researchers can efficiently conduct tasks across datasets.

Currently, MIM supports downloading VOC and COCO datasets from OpenDataLab with one command line. More datasets will be supported in the future. You can also directly download the datasets you need from the OpenDataLab platform and then convert them to the format required by MMDetection.

The data field. Specifically, you need to explicitly add the metainfo=dict(classes=classes) fields in train_dataloader.dataset, val_dataloader.dataset and test_dataloader.dataset and classes must be a tuple type.

The annotation of the dataset must be in json or yaml, yml or pickle, pkl format; the dictionary stored in the annotation file must contain two fields metainfo and data_list. The metainfo is a dictionary, which contains the metadata of the dataset, such as class information; data_list is a list, each element in the list is a dictionary, the dictionary defines the raw data of one image, and each raw data contains a or several training/testing samples.

With existing dataset types, we can modify the metainfo of them to train subset of the annotations.For example, if you want to train only three classes of the current dataset,you can modify the classes of dataset.The dataset will filter out the ground truth boxes of other classes automatically.

Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when filter_cfg=dict(filter_empty_gt=True) and test_mode=False, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.

I'm working using Mmdetection to train a Deformable DETR model using a custom COCO Dataset. Meaning a Custom Dataset using the COCO format of annotations. The dataset uses the same images as the COCO with different "toy" annotations for a "playground" experiment and the annotation file was created using the packages pycocotools and json exclusively.

I have made five variations of this playground dataset: 2 datasets with three classes (classes 1, 2, and 3), 1 dataset with six classes (classes 1 to 6) and 2 datasets with 7 classes (classes 1 to 7).

For three of the datasets the amounts from the json file and from the constructed mmdet dataset are almost exactly equal. However, for one of the 3-classes dataset and for the 6-classes dataset, the results are incredibly different, where this code returns the following:

You can see that there is no "-1" id in the annotation json, and also some of the classes from the 3-classes dataset have 0 annotations, while the json clearly shows more than that. Has anyone encountered something similar using Mmdetection? What could be causing this problem?

It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.Actually, we define a simple annotation format and all existing datasets areprocessed to be compatible with it, either online or offline.

The annotation of a dataset is a list of dict, each dict corresponds to an image.There are 3 field filename (relative path), width, height for testing,and an additional field ann for training. ann is also a dict containing at least 2 fields:bboxes and labels, both of which are numpy arrays. Some datasets may provideannotations like crowd/difficult/ignored bboxes, we use bboxes_ignore and labels_ignoreto cover them.

We use ClassBalancedDataset as wrapper to repeat the dataset based on categoryfrequency. The dataset to repeat needs to instantiate function self.get_cat_ids(idx)to support ClassBalancedDataset.For example, to repeat Dataset_A with oversample_thr=1e-3, the config looks like the following

If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set separate_eval=False as below.

With existing dataset types, we can modify the class names of them to train subset of the annotations.For example, if you want to train only three classes of the current dataset,you can modify the classes of dataset.The dataset will filter out the ground truth boxes of other classes automatically.

To train a model with the MMDetection framework, we need a dataset in COCO format. In this tutorial, I will use the football-player-detection dataset. Feel free to replace it with your dataset or another dataset from Roboflow Universe.

Once we have a complete configuration file, most of the work is already behind us. All we have to do is run the train.py script and be patient. The training time depends on the chosen model architecture, the size of the dataset, and the hardware you have.

I recently published a post where I showed how to use DVC to maintain versions of our datasets so we reduce data reproducibility problems to a minimum. This is the second part of the tutorial where we are going to see how we can combine the power of mmdetection framework and its huge model zoo with DVC for designing ML pipelines, versioning our models and monitor training progress.

They have an extense documentation which really helps first time users. In this post I will skip the very basics and focus on showing how easily can we train a RetinaNet object detector on our coco_sample dataset.

Mmdetection framework also uses config files for datasets. There we define our train and validation data and which types of transformation do we want to apply before images are feed into the network. Since our dataset follows COCO format, I just modified original COCO_detection.py. Note that:

I have created a simple training script in src/train.py that adjusts to our needs. You could also use mmdetection train tool since I just applied some minor modifications to it that will allow us to use dvc params.

In this article, we investigate state of the art object detection algorithms and their implementations in MMDetection. We write an up-to-date guide for setting up and running MMDetection on a structured COCO-2017 dataset in Google Colab based on our experience. We analyse and classify the key component structure (detection head/neck/backbone) of MMdetection models. After which, we reproduce the object detection task for popular models on main-stream benchmarks including the COCO-2017 dataset and construct a datailed interactive table. In the last part of our work, we use signature object detection algorithms on real-live photos and videos taken at UCLA campus and provide error analysis. Finally, we perform basic adversarial attacks on our models using representave photos and evaluate their effects.

Now that we undstand the model config files, we can use them to customize and evaluate the models and backbones of our choice. Here is an example of evaluating the Verifocal-Net framework with a ResNet-50 Backbone (FPN neck) on the COCO-2017 dataset. We provide model setting file routes and complete execution results below.(Execution takes approx 20 min on 1 Nvidia T-100 GPU): ff782bc1db