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范耀文
120c3c4ef7
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1 year ago | |
|---|---|---|
| ascend310_infer | 1 year ago | |
| data | 1 year ago | |
| model_utils | 1 year ago | |
| scripts | 1 year ago | |
| src | 1 year ago | |
| tracker | 1 year ago | |
| .DS_Store | 1 year ago | |
| DATASET_ZOO.md | 1 year ago | |
| README.md | 1 year ago | |
| README_CN.md | 1 year ago | |
| darknet53_pretrained_imagenet.ckpt | 1 year ago | |
| default_config.yaml | 1 year ago | |
| eval.py | 1 year ago | |
| eval_detect.py | 1 year ago | |
| export.py | 1 year ago | |
| infer.py | 1 year ago | |
| postprocess.py | 1 year ago | |
| preprocess.py | 1 year ago | |
| requirements.txt | 1 year ago | |
| train.py | 1 year ago | |
README.md
Contents
- Contents
JDE Description
Paper with introduced JDE model is dedicated to the improving efficiency of an MOT system.
It's introduce an early attempt that Jointly learns the Detector and Embedding model (JDE) in a single-shot deep network.
In other words, the proposed JDE employs a single network to simultaneously output detection results and the corresponding appearance embeddings of the detected boxes.
In comparison, SDE methods and two-stage methods are characterized by re-sampled pixels (bounding boxes) and feature maps, respectively.
Both the bounding boxes and feature maps are fed into a separate re-ID model for appearance feature extraction.
Method is near real-time while being almost as accurate as the SDE methods.
Paper: Towards Real-Time Multi-Object Tracking. Department of Electronic Engineering, Tsinghua University
Model Architecture
Architecture of the JDE is the Feature Pyramid Network (FPN).
FPN makes predictions from multiple scales, thus bringing improvement in pedestrian detection where the scale of targets varies a lot.
An input video frame first undergoes a forward pass through a backbone network to obtain feature maps at three scales, namely, scales with 1/32, 1/16 and 1/8 down-sampling rate, respectively.
Then, the feature map with the smallest size (also the semantically strongest features) is up-sampled and fused with the feature map from the second smallest scale by skip connection, and the same goes for the other scales.
Finally, prediction heads are added upon fused feature maps at all the three scales.
A prediction head consists of several stacked convolutional layers and outputs a dense prediction map of size (6A + D) × H × W, where A is the number of anchor templates assigned to this scale, and D is the dimension of the embedding.
Dataset
Used a large-scale training set by putting together six publicly available datasets on pedestrian detection, MOT and person search.
These datasets can be categorized into two types: ones that only contain bounding box annotations, and ones that have both bounding box and identity annotations.
The first category includes the ETH dataset and the CityPersons (CP) dataset. The second category includes the CalTech (CT) dataset, MOT16 (M16) dataset, CUHK-SYSU (CS) dataset and PRW dataset.
Training subsets of all these datasets are gathered to form the joint training set, and videos in the ETH dataset that overlap with the MOT-16 test set are excluded for fair evaluation.
Datasets preparations are described in DATASET_ZOO.md.
Datasets size: 134G, 1 object category (pedestrian).
Note: --dataset_root is used as an entry point for all datasets, used for training and evaluating this model.
Organize your dataset structure as follows:
.
└─dataset_root/
├─Caltech/
├─Cityscapes/
├─CUHKSYSU/
├─ETHZ/
├─MOT16/
├─MOT17/
└─PRW/
Information about train part of dataset.
| Dataset | ETH | CP | CT | M16 | CS | PRW | Total |
|---|---|---|---|---|---|---|---|
| # img | 2K | 3K | 27K | 53K | 11K | 6K | 54K |
| # box | 17K | 21K | 46K | 112K | 55K | 18K | 270K |
| # ID | - | - | 0.6K | 0.5K | 7K | 0.5K | 8.7K |
Environment Requirements
- Hardware（GPU/Ascend）
- Prepare hardware environment with GPU/Ascend processor.
- Framework
- For more information, please check the resources below：
Quick Start
After installing MindSpore through the official website, you can follow the steps below for training and evaluation,
in particular, before training, you need to install requirements.txt by following command pip install -r requirements.txt.
If an error occurred, update pip by
pip install --upgrade pipand try again.
If it didn't help install packages manually by usingpip install {package from requirements.txt}.
Note: The PyTorch is used only for checkpoint conversion.
All trainings will starts from pre-trained backbone,
download and convert the pre-trained on
ImageNet backbone with commands below:
# From the root model directory run
python -m src.convert_checkpoint --ckpt_url [PATH_TO_PYTORCH_CHECKPOINT]
- PATH_TO_PYTORCH_CHECKPOINT - Path to the downloaded darknet53 PyTorch checkpoint.
After converting the checkpoint and installing the requirements.txt, you can run the training scripts:
note: please change the parameter is_distributed of default_config.yaml file by running environment.
- running on GPU
for running on GPU,please modify default_config.yaml: device_target from Ascend to GPU
# Run standalone training example
bash scripts/run_standalone_train_gpu.sh [DEVICE_ID] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT]
# Run distribute training example
bash scripts/run_distribute_train_gpu.sh [DEVICE_NUM] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT]
- running on Ascend
# Run standalone training example
bash scripts/run_standalone_train.sh [DEVICE_ID] [LOGS_CKPT_DIR] [LOAD_DARKNET_URL] [DATASET_ROOT]
# Run distribute training example
bash scripts/run_distribute_train.sh [DEVICE_NUM] [LOGS_CKPT_DIR] [LOAD_DARKNET_URL] [DATASET_ROOT] [RANK_TABLE_FILE]
- DEVICE_ID - Device ID
- DEVICE_NUM - Device Number
- LOGS_CKPT_DIR - path to the directory, where the training results will be stored.
- CKPT_URL - Path to the converted pre-trained DarkNet53 backbone.
- DATASET_ROOT - Path to the dataset root directory (containing all dataset parts, described in DATASET_ZOO.md)
Script Description
Script and Sample Code
.
└─JDE
├─data
│ └─prepare_mot17.py # MOT17 data preparation script
├─model_utils
│ ├─ccmcpe.json # paths to dataset schema (defining relative paths structure)
│ ├─config.py # parameter parser
| ├─devide_adapter.py # device adapter script
| ├─local_adapter.py # local device adapter script
| └─moxing_adapter.py # distribute device adapter script
├─scripts
│ ├─run_eval_gpu.sh # launch evaluation on GPU
│ ├─run_eval.sh # launch evaluation on Ascend
| ├─run_infer_310.sh # launch infer on Ascend310
│ ├─run_distribute_train_gpu.sh # launch distribute train on GPU
│ ├─run_distribute_train.sh # launch distribute train on Ascend
│ ├─run_standalone_train_gpu.sh # launch standalone train on GPU
│ └─run_standalone_train.sh # launch standalone train on Ascend
├─src
│ ├─__init__.py
│ ├─convert_checkpoint.py # backbone checkpoint converter (torch to mindspore)
│ ├─darknet.py # backbone of network
│ ├─dataset.py # create dataset
│ ├─evaluation.py # motmetrics evaluator
│ ├─initializer.py # model initialization
│ ├─io.py # MOT evaluation utils
│ ├─kalman_filter.py # kalman filter script
│ ├─log.py # logger script
│ ├─model.py # create model script
│ ├─timer.py # timer script
│ ├─utils.py # utilities used in other scripts
│ └─visualization.py # visualization for inference
├─tracker
│ ├─__init__.py
│ ├─basetrack.py # base class for tracking
│ ├─matching.py # matching for tracking script
│ └─multitracker.py # tracker init script
├─ascend310_infer # Ascend310 inference source code
├─DATASET_ZOO.md # dataset preparing description
├─README.md
├─default_config.yaml # default configs
├─eval.py # evaluation script
├─eval_detect.py # detector evaluation script
├─export.py # export to MINDIR script
├─preprocess.py # 310 inference preprocess script
├─postprocess.py # 310 inference postprocess script
├─infer.py # inference script
├─requirements.txt
└─train.py # training script
Script Parameters
Parameters in config.py and default_config.yaml.
Include arguments for Train/Evaluation/Inference.
--config_path Path to default_config.yaml with hyperparameters and defaults
--data_cfg_url Path to .json with paths to datasets schemas
--momentum Momentum for SGD optimizer
--decay Weight_decay for SGD optimizer
--lr Init learning rate
--epochs Number of epochs to train
--batch_size Batch size per one device'
--num_classes Number of object classes
--k_max Max predictions per one map (made for optimization of FC layer embedding computation)
--img_size Size of input images
--track_buffer Tracking buffer
--keep_checkpoint_max Keep saved last N checkpoints
--backbone_input_shape Input filters of backbone layers
--backbone_shape Input filters of backbone layers
--backbone_layers Output filters of backbone layers
--out_channel Number of channels for detection
--embedding_dim Number of channels for embeddings
--iou_thres IOU thresholds
--conf_thres Confidence threshold
--nms_thres Threshold for Non-max suppression
--min_box_area Filter out tiny boxes
--anchor_scales 12 predefined anchor boxes. Different 4 per each of 3 feature maps
--col_names_train Names of columns for training GeneratorDataset
--col_names_val Names of columns for validation GeneratorDataset
--is_distributed Distribute training or not
--dataset_root Path to datasets root folder
--device_target Device GPU or any
--device_id Device id of target device
--device_start Start device id
--ckpt_url Location of checkpoint
--logs_dir Dir to save logs and ckpt
--input_video Path to the input video
--output_format Expected output format
--output_root Expected output root path
--save_images Save tracking results (image)
--save_videos Save tracking results (video)
--file_format: "MINDIR" # ["AIR", "ONNX", "MINDIR"]
--infer310: whether infer310
Training Process
Standalone Training
Note: For all trainings necessary to use pretrained backbone darknet53 and please change the parameter is_distributed of default_config.yaml file by running environment
- running on Ascend
bash scripts/run_standalone_train.sh [DEVICE_ID] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT]
- running on GPU
bash scripts/run_standalone_train_gpu.sh [DEVICE_ID] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT]
- DEVICE_ID - device ID
- LOGS_CKPT_DIR - path to the directory, where the training results will be stored.
- CKPT_URL - Path to the converted pre-trained DarkNet53 backbone.
- DATASET_ROOT - Path to the dataset root directory (containing all dataset parts, described in DATASET_ZOO.md)
The above command will run in the background, you can view the result through the generated standalone_train.log file.
After training, you can get the training loss and time logs in chosen logs_dir.
The model checkpoints will be saved in LOGS_CKPT_DIR directory.
Distribute Training
- running on Ascend
bash scripts/run_distribute_train.sh [DEVICE_NUM] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT] [RANK_TABLE_FILE]
epoch: 50 step: 1072, loss is -14.7477
epoch: 50 step: 1072, loss is -11.798253
epoch: 50 step: 1172, loss is -18.3406
epoch: 50 step: 1172, loss is -20.445492
epoch: 50 step: 1172, loss is -13.039152
epoch: 50 step: 1172, loss is -16.564177epoch: 50 step: 1172, loss is -11.598206
epoch: 50 step: 1172, loss is -12.547735
epoch: 50 step: 1172, loss is -16.880716
epoch: 50 step: 1172, loss is -19.950447
epoch: 50 step: 1272, loss is -16.732803
epoch: 50 step: 1272, loss is -19.284496
epoch: 50 step: 1272, loss is -13.200997
epoch: 50 step: 1272, loss is -20.25792
epoch: 50 step: 1272, loss is -18.699135epoch: 50 step: 1272, loss is -17.323572
epoch: 50 step: 1272, loss is -19.999825
epoch: 50 step: 1272, loss is -15.697657
epoch: 50 step: 1372, loss is -7.870503
epoch: 50 step: 1372, loss is -18.403461
epoch: 50 step: 1372, loss is -19.886168
epoch: 50 step: 1372, loss is -16.99081
epoch: 50 step: 1372, loss is -16.271353epoch: 50 step: 1372, loss is -18.211508
epoch: 50 step: 1372, loss is -16.854897
epoch: 50 step: 1372, loss is -15.579943
epoch: 50 step: 1472, loss is -19.984272
epoch: 50 step: 1472, loss is -12.321886
epoch: 50 step: 1472, loss is -17.311712epoch: 50 step: 1472, loss is -17.272167
epoch: 50 step: 1472, loss is -18.001553
epoch: 50 step: 1472, loss is -18.723167
epoch: 50 step: 1472, loss is -16.447176
epoch: 50 step: 1472, loss is -20.523182
epoch: 50 step: 1572, loss is -20.207062
epoch: 50 step: 1572, loss is -19.602242
epoch: 50 step: 1572, loss is -19.595556
epoch: 50 step: 1572, loss is -15.631174epoch: 50 step: 1572, loss is -19.064281
epoch: 50 step: 1572, loss is -17.65372
epoch: 50 step: 1572, loss is -19.230976epoch: 50 step: 1572, loss is -17.307154
epoch: 50 step: 1672, loss is -20.424273
epoch: 50 step: 1672, loss is -19.456707epoch: 50 step: 1672, loss is -18.910358
epoch: 50 step: 1672, loss is -15.7901325
epoch: 50 step: 1672, loss is -17.905373epoch: 50 step: 1672, loss is -18.419804
epoch: 50 step: 1672, loss is -19.16711
epoch: 50 step: 1672, loss is -17.312708
epoch time: 710695.457 ms, per step time: 425.057 ms
epoch time: 710700.617 ms, per step time: 425.060 ms
epoch time: 710695.830 ms, per step time: 425.057 msepoch time: 710700.808 ms, per step time: 425.060 ms
epoch time: 710702.623 ms, per step time: 425.061 ms
epoch time: 710703.826 ms, per step time: 425.062 ms
epoch time: 711144.133 ms, per step time: 425.325 ms
train success
train success
train success
train success
train success
train success
- running on GPU
bash scripts/run_distribute_train_gpu.sh [DEVICE_NUM] [LOGS_CKPT_DIR] [CKPT_URL] [DATASET_ROOT]
- DEVICE_ID - device ID
- LOGS_CKPT_DIR - path to the directory, where the training results will be stored.
- CKPT_URL - Path to the converted pre-trained DarkNet53 backbone.
- DATASET_ROOT - Path to the dataset root directory (containing all dataset parts, described in DATASET_ZOO.md)
The above shell script will run the distributed training in the background.
Here is the example of the training logs:
epoch: 30 step: 1612, loss is -4.7679796
epoch: 30 step: 1612, loss is -5.816874
epoch: 30 step: 1612, loss is -5.302864
epoch: 30 step: 1612, loss is -5.775913
epoch: 30 step: 1612, loss is -4.9537477
epoch: 30 step: 1612, loss is -4.3535285
epoch: 30 step: 1612, loss is -5.0773625
epoch: 30 step: 1612, loss is -4.2019467
epoch time: 2023042.925 ms, per step time: 1209.954 ms
epoch time: 2023069.500 ms, per step time: 1209.970 ms
epoch time: 2023097.331 ms, per step time: 1209.986 ms
epoch time: 2023038.221 ms, per step time: 1209.951 ms
epoch time: 2023098.113 ms, per step time: 1209.987 ms
epoch time: 2023093.300 ms, per step time: 1209.984 ms
epoch time: 2023078.631 ms, per step time: 1209.975 ms
epoch time: 2017509.966 ms, per step time: 1206.645 ms
train success
train success
train success
train success
train success
train success
train success
train success
Evaluation Process
Evaluation
Tracking ability of the model is tested on the train part of the MOT16 dataset (doesn't use during training).
To start tracker evaluation run the command below.
bash scripts/run_eval[_gpu].sh [DEVICE_ID] [CKPT_URL] [DATASET_ROOT]
- DEVICE_ID - Device ID.
- CKPT_URL - Path to the trained JDE model.
- DATASET_ROOT - Path to the dataset root directory (containing all dataset parts, described in DATASET_ZOO.md).
Note: the script expects that the DATASET_ROOT directory contains the MOT16 sub-folder.
The above python command will run in the background. The validation logs will be saved in "eval.log".
For more details about motmetrics, you can refer to MOT benchmark.
- Evaluation on Ascend
DATE-DATE-DATE TIME:TIME:TIME [INFO]: Time elapsed: 323.49 seconds, FPS: 16.39
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm
MOT16-02 54.4% 59.6% 50.0% 74.4% 88.7% 54 25 24 5 1698 4557 345 530 63.0% 0.211 149 83 8
MOT16-04 72.4% 79.7% 66.3% 79.2% 95.3% 83 39 33 11 1860 9884 191 424 74.9% 0.210 72 59 3
MOT16-05 70.1% 75.3% 65.6% 79.6% 91.3% 125 64 52 9 518 1394 129 208 70.1% 0.216 80 54 26
MOT16-09 57.7% 63.8% 52.7% 77.6% 94.0% 25 16 8 1 262 1175 130 161 70.2% 0.193 68 34 3
MOT16-10 62.7% 65.7% 60.0% 81.5% 89.2% 54 30 24 0 1219 2284 313 506 69.0% 0.229 136 72 5
MOT16-11 70.7% 72.7% 68.8% 88.8% 93.7% 69 46 21 2 544 1031 82 144 81.9% 0.184 28 33 4
MOT16-13 70.7% 77.2% 65.2% 78.5% 92.9% 107 63 38 6 685 2462 236 539 70.5% 0.219 115 70 32
OVERALL 67.2% 72.9% 62.4% 79.4% 92.8% 517 283 200 34 6786 22787 1426 2512 71.9% 0.210 648 405 81
- Evaluation on GPU
DATE-DATE-DATE TIME:TIME:TIME [INFO]: Time elapsed: 240.54 seconds, FPS: 22.04
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm
MOT16-02 45.1% 49.9% 41.2% 71.0% 86.0% 54 17 31 6 2068 5172 425 619 57.0% 0.215 239 68 14
MOT16-04 69.5% 75.5% 64.3% 80.6% 94.5% 83 45 24 14 2218 9234 175 383 75.6% 0.184 98 28 3
MOT16-05 63.6% 68.1% 59.7% 82.0% 93.7% 125 67 49 9 376 1226 137 210 74.5% 0.203 113 40 40
MOT16-09 55.2% 60.4% 50.8% 78.1% 92.9% 25 16 8 1 316 1152 108 147 70.0% 0.187 76 15 11
MOT16-10 57.1% 59.9% 54.5% 80.1% 88.1% 54 28 26 0 1337 2446 376 569 66.2% 0.228 202 66 16
MOT16-11 75.0% 76.4% 73.7% 89.6% 92.9% 69 50 16 3 626 953 78 137 81.9% 0.159 49 24 12
MOT16-13 64.8% 69.9% 60.3% 78.5% 90.9% 107 58 43 6 900 2463 272 528 68.3% 0.223 200 59 48
OVERALL 63.2% 68.1% 58.9% 79.5% 91.8% 517 281 197 39 7841 22646 1571 2593 71.0% 0.196 977 300 144
To evaluate detection ability (get mAP, Precision and Recall metrics) of the model, run command below.
python eval_detect.py --device_id [DEVICE_ID] --ckpt_url [CKPT_URL] --dataset_root [DATASET_ROOT]
- DEVICE_ID - Device ID.
- CKPT_URL - Path to the trained JDE model.
- DATASET_ROOT - Path to the dataset root directory (containing all dataset parts, described in DATASET_ZOO.md).
Results of evaluation will be visualized at command line.
- evaluation on Ascend
Image Total P R mAP
4000 30353 0.849 0.782 0.771 0.271s
8000 30353 0.878 0.796 0.785 0.253s
12000 30353 0.869 0.814 0.801 0.259s
16000 30353 0.873 0.823 0.811 0.287s
20000 30353 0.881 0.833 0.822 0.26s
24000 30353 0.886 0.842 0.832 0.261s
28000 30353 0.887 0.838 0.828 0.275s
mean_mAP: 0.8214, mean_R: 0.8316, mean_P: 0.8843
- evaluation on GPU
Image Total P R mAP
4000 30353 0.829 0.778 0.765 0.426s
8000 30353 0.863 0.798 0.788 0.42s
12000 30353 0.854 0.815 0.802 0.419s
16000 30353 0.857 0.821 0.809 0.582s
20000 30353 0.865 0.834 0.824 0.413s
24000 30353 0.868 0.841 0.832 0.415s
28000 30353 0.874 0.839 0.83 0.419s
mean_mAP: 0.8225, mean_R: 0.8325, mean_P: 0.8700
GPU Inference Process
Usage
To compile video from frames with predicted bounding boxes, you need to install ffmpeg by using
sudo apt-get install ffmpeg. Video compiling will happen automatically.
python infer.py --device_id [DEVICE_ID] --ckpt_url [CKPT_URL] --input_video [INPUT_VIDEO]
- DEVICE_ID - Device ID.
- CKPT_URL - Path to the trained JDE model.
- INPUT_VIDEO - Path to the input video to tracking.
Result
Results of the inference will be saved into default ./results folder, logs will be shown at command line.
Export Process
change some parameters of default_config.yaml, such as ckpt_url、img_size、file_format.
python export.py --ckpt_url [CKPT_URL] --file_format [FILE_FORMAT]
-
CKPT_URL - Path to the trained JDE model.
-
file_format - choose from["AIR", "MINDIR"]
Ascend Inference Process
Before inference we need to export the model first. Air models can only be exported on the Ascend 910 environment, mindir can be exported on any environment. batch_size only supports 1.
# Ascend310 inference
bash scripts/run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
-
MINDIR_PATH - Path to exported model.
-
DATA_PATH - Inference dataset MOT16 path, (PATH TO/MOT16/train).
-
DEVICE_ID - Device ID.
Model Description
Performance
Training Performance
- running on Ascend
| Parameters | Ascend (8p) |
|---|---|
| Model | JDE (1088*608) |
| Hardware | Ascend: 8 * Ascend-910 |
| Upload Date | 06/08/2022 (day/month/year) |
| MindSpore Version | 1.5.0 |
| Dataset | Joint Dataset (see DATASET_ZOO.md) |
| Training Parameters | epoch=30, batch_size=4 (per device), lr=0.01, momentum=0.9, weight_decay=0.0001 |
| Optimizer | SGD |
| Loss Function | SmoothL1Loss, SoftmaxCrossEntropyWithLogits (and apply auto-balancing loss strategy) |
| Outputs | Tensor of bbox cords, conf, class, emb |
| Speed | Eight cards: ~425 ms/step |
| Total time | Eight cards: ~15 hours |
- running on GPU
| Parameters | GPU (8p) |
|---|---|
| Model | JDE (1088*608) |
| Hardware | 8 Nvidia RTX 3090, Intel Xeon Gold 6226R CPU @ 2.90GHz |
| Upload Date | 02/02/2022 (day/month/year) |
| MindSpore Version | 1.5.0 |
| Dataset | Joint Dataset (see DATASET_ZOO.md) |
| Training Parameters | epoch=30, batch_size=4 (per device), lr=0.01, momentum=0.9, weight_decay=0.0001 |
| Optimizer | SGD |
| Loss Function | SmoothL1Loss, SoftmaxCrossEntropyWithLogits (and apply auto-balancing loss strategy) |
| Outputs | Tensor of bbox cords, conf, class, emb |
| Speed | Eight cards: ~1206 ms/step |
| Total time | Eight cards: ~17 hours |
Evaluation Performance
- evaluation on Ascend
| Parameters | Ascend(1p) |
|---|---|
| Model | JDE (1088*608) |
| Resource | Ascend: 1 * Ascend-910 |
| Upload Date | 02/02/2022 (day/month/year) |
| MindSpore Version | 1.5.0 |
| Dataset | MOT-16 |
| Batch_size | 1 |
| Outputs | Metrics, .txt predictions |
| FPS | 16.39 |
| Metrics | mAP 82.14, MOTA 71.9% |
- evaluation on GPU
| Parameters | GPU (1p) |
|---|---|
| Model | JDE (1088*608) |
| Resource | 1 Nvidia RTX 3090, Intel Xeon Gold 6226R CPU @ 2.90GHz |
| Upload Date | 02/02/2022 (day/month/year) |
| MindSpore Version | 1.5.0 |
| Dataset | MOT-16 |
| Batch_size | 1 |
| Outputs | Metrics, .txt predictions |
| FPS | 22.04 |
| Metrics | mAP 82.2, MOTA 71.0% |
ModelZoo Homepage
Please check the official homepage.
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