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Video Detection Module Tutorial

I. Overview

Video detection tasks are a critical component of computer vision systems, focusing on identifying and locating objects or events within video sequences. Video detection involves decomposing the video into individual frame sequences and then analyzing these frames to recognize detected objects or actions, such as detecting pedestrians in surveillance videos or identifying specific activities like "running," "jumping," or "playing guitar" in sports or entertainment videos.

The output of the video detection module includes bounding boxes and class labels for each detected object or event. This information can be used by other modules or systems for further analysis, such as tracking the movement of detected objects, generating alerts, or compiling statistics for decision-making processes. Therefore, video detection plays an important role in various applications ranging from security surveillance and autonomous driving to sports analytics and content moderation.

II. List of Supported Models

Model	Model Download Link	Frame-mAP (@ IoU 0.5)	Model Storage Size (M)	Description
YOWO	Inference Model/训练模型	80.94	462.891M	YOWO is a single-stage network with two branches. One branch extracts spatial features of key frames (i.e., the current frame) through a 2D-CNN, while the other branch captures spatiotemporal features of a clip composed of previous frames using a 3D-CNN. To accurately aggregate these features, YOWO employs a channel fusion and attention mechanism to maximize the utilization of inter-channel dependencies. Finally, the fused features are used for frame-level detection.

Test Environment Description:

  <li><b>Performance Test Environment</b>
      <ul>
           <li><strong>Test Dataset：</strong> <a href="http://www.thumos.info/download.html">UCF101-24</a> test set.</li>
          <li><strong>Hardware Configuration：</strong>
              <ul>
                  <li>GPU: NVIDIA Tesla T4</li>
                  <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
                  <li>Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2</li>
              </ul>
          </li>
      </ul>
  </li>
  <li><b>Inference Mode Description</b></li>

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Normal Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Optimal combination of pre-selected precision types and acceleration strategies	FP32 Precision / 8 Threads	Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.)

III. Quick Integration

❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the PaddleX Local Installation Guide.

After installing the wheel package, you can complete video Detection module inference with just a few lines of code. You can switch between models in this module freely, and you can also integrate the model inference of the video Detection module into your project. Before running the following code, please download the demo video to your local machine.

from paddlex import create_model
model = create_model(model_name="YOWO")
output = model.predict(input="HorseRiding.avi", batch_size=1)
for res in output:
    res.print()
    res.save_to_video(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

👉 After running, the result is: (Click to expand)

```bash {'res': {'input_path': 'HorseRiding.avi', 'result': [[[[110, 40, 170, 171], 0.8385784886274905, 'HorseRiding']], [[[112, 31, 168, 167], 0.8587647461352432, 'HorseRiding']], [[[106, 28, 164, 165], 0.8579590929730969, 'HorseRiding']], [[[106, 24, 165, 171], 0.8743957465404151, 'HorseRiding']], [[[107, 22, 165, 172], 0.8488322619908999, 'HorseRiding']], [[[112, 22, 173, 171], 0.8446755521458691, 'HorseRiding']], [[[115, 23, 177, 176], 0.8454028365262367, 'HorseRiding']], [[[117, 22, 178, 179], 0.8484261880748285, 'HorseRiding']], [[[117, 22, 181, 181], 0.8319480115446183, 'HorseRiding']], [[[117, 39, 182, 183], 0.820551099084625, 'HorseRiding']], [[[117, 41, 183, 185], 0.8202395831914338, 'HorseRiding']], [[[121, 47, 185, 190], 0.8261058921745246, 'HorseRiding']], [[[123, 46, 188, 196], 0.8307278306829033, 'HorseRiding']], [[[125, 44, 189, 197], 0.8259781361122833, 'HorseRiding']], [[[128, 47, 191, 195], 0.8227593229866699, 'HorseRiding']], [[[127, 44, 192, 193], 0.8205373129456817, 'HorseRiding']], [[[129, 39, 192, 185], 0.8223318812628619, 'HorseRiding']], [[[127, 31, 196, 179], 0.8501208612019866, 'HorseRiding']], [[[128, 22, 193, 171], 0.8315708410681566, 'HorseRiding']], [[[130, 22, 192, 169], 0.8318588228062005, 'HorseRiding']], [[[132, 18, 193, 170], 0.8310494469100611, 'HorseRiding']], [[[132, 18, 194, 172], 0.8302132445350239, 'HorseRiding']], [[[133, 18, 194, 176], 0.8339063714162727, 'HorseRiding']], [[[134, 26, 200, 183], 0.8365876380675275, 'HorseRiding']], [[[133, 16, 198, 182], 0.8395230321418268, 'HorseRiding']], [[[133, 17, 199, 184], 0.8198139782724922, 'HorseRiding']], [[[140, 28, 204, 189], 0.8344166596681291, 'HorseRiding']], [[[139, 27, 204, 187], 0.8412694521771158, 'HorseRiding']], [[[139, 28, 204, 185], 0.8500098862888805, 'HorseRiding']], [[[135, 19, 199, 179], 0.8506627974981384, 'HorseRiding']], [[[132, 15, 201, 178], 0.8495054272547193, 'HorseRiding']], [[[136, 14, 199, 173], 0.8451630721500223, 'HorseRiding']], [[[136, 12, 200, 167], 0.8366456814214907, 'HorseRiding']], [[[133, 8, 200, 168], 0.8457252233401213, 'HorseRiding']], [[[131, 7, 197, 162], 0.8400586356358062, 'HorseRiding']], [[[131, 8, 195, 163], 0.8320492682901985, 'HorseRiding']], [[[129, 4, 194, 159], 0.8298043752822792, 'HorseRiding']], [[[127, 5, 194, 162], 0.8348390851948722, 'HorseRiding']], [[[125, 7, 190, 164], 0.8299688814865505, 'HorseRiding']], [[[125, 6, 191, 164], 0.8303107088154711, 'HorseRiding']], [[[123, 8, 190, 168], 0.8348342187965798, 'HorseRiding']], [[[125, 14, 189, 170], 0.8356523950497134, 'HorseRiding']], [[[127, 18, 191, 171], 0.8392671764931521, 'HorseRiding']], [[[127, 30, 193, 178], 0.8441704160826191, 'HorseRiding']], [[[128, 18, 190, 181], 0.8438125326146775, 'HorseRiding']], [[[128, 12, 189, 186], 0.8390128962093542, 'HorseRiding']], [[[129, 15, 190, 185], 0.8471056476788448, 'HorseRiding']], [[[129, 16, 191, 184], 0.8536121834731034, 'HorseRiding']], [[[129, 16, 192, 185], 0.8488154629800881, 'HorseRiding']], [[[128, 15, 194, 184], 0.8417711698421471, 'HorseRiding']], [[[129, 13, 195, 187], 0.8412510238991473, 'HorseRiding']], [[[129, 14, 191, 187], 0.8404350980083457, 'HorseRiding']], [[[129, 13, 190, 189], 0.8382891279858882, 'HorseRiding']], [[[129, 11, 187, 191], 0.8318282305903217, 'HorseRiding']], [[[128, 8, 188, 195], 0.8043430817880264, 'HorseRiding']], [[[131, 25, 193, 199], 0.826184954516826, 'HorseRiding']], [[[124, 35, 191, 203], 0.8270462809459467, 'HorseRiding']], [[[121, 38, 191, 206], 0.8350931715324705, 'HorseRiding']], [[[124, 41, 195, 212], 0.8331239341053625, 'HorseRiding']], [[[128, 42, 194, 211], 0.8343046153103657, 'HorseRiding']], [[[131, 40, 192, 203], 0.8309784496027532, 'HorseRiding']], [[[130, 32, 195, 202], 0.8316640083647542, 'HorseRiding']], [[[135, 30, 196, 197], 0.8272172409555161, 'HorseRiding']], [[[131, 16, 197, 186], 0.8388410406147955, 'HorseRiding']], [[[134, 15, 202, 184], 0.8485738297037244, 'HorseRiding']], [[[136, 15, 209, 182], 0.8529430205135213, 'HorseRiding']], [[[134, 13, 218, 182], 0.8601191479922718, 'HorseRiding']], [[[144, 10, 213, 183], 0.8591963099263467, 'HorseRiding']], [[[151, 12, 219, 184], 0.8617965108346937, 'HorseRiding']], [[[151, 10, 220, 186], 0.8631923599955371, 'HorseRiding']], [[[145, 10, 216, 186], 0.8800860885204287, 'HorseRiding']], [[[144, 10, 216, 186], 0.8858840451538228, 'HorseRiding']], [[[146, 11, 214, 190], 0.8773644144886106, 'HorseRiding']], [[[145, 24, 214, 193], 0.8605544385867248, 'HorseRiding']], [[[146, 23, 214, 193], 0.8727294882672254, 'HorseRiding']], [[[148, 22, 212, 198], 0.8713131467067079, 'HorseRiding']], [[[146, 29, 213, 197], 0.8579099324651196, 'HorseRiding']], [[[154, 29, 217, 199], 0.8547794072847914, 'HorseRiding']], [[[151, 26, 217, 203], 0.8641733722316758, 'HorseRiding']], [[[146, 24, 212, 199], 0.8613466257602624, 'HorseRiding']], [[[142, 25, 210, 194], 0.8492670944810214, 'HorseRiding']], [[[134, 24, 204, 192], 0.8428117300203049, 'HorseRiding']], [[[136, 25, 204, 189], 0.8486779356971397, 'HorseRiding']], [[[127, 21, 199, 179], 0.8513896296400709, 'HorseRiding']], [[[125, 10, 192, 192], 0.8510201771386576, 'HorseRiding']], [[[124, 8, 191, 192], 0.8493999019508465, 'HorseRiding']], [[[121, 8, 192, 193], 0.8487097098892171, 'HorseRiding']], [[[119, 6, 187, 193], 0.847543279648022, 'HorseRiding']], [[[118, 12, 190, 190], 0.8503535936620565, 'HorseRiding']], [[[122, 22, 189, 194], 0.8427901493276977, 'HorseRiding']], [[[118, 24, 188, 195], 0.8418835400352087, 'HorseRiding']], [[[120, 25, 188, 205], 0.847192725785284, 'HorseRiding']], [[[122, 25, 189, 207], 0.8444105420674433, 'HorseRiding']], [[[120, 23, 189, 208], 0.8470784016639392, 'HorseRiding']], [[[121, 23, 188, 205], 0.843428111269418, 'HorseRiding']], [[[117, 23, 186, 206], 0.8420809714166708, 'HorseRiding']], [[[119, 5, 199, 197], 0.8288265053231356, 'HorseRiding']], [[[121, 8, 192, 195], 0.8197548738023599, 'HorseRiding']]]}} ``` Parameter meanings are as follows: - `input_path`: Represents the path of the input video to be predicted. - `result`: The target bounding box information of the video frames, which is a list. Each element in the list represents the detection result of an image frame, which is still a list. Each element contains the information of a detection box, which are: - [xmin, ymin, xmax, ymax]: The coordinates of the top-left and bottom-right corners of the bounding box, both are floating-point numbers. - float: The confidence score of the bounding box, a floating-point number. - string: The category to which the bounding box belongs, a string.

The visualized video is as follows:

In the above Python script, the following steps are executed:

create_model instantiates the video detection model (YOWO), with specific explanations as follows:

Parameter	Parameter Description	Parameter Type	Options	Default Value
`model_name`	Name of the model	`str`	All model names supported by PaddleX	None
`model_dir`	Path to store the model	`str`	None	None
`device`	The device used for model inference	`str`	It supports specifying specific GPU card numbers, such as "gpu:0", other hardware card numbers, such as "npu:0", or CPU, such as "cpu".	`gpu:0`
`use_hpip`	Whether to enable high-performance inference.	`bool`	None	`False`
`nms_thresh`	The IoU threshold parameter in the Non-Maximum Suppression (NMS) process; if not specified, the default configuration of the PaddleX official model will be used	`float/None`	Floating-point number greater than 0/None	`None`
`score_thresh`	The prediction confidence threshold; if not specified, the default configuration of the PaddleX official model will be used	`float/None`	Floating-point number greater than 0/None	`None`

The predict method of the video classification model is called to perform inference and prediction. The parameter of the predict method is input, which is used to input the data to be predicted and supports multiple input types, with specific explanations as follows:

Parameter	Parameter Description	Parameter Type	Options	Default Value
`input`	Data to be predicted, supporting multiple input types	`Python Var`/`str`/`list`	Python variable, such as a `str` representing the local path of a video file File path, such as the local path of a video file: `/root/data/video.mp4` URL link, such as the network URL of a video file: Example Local directory, the directory should contain data files to be predicted, such as the local path: `/root/data/` List, elements of the list should be of the above types, such as `[\"/root/data/video1.mp4\", \"/root/data/video2.mp4\"]`, `[\"/root/data1\", \"/root/data2\"]`	None
`batch_size`	Batch size	`int`	None	1
`nms_thresh`	The IoU threshold parameter in the Non-Maximum Suppression (NMS) process	`float\|None`	float: Floating-point number greater than 0; None: If set to `None`, the default value initialized in the production environment will be used, initialized to 0.4;	`None`
`score_thresh`	Prediction confidence threshold	`float\|None`	float: Floating-point number greater than 0; None: If set to `None`, the default value initialized in the production environment will be used, initialized to 0.8;	`None`

Process the prediction results. Each sample's prediction result is a corresponding Result object, and it supports operations such as printing, saving as an image, and saving as a json file:

Method	Method Description	Parameter	Parameter Type	Parameter Description	Default Value
`print()`	Print the result to the terminal	`format_json`	`bool`	Whether to format the output content using `json` indentation	`True`
		`indent`	`int`	JSON formatting setting, only effective when `format_json` is `True`	4
		`ensure_ascii`	`bool`	JSON formatting setting, only effective when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a JSON file	`save_path`	`str`	The path to save the file. When it is a directory, the saved file name is consistent with the input file name	None
		`indent`	`int`	JSON formatting setting	4
		`ensure_ascii`	`bool`	JSON formatting setting	`False`
`save_to_video()`	Save the result as a video file	`save_path`	`str`	The path to save the file. When it is a directory, the saved file name is consistent with the input file name	None

Additionally, it supports obtaining the result visualization video and json results through attributes:

Attribute	Attribute Description
`json`	Get the prediction result in `json` format
`video`	Get the visualization video and frame rate in `dict` format. Here, the visualization video is an np.array array with dimensions (number of video frames, video height, video width, number of video channels)

For more information on using PaddleX's single-model inference APIs, please refer to the PaddleX Single-Model Python Script Usage Instructions.

IV. Custom Development

If you are seeking higher accuracy from existing models, you can use PaddleX's custom development capabilities to develop better video Detection models. Before using PaddleX to develop video Detection models, please ensure that you have installed the relevant model training plugins for video Detection in PaddleX. The installation process can be found in the custom development section of the PaddleX Local Installation Guide.

4.1 Data Preparation

Before model training, you need to prepare the dataset for the corresponding task module. PaddleX provides data validation functionality for each module, and only data that passes data validation can be used for model training. Additionally, PaddleX provides demo datasets for each module, which you can use to complete subsequent development. If you wish to use your own private dataset for subsequent model training, please refer to the PaddleX Video Detection Task Module Data Annotation Guide.

4.1.1 Demo Data Download

You can use the following command to download the demo dataset to a specified folder:

cd /path/to/paddlex
wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/video_det_examples.tar -P ./dataset
tar -xf ./dataset/video_det_examples.tar -C ./dataset/

4.1.2 Data Validation

One command is all you need to complete data validation:

python main.py -c paddlex/configs/modules/video_detection/YOWO.yaml \
    -o Global.mode=check_dataset \
    -o Global.dataset_dir=./dataset/video_det_examples

After executing the above command, PaddleX will validate the dataset and summarize its basic information. If the command runs successfully, it will print Check dataset passed ! in the log. The validation results file is saved in ./output/check_dataset_result.json, and related outputs are saved in the ./output/check_dataset directory in the current directory, including visual examples of sample images and sample distribution histograms.

👉 Validation Results Details (Click to Expand)


{
  "done_flag": true,
  "check_pass": true,
  "attributes": {
    "label_file": "..\/..\/dataset\/video_det_examples\/label_map.txt",
    "num_classes": 24,
    "train_samples": 6878,
    "train_sample_paths": [
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/SoccerJuggling\/v_SoccerJuggling_g19_c06\/00296.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/SkateBoarding\/v_SkateBoarding_g17_c04\/00026.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/RopeClimbing\/v_RopeClimbing_g01_c03\/00055.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/HorseRiding\/v_HorseRiding_g11_c05\/00132.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/PoleVault\/v_PoleVault_g13_c03\/00089.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/Basketball\/v_Basketball_g13_c04\/00050.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/PoleVault\/v_PoleVault_g01_c05\/00024.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/RopeClimbing\/v_RopeClimbing_g03_c04\/00118.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/GolfSwing\/v_GolfSwing_g01_c06\/00231.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/TrampolineJumping\/v_TrampolineJumping_g02_c02\/00134.jpg"
    ],
    "val_samples": 3916,
    "val_sample_paths": [
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/IceDancing\/v_IceDancing_g22_c02\/00017.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/TennisSwing\/v_TennisSwing_g04_c02\/00046.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/SoccerJuggling\/v_SoccerJuggling_g08_c03\/00169.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/Fencing\/v_Fencing_g24_c02\/00009.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/Diving\/v_Diving_g16_c02\/00110.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/HorseRiding\/v_HorseRiding_g08_c02\/00079.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/PoleVault\/v_PoleVault_g17_c07\/00008.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/Skiing\/v_Skiing_g20_c06\/00221.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/PoleVault\/v_PoleVault_g17_c07\/00137.jpg",
      "check_dataset\/..\/..\/dataset\/video_det_examples\/rgb-images\/GolfSwing\/v_GolfSwing_g24_c01\/00093.jpg"
    ]
  },
  "analysis": {
    "histogram": "check_dataset\/histogram.png"
  },
  "dataset_path": "video_det_examples",
  "show_type": "video",
  "dataset_type": "VideoDetDataset"
}

The above validation results, with check_pass being True, indicate that the dataset format meets the requirements. Explanations for other indicators are as follows:

attributes.num_classes: The number of classes in this dataset is 24;
attributes.train_samples: The number of training set samples in this dataset is 6878;
attributes.val_samples: The number of validation set samples in this dataset is 3916;
attributes.train_sample_paths: A list of relative paths to the visual samples in the training set of this dataset;
attributes.val_sample_paths: A list of relative paths to the visual samples in the validation set of this dataset;

Additionally, the dataset validation analyzes the sample number distribution across all classes in the dataset and generates a distribution histogram (histogram.png):

4.1.3 Dataset Format Conversion/Dataset Splitting (Optional)

After completing data validation, you can convert the dataset format or re-split the training/validation ratio of the dataset by modifying the configuration file or appending hyperparameters.

👉 Dataset Format Conversion/Dataset Splitting Details (Click to Expand)

(1) Dataset Format Conversion

Image Detection does not currently support data conversion.

(2) Dataset Splitting

Image Detection does not currently support data conversion.

### 4.2 Model Training A single command can complete the model training. Taking the training of the video Detection model YOWO as an example: ``` python main.py -c paddlex/configs/modules/video_det_examples/YOWO.yaml \ -o Global.mode=train \ -o Global.dataset_dir=./dataset/video_det_examples ``` the following steps are required: * Specify the path of the model's `.yaml` configuration file (here it is `YOWO.yaml`. When training other models, you need to specify the corresponding configuration files. The relationship between the model and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list.en.md)) * Specify the mode as model training: `-o Global.mode=train` * Specify the path of the training dataset: `-o Global.dataset_dir`. * Other related parameters can be set by modifying the fields under `Global` and `Train` in the `.yaml` configuration file, or adjusted by appending parameters in the command line. For example, to specify training on the second GPU: `-o Global.device=gpu:2`; to set the number of training epochs to 10: `-o Train.epochs_iters=10`. For more modifiable parameters and their detailed explanations, refer to the configuration file parameter instructions for the corresponding task module of the model [PaddleX Common Model Configuration File Parameters](../../instructions/config_parameters_common.en.md). * New Feature: Paddle 3.0 support CINN (Compiler Infrastructure for Neural Networks) to accelerate training speed when using GPU device. Please specify `-o Train.dy2st=True` to enable it.

👉 More Details (Click to Expand)

During model training, PaddleX automatically saves the model weight files, with the default being output. If you need to specify a save path, you can set it through the -o Global.output field in the configuration file.
PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
After completing the model training, all outputs are saved in the specified output directory (default is ./output/), typically including:
train_result.json: Training result record file, recording whether the training task was completed normally, as well as the output weight metrics, related file paths, etc.;
train.log: Training log file, recording changes in model metrics and loss during training;
config.yaml: Training configuration file, recording the hyperparameter configuration for this training session;
.pdparams, .pdema, .pdopt.pdstate, .pdiparams, .json: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;
Notice: Since Paddle 3.0.0, the format of storing static graph network structure has changed to json(the current.json file) from protobuf(the former.pdmodel file) to be compatible with PIR and more flexible and scalable.

4.3 Model Evaluation

After completing model training, you can evaluate the specified model weight file on the validation set to verify the model accuracy. Using PaddleX for model evaluation, a single command can complete the model evaluation:

python main.py -c  paddlex/configs/modules/video_detection/YOWO.yaml  \
    -o Global.mode=evaluate \
    -o Global.dataset_dir=./dataset/video_det_examples

Similar to model training, the following steps are required:

Specify the path of the model's .yaml configuration file (here it is YOWO.yaml)
Specify the mode as model evaluation: -o Global.mode=evaluate
Specify the path of the validation dataset: -o Global.dataset_dir. Other related parameters can be set by modifying the fields under Global and Evaluate in the .yaml configuration. Other related parameters can be set by modifying the fields under Global and Evaluate in the .yaml configuration file. For details, please refer to PaddleX Common Model Configuration File Parameter Description.

👉 More Details (Click to Expand)

When evaluating the model, you need to specify the model weight file path. Each configuration file has a default weight save path built-in. If you need to change it, simply set it by appending a command line parameter, such as -o Evaluate.weight_path=./output/best_model/best_model.pdparams.

After completing the model evaluation, an evaluate_result.json file will be generated, which records the evaluation results. Specifically, it records whether the evaluation task was completed successfully and the model's evaluation metrics, including mAP;

4.4 Model Inference and Model Integration

After completing model training and evaluation, you can use the trained model weights for inference predictions or Python integration.

4.4.1 Model Inference

To perform inference prediction through the command line, simply use the following command. Before running the following code, please download the demo video to your local machine.

python main.py -c paddlex/configs/modules/video_detection/YOWO.yaml \
    -o Global.mode=predict \
    -o Predict.model_dir="./output/best_model/inference" \
    -o Predict.input="HorseRiding.avi"

Similar to model training and evaluation, the following steps are required:

Specify the .yaml configuration file path for the model (here it is YOWO.yaml)
Specify the mode as model inference prediction: -o Global.mode=predict
Specify the model weight path: -o Predict.model_dir="./output/best_model/inference"
Specify the input data path: -o Predict.input="..." Other related parameters can be set by modifying the fields under Global and Predict in the .yaml configuration file. For details, please refer to PaddleX Common Model Configuration File Parameter Description.

4.4.2 Model Integration

The model can be directly integrated into the PaddleX pipelines or directly into your own project.

1.Pipeline Integration

The video Detection module can be integrated into the General Video Detection Pipeline of PaddleX. Simply replace the model path to update the video Detection module of the relevant pipeline. In pipeline integration, you can use high-performance inference and serving deployment to deploy your obtained model.

2.Module Integration

The weights you produce can be directly integrated into the video Detection module. You can refer to the Python example code in Quick Integration and simply replace the model with the path to your trained model.

video_detection.en.md 31 KB 히스토리 Raw