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+# FAQ
+
+We list some common issues faced by many users and their corresponding solutions here.
+Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them.
+If the contents here do not cover your issue, please create an issue using the [provided templates](/.github/ISSUE_TEMPLATE/error-report.md) and make sure you fill in all required information in the template.
+
+## Installation
+
+- **Unable to install xtcocotools**
+
+  1. Try to install it using pypi manually `pip install xtcocotools`.
+  1. If step1 does not work. Try to install it from [source](https://github.com/jin-s13/xtcocoapi).
+
+  ```
+  git clone https://github.com/jin-s13/xtcocoapi
+  cd xtcocoapi
+  python setup.py install
+  ```
+
+- **No matching distribution found for xtcocotools>=1.6**
+
+  1. Install cython by `pip install cython`.
+  1. Install xtcocotools from [source](https://github.com/jin-s13/xtcocoapi).
+
+  ```
+  git clone https://github.com/jin-s13/xtcocoapi
+  cd xtcocoapi
+  python setup.py install
+  ```
+
+- **"No module named 'mmcv.ops'"; "No module named 'mmcv._ext'"**
+
+  1. Uninstall existing mmcv in the environment using `pip uninstall mmcv`.
+  1. Install mmcv-full following the [installation instruction](https://mmcv.readthedocs.io/en/latest/#installation).
+
+## Data
+
+- **How to convert my 2d keypoint dataset to coco-type?**
+
+  You may refer to this conversion [tool](https://github.com/open-mmlab/mmpose/blob/master/tools/dataset/parse_macaquepose_dataset.py) to prepare your data.
+  Here is an [example](https://github.com/open-mmlab/mmpose/blob/master/tests/data/macaque/test_macaque.json) of the coco-type json.
+  In the coco-type json, we need "categories", "annotations" and "images". "categories" contain some basic information of the dataset, e.g. class name and keypoint names.
+  "images" contain image-level information. We need "id", "file_name", "height", "width". Others are optional.
+  Note: (1) It is okay that "id"s are not continuous or not sorted (e.g. 1000, 40, 352, 333 ...).
+
+  "annotations" contain instance-level information. We need "image_id", "id", "keypoints", "num_keypoints", "bbox", "iscrowd", "area", "category_id". Others are optional.
+  Note: (1) "num_keypoints" means the number of visible keypoints. (2) By default, please set "iscrowd: 0". (3) "area" can be calculated using the bbox (area = w * h) (4) Simply set "category_id: 1". (5) The "image_id" in "annotations" should match the "id" in "images".
+
+- **What if my custom dataset does not have bounding box label?**
+
+  We can estimate the bounding box of a person as the minimal box that tightly bounds all the keypoints.
+
+- **What if my custom dataset does not have segmentation label?**
+
+  Just set the `area` of the person as the area of the bounding boxes. During evaluation, please set `use_area=False` as in this [example](https://github.com/open-mmlab/mmpose/blob/a82dd486853a8a471522ac06b8b9356db61f8547/mmpose/datasets/datasets/top_down/topdown_aic_dataset.py#L113).
+
+- **What is `COCO_val2017_detections_AP_H_56_person.json`? Can I train pose models without it?**
+
+  "COCO_val2017_detections_AP_H_56_person.json" contains the "detected" human bounding boxes for COCO validation set, which are generated by FasterRCNN.
+  One can choose to use gt bounding boxes to evaluate models, by setting `use_gt_bbox=True` and `bbox_file=''`. Or one can use detected boxes to evaluate
+  the generalizability of models, by setting `use_gt_bbox=False` and `bbox_file='COCO_val2017_detections_AP_H_56_person.json'`.
+
+## Training
+
+- **RuntimeError: Address already in use**
+
+  Set the environment variables `MASTER_PORT=XXX`. For example,
+  `MASTER_PORT=29517 GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh Test res50 configs/body/2D_Kpt_SV_RGB_Img/topdown_hm/coco/res50_coco_256x192.py work_dirs/res50_coco_256x192`
+
+- **"Unexpected keys in source state dict" when loading pre-trained weights**
+
+  It's normal that some layers in the pretrained model are not used in the pose model. ImageNet-pretrained classification network and the pose network may have different architectures (e.g. no classification head). So some unexpected keys in source state dict is actually expected.
+
+- **How to use trained models for backbone pre-training ?**
+
+  Refer to [Use Pre-Trained Model](/docs/en/tutorials/1_finetune.md#use-pre-trained-model),
+  in order to use the pre-trained model for the whole network (backbone + head), the new config adds the link of pre-trained models in the `load_from`.
+
+  And to use backbone for pre-training, you can change `pretrained` value in the backbone dict of config files to the checkpoint path / url.
+  When training, the unexpected keys will be ignored.
+
+- **How to visualize the training accuracy/loss curves in real-time ?**
+
+  Use `TensorboardLoggerHook` in `log_config` like
+
+  ```python
+  log_config=dict(interval=20, hooks=[dict(type='TensorboardLoggerHook')])
+  ```
+
+  You can refer to [tutorials/6_customize_runtime.md](/tutorials/6_customize_runtime.md#log-config) and the example [config](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L26).
+
+- **Log info is NOT printed**
+
+  Use smaller log interval. For example, change `interval=50` to `interval=1` in the [config](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L23).
+
+- **How to fix stages of backbone when finetuning a model ?**
+
+    You can refer to [`def _freeze_stages()`](https://github.com/open-mmlab/mmpose/blob/d026725554f9dc08e8708bd9da8678f794a7c9a6/mmpose/models/backbones/resnet.py#L618) and [`frozen_stages`](https://github.com/open-mmlab/mmpose/blob/d026725554f9dc08e8708bd9da8678f794a7c9a6/mmpose/models/backbones/resnet.py#L498),
+    reminding to set `find_unused_parameters = True` in config files for distributed training or testing.
+
+## Evaluation
+
+- **How to evaluate on MPII test dataset?**
+  Since we do not have the ground-truth for test dataset, we cannot evaluate it 'locally'.
+  If you would like to evaluate the performance on test set, you have to upload the pred.mat (which is generated during testing) to the official server via email, according to [the MPII guideline](http://human-pose.mpi-inf.mpg.de/#evaluation).
+
+- **For top-down 2d pose estimation, why predicted joint coordinates can be out of the bounding box (bbox)?**
+  We do not directly use the bbox to crop the image. bbox will be first transformed to center & scale, and the scale will be multiplied by a factor (1.25) to include some context. If the ratio of width/height is different from that of model input (possibly 192/256), we will adjust the bbox.
+
+## Inference
+
+- **How to run mmpose on CPU?**
+
+  Run demos with `--device=cpu`.
+
+- **How to speed up inference?**
+
+  For top-down models, try to edit the config file. For example,
+
+  1. set `flip_test=False` in [topdown-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L51).
+  1. set `post_process='default'` in [topdown-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L54).
+  1. use faster human bounding box detector, see [MMDetection](https://mmdetection.readthedocs.io/en/latest/model_zoo.html).
+
+  For bottom-up models, try to edit the config file. For example,
+
+  1. set `flip_test=False` in [AE-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/bottom_up/resnet/coco/res50_coco_512x512.py#L91).
+  1. set `adjust=False` in [AE-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/bottom_up/resnet/coco/res50_coco_512x512.py#L89).
+  1. set `refine=False` in [AE-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/bottom_up/resnet/coco/res50_coco_512x512.py#L90).
+  1. use smaller input image size in [AE-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/bottom_up/resnet/coco/res50_coco_512x512.py#L39).
+
+## Deployment
+
+- **Why is the onnx model converted by mmpose throwing error when converting to other frameworks such as TensorRT?**
+
+    For now, we can only make sure that models in mmpose are onnx-compatible. However, some operations in onnx may be unsupported by your target framework for deployment, e.g. TensorRT in [this issue](https://github.com/open-mmlab/mmaction2/issues/414). When such situation occurs, we suggest you raise an issue and ask the community to help as long as `pytorch2onnx.py` works well and is verified numerically.