- Multi-Object Tracking(MOT) datasets are always used for single category tracking. DeepSORT, JDE and FairMOT are single category MOT models. 'MIX' dataset and it's sub datasets are also single category pedestrian tracking datasets. It can be considered that there are additional IDs ground truth for detection datasets.
- In order to train the feature models of more scenes, more datasets are also processed into the same format as the MIX dataset. PaddleDetection Team also provides feature datasets and models of [vehicle tracking](vehicle/readme.md), [head tracking](headtracking21/readme.md) and more general [pedestrian tracking](pedestrian/readme.md). User defined datasets can also be prepared by referring to data preparation [doc](../../docs/tutorials/PrepareMOTDataSet.md).
- In order to train the feature models of more scenes, more datasets are also processed into the same format as the MIX dataset. PaddleDetection Team also provides feature datasets and models of [vehicle tracking](vehicle/README.md), [head tracking](headtracking21/README.md) and more general [pedestrian tracking](pedestrian/README.md). User defined datasets can also be prepared by referring to data preparation [doc](../../docs/tutorials/data/PrepareMOTDataSet.md).
- The multipe category MOT model is [MCFairMOT] (mcfairmot/readme_cn.md), and the multi category dataset is the integrated version of VisDrone dataset. Please refer to the doc of [MCFairMOT](mcfairmot/README.md).
- The Multi-Target Multi-Camera Tracking (MTMCT) model is [AIC21 MTMCT](https://www.aicitychallenge.org)(CityFlow) Multi-Camera Vehicle Tracking dataset. The dataset and model can refer to the doc of [MTMCT](mtmct/README.md)
@@ -91,7 +91,7 @@ COCO datasets are currently divided into COCO2014 and COCO2017, which are mainly
│ │ ...
```
class `COCODataSet` is defined and registered on `source/coco.py`. And implements the parse the dataset method, called [COCO API](https://github.com/cocodataset/cocoapi) to load and parse COCO format data source` roidbs ` and ` cname2cid `, See `source/coco.py`source code for details. Converting other datasets to COCO format can be done by referring to [converting User Data to COCO Data](../tutorials/PrepareDataSet_en.md#convert-user-data-to-coco-data)
And implements the parse the dataset method, called [COCO API](https://github.com/cocodataset/cocoapi) to load and parse COCO format data source`roidbs` and `cname2cid`, See `source/coco.py`source code for details. Converting other datasets to COCO format can be done by referring to [converting User Data to COCO Data](../tutorials/PrepareDataSet_en.md#convert-user-data-to-coco-data)
And implements the parse the dataset method, called [COCO API](https://github.com/cocodataset/cocoapi) to load and parse COCO format data source`roidbs` and `cname2cid`, See `source/coco.py`source code for details. Converting other datasets to COCO format can be done by referring to [converting User Data to COCO Data](../tutorials/data/PrepareDetDataSet_en.md#convert-user-data-to-coco-data)
#### 2.2Pascal VOC dataset
...
...
@@ -120,7 +120,7 @@ The dataset is currently divided into VOC2007 and VOC2012, mainly composed of XM
│ ├── ImageSets
│ │ ...
```
The `VOCDataSet` dataset is defined and registered in `source/voc.py` . It inherits the `DetDataSet` base class and rewrites the `parse_dataset` method to parse XML annotations in the VOC dataset. Update `roidbs` and `cname2cid`. To convert other datasets to VOC format, refer to [User Data to VOC Data](../tutorials/PrepareDataSet_en.md#convert-user-data-to-voc-data)
The `VOCDataSet` dataset is defined and registered in `source/voc.py` . It inherits the `DetDataSet` base class and rewrites the `parse_dataset` method to parse XML annotations in the VOC dataset. Update `roidbs` and `cname2cid`. To convert other datasets to VOC format, refer to [User Data to VOC Data](../tutorials/data/PrepareDetDataSet_en.md#convert-user-data-to-voc-data)
