Video Classification Module Development Tutorial¶

I. Overview¶

The Video Classification Module is a crucial component in a computer vision system, responsible for categorizing input videos. The performance of this module directly impacts the accuracy and efficiency of the entire computer vision system. The Video Classification Module typically receives videos as input and then, through deep learning or other machine learning algorithms, classifies them into predefined categories based on their characteristics and content. For example, in an action recognition system, the Video Classification Module may need to classify input videos into categories such as "Abseiling," "Air Drumming," "Answering Questions," etc. The classification results of the Video Classification Module are output for use by other modules or systems.

II. List of Supported Models¶

Model	Model Download Link	Top1 Acc(%)	Model Storage Size (M)	Description
PP-TSM-R50_8frames_uniform	Inference Model/Trained Model	74.36	93.4 M	PP-TSM is a video classification model developed by Baidu PaddlePaddle's Vision Team. This model is optimized based on the ResNet-50 backbone network and undergoes model tuning in six aspects: data augmentation, network structure fine-tuning, training strategies, Batch Normalization (BN) layer optimization, pre-trained model selection, and model distillation. Under the center crop evaluation method, its accuracy on Kinetics-400 is improved by 3.95 points compared to the original paper's implementation.
PP-TSMv2-LCNetV2_8frames_uniform	Inference Model/Trained Model	71.71	22.5 M	PP-TSMv2 is a lightweight video classification model optimized based on the CPU-oriented model PP-LCNetV2. It undergoes model tuning in seven aspects: backbone network and pre-trained model selection, data augmentation, TSM module tuning, input frame number optimization, decoding speed optimization, DML distillation, and LTA module. Under the center crop evaluation method, it achieves an accuracy of 75.16%, with an inference speed of only 456ms on the CPU for a 10-second video input.
PP-TSMv2-LCNetV2_16frames_uniform	Inference Model/Trained Model	73.11	22.5 M

Test Environment Description:

Performance Test Environment
Test Dataset: K400 validation set.
Hardware Configuration:
- GPU: NVIDIA Tesla T4
- CPU: Intel Xeon Gold 6271C @ 2.60GHz
- Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
Inference Mode Description

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 classification 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 classification 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="PP-TSMv2-LCNetV2_8frames_uniform")
output = model.predict(input="general_video_classification_001.mp4", 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")

The result obtained after running is:

{'res': "{'input_path': 'general_video_classification_001.mp4', 'class_ids': array([0], dtype=int32), 'scores': array([0.91997], dtype=float32), 'label_names': ['abseiling']}"}

The meanings of the parameters are as follows: - input_path: Indicates the path of the input video to be predicted. - class_ids: Indicates the classification IDs of the video. - scores: Indicates the classification scores of the video. - label_names: Indicates the classification label names of the video.

The visualization video is as follows:

Visualization Image

The Python script above performs the following steps: * create_model instantiates a video classification model (here using PP-TSMv2-LCNetV2_8frames_uniform as an example), with specific explanations as follows:

Parameter	Description	Type	Options	Default Value
`model_name`	The name of the model	`str`	All model names supported by PaddleX	None
`model_dir`	The storage path of the model	`str`	None	None
`topk`	The top `topk` categories and corresponding classification probabilities of the prediction result；if not specified, the default configuration of the PaddleX official model will be used	`int`	None	`1`

The predict method of the video classification model is called for 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	Description	Type	Options	Default Value
`input`	Data to be predicted, supporting multiple input types	`Python Var`/`str`/`list`	Python Variable, such as the local path of a video file represented by `str` 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 data 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
`topk`	The `topk` predicted classes and their corresponding probabilities; if not specified, the `topk` parameter specified in create_model will be used by default. If create_model also does not specify it, the default will be the PaddleX official model configuration.	`int`	None	`1`

The prediction results are processed as dict type for each sample and support operations such as printing, saving as an image, and saving as a json file:

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 with `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 file in `json` format	`save_path`	`str`	The file path for saving. When it is a directory, the saved file name will match 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 file in video format	`save_path`	`str`	The file path for saving. When it is a directory, the saved file name will match the input file name	None

Additionally, it also supports obtaining result visualization videos and json results through attributes, as follows:

Attribute	Description
`json`	Get the prediction result in `json` format
`video`	Get the visualization video and frame rate in `dict` format

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 classification models. Before using PaddleX to develop video classification models, please ensure that you have installed the relevant model training plugins for video classification 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 Classification 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/k400_examples.tar -P ./dataset
tar -xf ./dataset/k400_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_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml \
    -o Global.mode=check_dataset \
    -o Global.dataset_dir=./dataset/k400_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\/k400_examples\/label.txt",
    "num_classes": 5,
    "train_samples": 250,
    "train_sample_paths": [
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/Wary2ON3aSo_000079_000089.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/_LHpfh0rXjk_000012_000022.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/dyoiNbn80q0_000039_000049.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/brBw6cFwock_000049_000059.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/-o4X5Z_Isyc_000085_000095.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/e24p-4W3TiU_000011_000021.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/2Grg_zwmYZE_000004_000014.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/aZY_0UqRNgA_000098_000108.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/WZlsi4nQHOo_000025_000035.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/rRh-lkFj4Tw_000001_000011.mp4"
    ],
    "val_samples": 50,
    "val_sample_paths": [
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/7Mga5kywfU4.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/w5UCdQ2NmfY.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/Qbo_tnzfjOY.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/LgW8pMDtylE.mkv",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/BY0883Dvt1c.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/PHQkMPu-KNo.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/7LSJ2Ryv1a8.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/oBYZWvlI8Uk.mp4",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/dpn2eg9O3Rs.mkv",
      "check_dataset\/..\/..\/dataset\/k400_examples\/videos\/hXtsZAaZ3yc.mkv"
    ]
  },
  "analysis": {
    "histogram": "check_dataset\/histogram.png"
  },
  "dataset_path": "k400_examples",
  "show_type": "video",
  "dataset_type": "VideoClsDataset"
}

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 5;
attributes.train_samples: The number of training set samples in this dataset is 250;
attributes.val_samples: The number of validation set samples in this dataset is 50;
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 classification does not currently support data conversion.

(2) Dataset Splitting

The parameters for dataset splitting can be set by modifying the fields under CheckDataset in the configuration file. The following are example explanations for some of the parameters in the configuration file:

CheckDataset:
split:
enable: Whether to re-split the dataset. When set to True, the dataset format will be converted. The default is False;
train_percent: If re-splitting the dataset, you need to set the percentage of the training set, which should be an integer between 0-100, ensuring that the sum with val_percent equals 100;

For example, if you want to re-split the dataset with a 90% training set and a 10% validation set, you need to modify the configuration file as follows:

......
CheckDataset:
  ......
  split:
    enable: True
    train_percent: 90
    val_percent: 10
  ......

Then execute the command:

python main.py -c paddlex/configs/modules/video_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml \
    -o Global.mode=check_dataset \
    -o Global.dataset_dir=./dataset/k400_examples

After the data splitting is executed, the original annotation files will be renamed to xxx.bak in the original path.

These parameters also support being set through appending command line arguments:

python main.py -c paddlex/configs/modules/video_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml \
    -o Global.mode=check_dataset \
    -o Global.dataset_dir=./dataset/k400_examples \
    -o CheckDataset.split.enable=True \
    -o CheckDataset.split.train_percent=90 \
    -o CheckDataset.split.val_percent=10

4.2 Model Training¶

A single command can complete the model training. Taking the training of the video classification model PP-TSMv2-LCNetV2_8frames_uniform as an example:

python main.py -c paddlex/configs/modules/video_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml  \
    -o Global.mode=train \
    -o Global.dataset_dir=./dataset/k400_examples

the following steps are required:

Specify the path of the model's .yaml configuration file (here it is PP-TSMv2-LCNetV2_8frames_uniform.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))
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 first 2 GPUs: -o Global.device=gpu:0,1; 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.

👉 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, .pdmodel: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;

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_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml  \
    -o Global.mode=evaluate \
    -o Global.dataset_dir=./dataset/k400_examples

Similar to model training, the following steps are required:

Specify the path of the model's .yaml configuration file (here it is PP-TSMv2-LCNetV2_8frames_uniform.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 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 val.top1, val.top5;

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_classification/PP-TSMv2-LCNetV2_8frames_uniform.yaml \
    -o Global.mode=predict \
    -o Predict.model_dir="./output/best_model/inference" \
    -o Predict.input="general_video_classification_001.mp4"

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

Specify the .yaml configuration file path for the model (here it is PP-TSMv2-LCNetV2_8frames_uniform.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 classification module can be integrated into the General Video Classification Pipeline of PaddleX. Simply replace the model path to update the video classification module of the relevant pipeline. In pipeline integration, you can use high-performance inference and service-oriented deployment to deploy your obtained model.

2.Module Integration

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