From 2d49d9b52d0e25a8cd962a862a85e143fd59659b Mon Sep 17 00:00:00 2001 From: nullptr Date: Fri, 27 Dec 2024 02:14:51 +0000 Subject: [PATCH] docs: fix deadlinks --- .gitignore | 2 +- docs/datasets/custom.md | 141 +++++++++++ docs/datasets/extension.md | 228 +++++++++++++++++ docs/datasets/images/labelme_pfld_1p.png | Bin 0 -> 251769 bytes docs/datasets/images/roboflow_download.png | Bin 0 -> 306321 bytes docs/datasets/images/roboflow_fomo.png | Bin 0 -> 306662 bytes .../images/roboflow_generate_version.png | Bin 0 -> 232268 bytes docs/datasets/public.md | 110 +++++++++ docs/introduction/quick_start.md | 10 +- docs/tutorials/overview.md | 2 +- docs/zh_cn/datasets/custom.md | 143 +++++++++++ docs/zh_cn/datasets/extension.md | 229 ++++++++++++++++++ .../zh_cn/datasets/images/labelme_pfld_1p.png | Bin 0 -> 251769 bytes .../datasets/images/roboflow_download.png | Bin 0 -> 306321 bytes docs/zh_cn/datasets/images/roboflow_fomo.png | Bin 0 -> 306662 bytes .../images/roboflow_generate_version.png | Bin 0 -> 232268 bytes docs/zh_cn/datasets/public.md | 114 +++++++++ docs/zh_cn/introduction/quick_start.md | 10 +- docs/zh_cn/tutorials/overview.md | 5 +- 19 files changed, 979 insertions(+), 15 deletions(-) create mode 100644 docs/datasets/custom.md create mode 100644 docs/datasets/extension.md create mode 100644 docs/datasets/images/labelme_pfld_1p.png create mode 100644 docs/datasets/images/roboflow_download.png create mode 100644 docs/datasets/images/roboflow_fomo.png create mode 100644 docs/datasets/images/roboflow_generate_version.png create mode 100644 docs/datasets/public.md create mode 100644 docs/zh_cn/datasets/custom.md create mode 100644 docs/zh_cn/datasets/extension.md create mode 100644 docs/zh_cn/datasets/images/labelme_pfld_1p.png create mode 100644 docs/zh_cn/datasets/images/roboflow_download.png create mode 100644 docs/zh_cn/datasets/images/roboflow_fomo.png create mode 100644 docs/zh_cn/datasets/images/roboflow_generate_version.png create mode 100644 docs/zh_cn/datasets/public.md diff --git a/.gitignore b/.gitignore index 89dfce60..bdf5b0c5 100644 --- a/.gitignore +++ b/.gitignore @@ -30,7 +30,7 @@ env.bak/ venv.bak/ # Datasets (and explicitly disignore sscma/datasets) -datasets/ +./datasets/ !sscma/datasets !configs/datasets diff --git a/docs/datasets/custom.md b/docs/datasets/custom.md new file mode 100644 index 00000000..5dee070b --- /dev/null +++ b/docs/datasets/custom.md @@ -0,0 +1,141 @@ +# Custom Datasets + +SSCMA supports a variety of datasets in different formats. You can create your own datasets or convert other datasets into supported formats for use in SSCMA by following certain rules. Generally, creating a dataset involves the following steps: + +```mermaid +graph TD + A[Define Objectives and Requirements] --> B[Data Collection and Cleaning] + B --> C[Feature Engineering and Data Preprocessing] + C --> D[Data Annotation and Splitting] + D --> E[Data Augmentation and Exploratory Data Analysis] + E --> F[Data Storage and Compliance] +``` + +First, define the objectives and requirements, followed by data collection and cleaning, which involves obtaining data from various sources and cleaning it to remove duplicates, handle missing values, and correct errors. + +Next, feature engineering and data preprocessing are necessary, including extracting useful features from raw data, creating new features, and performing normalization, standardization, and encoding operations on the data. Further, we proceed with data annotation and splitting, which includes not only annotating the required data but also dividing the dataset into training, validation, and testing sets (if needed). + +Finally, data augmentation and exploratory data analysis aim to improve data diversity through data augmentation and understand the distribution of data and the relationships between features through exploratory data analysis. + +After all these steps, data storage is required, ensuring that the creation and use of the entire dataset comply with legal regulations and ethical standards. These steps together ensure the quality and applicability of the dataset, laying a solid foundation for training effective machine learning models. In this section, we mainly introduce the annotation methods for datasets. + +## Annotation Basics + +For models trained by traditional supervised learning algorithms, the process of annotating datasets is crucial as it determines the quality of the trained models. Common annotation methods include: + +- **Manual Annotation:** Manually annotating the dataset for each sample ensures accuracy but is slower. +- **Semi-Automatic Annotation:** Applying manual annotation results to other datasets to reduce annotation time, but the accuracy may be reduced. +- **Automatic Annotation:** Using algorithmic models to automatically annotate data, such as keyword extraction, text classification, etc. This can improve annotation efficiency, but the accuracy may also be affected. + +### Bounding Box Annotation + +In object detection tasks, bounding boxes are a common annotation method used to mark the location of targets in images, usually represented by the coordinates of the top-left and bottom-right corners. The format for bounding box annotation is usually `(x1, y1, x2, y2)`, where `(x1, y1)` are the top-left coordinates, and `(x2, y2)` are the bottom-right coordinates. When performing bounding box annotation, note the following key points: + +- **Appropriate Box Size:** The annotation box should just enclose the target object, not too loose or too tight. Improper sizes can lead to inaccurate target boxes during inference or severe target jitter in video image detection. +- **Appropriate Box Position:** The position of the annotation box needs to be accurate and not too far off. Inaccurate positions can affect the training results of the model. +- **Multi-视角 Materials:** Rich multi-perspective materials are needed to cover various camera angles to improve the model's versatility. +- **Annotate Occluded Targets:** Even if the target object is occluded by other objects, it should be annotated. +- **Edge Rule:** Annotation boxes should be drawn tightly against the edges of the target object, neither too small nor too large. +- **Overlap Rule:** When two target objects overlap, as long as it's not more than half occlusion, it's acceptable to draw boxes, allowing for overlapping parts. +- **Independence Rule:** Each target object needs an individual box; multiple targets should not be merged into one box. +- **No-Box Rule:** Images that are unclear, too dark, overexposed, or do not meet special project rules should not be boxed. +- **Boundary Check:** Ensure that box coordinates are not on the image boundaries to prevent out-of-bounds errors when loading data or extending data. +- **Small Target Rule:** For small targets, as long as they can be distinguished by the human eye, they should be marked. Depending on the algorithm's requirements, decide whether to include these samples in training. + +### Keypoint Annotation + +In tasks related to Pose, such as pointer Meter, keypoint annotation is a common method used to mark the location of key points in images. The format for keypoint annotation is usually `(x, y)`, where `(x, y)` are the coordinates of the key point. When performing keypoint annotation, note the following details: + +- **Data Preparation:** Ensure images are clear, unobstructed, and key points are visible for subsequent annotation work. +- **Annotation Standards:** Define the types and locations of key points, clarify annotation standards, such as naming rules for key points, coordinate system selection, etc., to maintain consistency and accuracy in annotation. +- **Annotation Order and Consistency:** Key points do not need a fixed order, but the annotation order for the same part in each image should be consistent. +- **Handle Occluded Points:** Points that are obstructed should also be annotated to consider this situation during model training. +- **Visibility of Key Points:** Annotation tools may not directly indicate whether key points are visible, requiring adjustments in subsequent processing to meet the format requirements of specific algorithms. +- **Avoid Overlap:** Each key point must and can only fall into one box, not a second box. +- **Quality Check:** After initial annotation, conduct a quality check to ensure all key points are correctly annotated, with no omissions or errors. + +### Anomaly Detection Dataset Creation + +In tasks such as vibration or audio anomaly detection, datasets do not require special annotation, but we still need to manually determine parameters to ensure effective model training. + +#### N-Axis Accelerometer + +- First, determine the device's sampling frequency, usually 100Hz, then determine the time length based on the sampling frequency and data length, i.e., sample_rate. This parameter represents the number of data points in a piece of data and is decisive for the effect of anomaly detection. +- Discrete accelerometer sampling data first undergoes preprocessing to reduce signal noise at high sampling rates. The factor parameter is crucial; adjusting it affects the model's sensitivity. This parameter can be understood as discarding the inaccuracy after a certain number of decimal places. A larger value means fewer decimal places are discarded, while a smaller value means more decimal places are discarded. + - Fan anomaly detection (detection of speed changes is also required): Due to the simple waveform produced by fan movement and a small movement state space, errors should not be discarded. The preprocessing factor in the dataset creation tool should be increased; otherwise, it will be impossible to judge speed changes. + - Robot arm anomaly detection: Due to the inherent mechanical error disturbances in the movement process of the robot arm and a large number of movement state spaces, the preprocessing factor in the dataset creation tool should be decreased; otherwise, it will not recognize minor jitter interference. +- Then, adjust parameters related to cutoff_freq, resize, scales based on the data visualization interface to obtain clear and stable images. This will not be discussed further. + +#### Microphone + +- Similar to the N-axis accelerometer, first determine the device's sampling frequency, usually 16kHz, then determine the time length based on the sampling frequency and data length, i.e., sample_rate. This parameter represents the number of data points in a piece of data and is decisive for the effect of anomaly detection. +- Window length (n_fft) determines the size of the STFT window, affecting frequency resolution. A larger window provides better frequency resolution but reduces time resolution, so it needs to be determined in combination with the sampling frequency and data length. +- Overlap length (hop_length) determines the degree of overlap between windows. A smaller overlap can improve time resolution but may introduce artifacts at window boundaries. +- The n_mels parameter determines the number of Mel filter banks, which affects the vertical resolution of the spectrogram. Increasing n_mels provides more detailed frequency information but also increases computational complexity. +- By adjusting the above parameters, obtain clear and stable spectrograms for use in anomaly detection tasks. + +## Dataset Annotation Tools + +Commonly used data annotation tools include: + +- [Labelme](https://github.com/wkentaro/labelme): An open-source tool for image annotation, supporting various annotation formats such as COCO, PASCAL VOC, etc. +- [LabelImg](https://github.com/heartexlabs/labelImg): A tool for image annotation, supporting various annotation formats such as PASCAL VOC, YOLO, etc. +- [Labelbox](https://labelbox.com/): An online annotation tool, supporting annotation of images, text, video, etc., with various annotation templates and custom annotation template functions. +- [Doccano](https://github.com/doccano/doccano): An open-source annotation tool for text classification and sequence annotation, supporting various annotation formats such as NER, POS, etc. +- [Annotator](https://github.com/openannotation/annotator): A lightweight online annotation tool, supporting annotation of images, text, audio, etc. +- [VGG Image Annotator (VIA)](https://gitlab.com/vgg/via): An open-source tool for image annotation, supporting various annotation formats such as PASCAL VOC, YOLO, etc. +- [COCO Annotator](https://github.com/jsbroks/coco-annotator): A web-based image and video annotation tool for tasks such as object detection, segmentation, and keypoint annotation. + +For tasks like object detection and keypoint detection, we recommend using professional annotation tools like Roboflow and Labelme to improve annotation efficiency and quality. + +### Roboflow Annotation + +1. First, you need to [sign up for a Roboflow account](https://app.roboflow.com/login) and then log in to the Roboflow platform. + +2. After logging in to Roboflow, click on `New Project` in the upper right corner to create a new project, select the corresponding annotation mode, such as Object Detection, Keypoint Detection, etc. Then proceed to the Project page and click Upload Data to upload image files. + +3. On the Project page, click `Annotate` to enter the annotation interface, select Unannotated images, then draw rectangles or mark keypoints on the images by dragging the mouse, and enter label information in the attribute bar on the left. (Taking FOMO as an example) + + ![FOMO Annotation](images/roboflow_fomo.png) + +4. After annotation is complete, on the Project page, click `Versions`, then click `Create New Version` to generate a version. During the version generation process, you need to choose the appropriate Source Images, Train/Test Split, Preprocessing, and Augmentation parameters, and finally click Create to create the version. + + ![Roboflow Generate Version](images/roboflow_generate_version.png) + +5. After the version is created, on the corresponding version page, you can click `Download Dataset` to download the dataset. In the pop-up dialog, choose the appropriate dataset format, then select the appropriate download method to download the dataset to your local machine. + + ![Roboflow Download Dataset](images/roboflow_download.png) + +:::tip + +Before generating a version, you can first analyze the dataset on the Project page by clicking `Analyze` to view the dataset's statistical information, which can help you better understand the characteristics of the dataset and adjust annotation and training parameters accordingly for better training results. After completing the above steps, you can continue to upload more image files to expand the dataset, generate new versions, and train more complex models. + +::: + +### Labelme Annotation + +1. First, you need to install Labelme, which can be done with the following command: + +```sh +pip install labelme +``` + +2. Then, you can launch Labelme with the following command: + +```sh +labelme +``` + +3. In Labelme, choose OpenDir to open the image folder, then select the corresponding annotation mode in Edit. For object detection tasks, select Create Rectangle, and for keypoint detection tasks, select Create Point. As shown in the figure, for the PFLD single-point keypoint detection task, annotate the keypoint at the end of the image pointer. + + ![PFLD Annotation](images/labelme_pfld_1p.png) + +4. During the annotation process, you can use the mouse to drag to draw rectangles or mark keypoints, and then enter label information in the attribute bar on the right. + +5. After annotation is complete, choose Save to save the annotation results. Labelme will generate a JSON file containing the image's annotation information. + + :::tip + Labelme will automatically remember the last used annotation type during the annotation process, so you can use the shortcut key `Ctrl + N` to quickly switch annotations. Enable File -> Save Automatically to save automatically for each image. Labelme will generate a JSON file with the corresponding filename in the specified folder. We recommend turning off File -> Save With Image Data to reduce the size of the JSON files. + ::: + +6. If you have not previously divided the training set, validation set, and test set, you can manually divide the annotated results into training, validation, and test sets according to a certain ratio, and then place the JSON files and image files into the corresponding folders. diff --git a/docs/datasets/extension.md b/docs/datasets/extension.md new file mode 100644 index 00000000..5ff69130 --- /dev/null +++ b/docs/datasets/extension.md @@ -0,0 +1,228 @@ +## Dataset Formats + +SSCMA supports a variety of dataset formats, including COCO, YOLO, and more. You can choose the dataset format that best suits your needs, and below we will introduce the dataset formats we support. + +### COCO Format + +The directory structure of a COCO dataset typically looks like this: + +``` +COCO/ + annotations/ + instances_val2017.json + instances_train2017.json + instances_val2014.json + instances_train2014.json + ... + img/ + train2017/ + image1.jpg + image2.jpg + ... + val2017/ + image1.jpg + image2.jpg + ... + train2014/ + image1.jpg + image2.jpg + ... + val2014/ + image1.jpg + image2.jpg + ... +``` + +- `annotations/`: Contains all annotation files, usually in JSON format. +- `img/`: Contains the actual image files, divided into training and validation sets, as well as different year versions. + +The annotation files of the COCO dataset are in JSON format, and their structure is as follows: + +```json +{ + "info": {}, + "licenses": [], + "images": [ + { + "id": int, + "width": int, + "height": int, + "file_name": str, + "license": int, + "flickr_url": str, + "coco_url": str, + "date_captured": str, + "tags": list + } + ... + ], + "annotations": [ + { + "id": int, + "image_id": int, + "category_id": int, + "segmentation": list, + "area": float, + "bbox": [x, y, width, height], + "iscrowd": 0 or 1 + } + ... + ], + "categories": [ + { + "id": int, + "name": str, + "supercategory": str + } + ... + ] +} +``` + +- `info`: Contains information about the dataset. +- `licenses`: Contains information about usage licenses. +- `images`: Contains image information, such as ID, dimensions, filename, etc. +- `annotations`: Contains annotation information, such as object ID, image ID, category ID, segmentation information, area, bounding box, etc. +- `categories`: Contains category information, such as ID and name. + +In SSCMA, the annotation files for the FOMO dataset are JSON files in COCO format, and you can directly use the annotation files of the COCO dataset for training and testing. + +### YOLO Format + +SSCMA's pointer Meter dataset uses a structure similar to the YOLO format dataset, and its directory is as follows: + +``` +Meter/ + data_root/ + images/ # Stores image files + *.jpg # Image files + annotations/ # Stores annotation files + *.json or *.txt # Annotation files, can be in JSON or TXT format +``` + +For JSON format, the annotation structure is as follows: + +```json +{ + "imagePath": "path/to/image.jpg", + "shapes": [ + { + "points": [[x1, y1], [x2, y2], ...] // Keypoint coordinates + } + ] +} +``` + +- `imagePath`: The path to the image file. +- `shapes`: Contains a list of one or more shapes, each composed of keypoint coordinates. + +For TXT format, the annotation structure is as follows: + +``` +image1.jpg x1 y1 x2 y2 ... xn yn +image2.jpg x1 y1 x2 y2 ... xn yn +... +``` + +- Each line represents the annotation information for an image, with the first column being the image filename and the subsequent columns being the keypoint coordinates. + +### ImageNet Format + +The directory structure of the ImageNet dataset typically looks like this: + +``` +ImageNet/ + train/ + n01440764/ + image1.jpg + image2.jpg + ... + n01443537/ + image1.jpg + image2.jpg + ... + ... + val/ + n01440764/ + image1.jpg + image2.jpg + ... + n01443537/ + image1.jpg + image2.jpg + ... + ... +``` + +- `train/`: Contains training set images. +- `val/`: Contains validation set images. +- `test/`: Contains test set images. + +Images for each category are placed in a subdirectory named after the category ID. + +The annotations for the ImageNet dataset usually exist in the form of plain text files, one file per category. The structure of the annotation file is as follows: + +``` +n01440764/image1.jpg 0 +n01440764/image1.jpg 1 +... +``` + +- Each line represents an annotation for an image, with the first column being the image file path and the second column being the category ID. + +### Anomaly Format + +The directory structure of the Anomaly dataset is as follows: + +``` +Anomaly/ + data_root/ + anomaly/ # Stores abnormal audio files + *.npy # Abnormal audio data files + normal/ # Stores normal audio files + *.npy # Normal audio data files +``` + +- `data_root/`: The root directory of the dataset. +- `anomaly/`: Contains all audio files marked as abnormal. +- `normal/`: Contains all audio files marked as normal. +- `*.npy`: Audio data files stored in NumPy array format. + +By default, we can use only the normal audio files for training without annotating the dataset. + +Regarding dataset types, there are the following two situations: + +For Signal datasets, each `.npy` file contains a set of N-axis gyroscope and accelerometer data with X entries, shaped as `(N, W, H)`, where N corresponds to each sensor axis, and each Channel is composed of a two-dimensional array of W and H, representing X sensor data entries. W and H are determined by wavelet transformation and Markov chains, and each data value is an integer between 0-255. + +For Microphone datasets, each `.npy` file contains a set of N audio data entries, shaped as `(N, W, H)`, where N is the number of audio data Channels, which is 1 for a single Microphone, and W and H are determined by the Mel spectrogram transformation, with each data value being an integer between 0-255. + +## Dataset Expansion + +You can use expanded data loading components according to your needs. + +### LanceDB + +LanceDB is a data storage and processing library based on PyArrow, providing efficient columnar storage and processing capabilities suitable for the storage and access of large-scale datasets. In this tutorial, we will introduce how to use LanceDB to expand the dataset, enabling SSCMA to store and access data more efficiently. + +#### Data Storage Structure and Method + +According to the provided code, data in the database should be stored with the following structure and method: + +1. **Table Structure**: Data is stored in tables, each consisting of multiple columns, with each column corresponding to a field (e.g., `image`, `filename`, `bbox`, `catid`, `label`). + +2. **Field Types**: + - `image`: Binary data (`pa.binary()`), storing the byte data of the image. + - `filename`: String type (`pa.string()`), storing the name of the image file. + - `bbox`: Binary data (`pa.binary()`), storing the byte data of the bounding box information. + - `catid`: Binary data (`pa.binary()`), storing the byte data of the category ID information. + - `label`: Integer type (`pa.int8()`), storing the label or category of the image. + +3. **Data Encoding**: Bounding box (`bbox`) and category ID (`catid`) data are encoded as byte data and stored in binary columns. + +4. **Data Batch Processing**: Data is generated in batches as PyArrow `RecordBatch` objects through the `process_images_detect` and `process_images_cls` functions, then written into LanceDB. + +5. **Data Reading**: Data is read from LanceDB using the `LanceDataset` class, where the `load_data_list` method reads all data from the database, and the `get_data_info` method obtains detailed information for individual data items. + +6. **Data Decoding**: In the `get_data_info` method, `bbox` and `catid` fields stored as byte data are decoded back into NumPy arrays for further processing. + +Through this method, the dataset's storage structure in LanceDB is clear and efficient for reading and processing. diff --git a/docs/datasets/images/labelme_pfld_1p.png b/docs/datasets/images/labelme_pfld_1p.png new file mode 100644 index 0000000000000000000000000000000000000000..91f0aa2418ba5d57dce88e24da9401162be9955c GIT binary 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public datasets, including the publicly available COCO (Common Objects in Context) dataset, ImageNet dataset, and datasets such as FOMO and Meter created by Seeed, among others. + +## Obtaining Public Datasets + +You can also download datasets from other platforms such as Roboflow, Kaggle, etc., as long as these datasets conform to the formats supported by SSCMA. + +### SSCMA + +[SSCMA](https://github.com/Seeed-Studio/ModelAssistant) currently offers the following official datasets for training and testing corresponding models. + +For datasets downloaded using commands, please ensure you are in the **project root directory** before running the commands. The commands will automatically download the datasets and save them in a folder named `datasets` in the current directory, and complete the unzipping process. + +- [Download Meter Dataset](https://files.seeedstudio.com/sscma/datasets/meter.zip): + + ```sh + wget https://files.seeedstudio.com/sscma/datasets/meter.zip -P datasets -O datasets/meter.zip && \ + mkdir -p datasets/meter && \ + unzip datasets/meter.zip -d datasets + ``` + +- [Download Mask COCO Dataset](https://files.seeedstudio.com/sscma/datasets/coco_mask.zip): + + ```sh + wget https://files.seeedstudio.com/sscma/datasets/coco_mask.zip -P datasets -O datasets/coco_mask.zip && \ + mkdir -p datasets/coco_mask && \ + unzip datasets/coco_mask.zip -d datasets/coco_mask + ``` + +### Roboflow + +[Roboflow](https://public.roboflow.com/) is a free hosting platform for public computer vision datasets, supporting formats including CreateML JSON, COCO JSON, Pascal VOC XML, YOLO, and Tensorflow TFRecords, among others, with additional缩小 and augmentation versions for corresponding datasets. + +:::tip + +We highly recommend looking for datasets here. You only need to register an account to download hundreds of datasets from the internet for free, to meet your specific needs. + +::: + +You can find some datasets available for SSCMA on Roboflow, as shown below: + +| Dataset | Description | +| -- | -- | +| [Digital Meter Water](https://universe.roboflow.com/seeed-studio-dbk14/digital-meter-water/dataset/1) | Digital Meter Water Dataset | +| [Digital Meter Seg7](https://universe.roboflow.com/seeed-studio-dbk14/digital-meter-seg7/dataset/1) | Digital Meter Seg7 Dataset | +| [Digit Seg7 Classification](https://universe.roboflow.com/seeed-studio-ovcjn/digit-seg7/1) | Digit Seg7 Classification Dataset | + +### Kaggle + +[Kaggle](https://www.kaggle.com/) is a data modeling and data analysis competition platform. Companies and researchers can publish data on it, and statisticians and data mining experts can compete to produce the best models. Kaggle also offers thousands of datasets, and you can visit [Kaggle Datasets](https://www.kaggle.com/datasets) to select datasets that meet your needs. + +### Other Dataset Platforms + +You can refer to the [Datasets List](https://www.datasetlist.com/) website, which provides a large number of public datasets. You can choose the appropriate datasets according to your needs. It is important to note that these datasets may need to be converted before they can be used in SSCMA. + +## Common Public Datasets + +We list some common public datasets here, and you can choose the appropriate datasets according to your needs. + +### COCO Dataset + +[COCO Dataset](https://cocodataset.org/) is a large-scale object detection, segmentation, and captioning dataset, containing 330K images, 1.5 million object instances, 80 object categories, 91 material categories, 5 captions per image, and 250,000 images with keypoints. + +#### Application Scenarios + +- **Computer Vision Research**: Used for developing and testing image recognition, object detection, image segmentation, and caption generation algorithms. +- **Autonomous Driving**: Used for training vehicles to recognize and locate pedestrians, vehicles, and other objects on the road. +- **Robot Vision**: Used for training robots to recognize and manipulate objects in the environment. +- **Security Surveillance**: Used for detecting abnormal behaviors or specific objects in surveillance videos. +- **Medical Image Analysis**: Used for auxiliary diagnosis, identifying lesion areas through image segmentation techniques. +- **Augmented Reality (AR)**: Used for overlaying virtual information on the real world, requiring precise object detection and image segmentation techniques. +- **E-commerce**: Used for automatic classification and retrieval of product images. + +### ImageNet Dataset + +[ImageNet Dataset](http://www.image-net.org/) is a large visual object recognition database containing over 14 million annotated images across more than 20,000 categories. + +#### Application Scenarios + +- **Computer Vision Research**: Used for developing and testing image recognition and object localization algorithms. +- **Deep Learning Model Training**: Used for training large-scale deep learning models, such as Convolutional Neural Networks (CNNs). +- **Image Search Engines**: Used to improve the accuracy and relevance of image searches. +- **Security Surveillance**: Used for identifying and locating specific objects in surveillance videos. +- **Autonomous Driving**: Used for training vehicles to recognize pedestrians, vehicles, etc., on the road. +- **Intelligent Monitoring**: Used for recognizing and classifying images captured by surveillance cameras. +- **Bioinformatics**: Used for analyzing and classifying biological images, such as cells and tissues. +- **Content Filtering and Copyright Protection**: Used for automatically recognizing and filtering inappropriate content or protecting copyrighted images. + +### FOMO Dataset + +[FOMO Dataset](https://files.seeedstudio.com/sscma/datasets/coco_mask.zip) is a dataset containing faces and masks created by Seeed, used for training and testing mask detection models, with the same directory structure and annotation format as the COCO dataset. + +#### Application Scenarios + +- **Epidemic Prevention and Control**: Used to detect whether people are wearing masks to ensure safety in public places. +- **Security Surveillance**: Used to monitor the wearing situation of people in surveillance videos and detect abnormalities in a timely manner. +- **Intelligent Access Control**: Used to recognize whether people are wearing masks to control the opening and closing of access control systems. +- **Intelligent Patrol**: Used to detect whether workers are wearing masks to ensure safety in the workplace. +- **Intelligent Security Check**: Used to detect whether passengers are wearing masks to ensure the safety of transportation tools. + +### Meter Dataset + +[Meter Dataset](https://files.seeedstudio.com/sscma/datasets/meter.zip) is a dataset containing analog and digital meter data created by Seeed, used for training and testing meter recognition models, with the same directory structure and annotation format as the YOLO TXT dataset. + +#### Application Scenarios + +- **Intelligent Meter Reading**: Used for automatically recognizing and reading pointer readings on meters. +- **Intelligent Metering**: Used for automatically metering pointer readings on meters to improve the accuracy of metering. +- **Intelligent Monitoring**: Used for monitoring the readings of meters to detect abnormalities in a timely manner. diff --git a/docs/introduction/quick_start.md b/docs/introduction/quick_start.md index 0acff04d..9fae532f 100644 --- a/docs/introduction/quick_start.md +++ b/docs/introduction/quick_start.md @@ -10,7 +10,7 @@ We suggest that all beginners of [SSCMA](https://github.com/Seeed-Studio/ModelAs ### Model Deployment -If you want to deploy the model on the device, please refer to the section [Deploy](../deploy/overview) to learn how to deploy the model. +If you want to deploy the model on the device, please refer to the section [Deploy](../tutorials/deploy/overview) to learn how to deploy the model. ### Model Training @@ -48,15 +48,15 @@ If you want to train a model, we highly recommend that you first try to train a - **Model Training**, please refer to [Model Training](../tutorials/training/overview) to learn how to use [SSCMA](https://github.com/Seeed-Studio/ModelAssistant) to train a model. We suggest that you select a model from an example for training. - - **Model Export**. After completing model training, in order to deploy on the edge-computing device, it is necessary to first export the model. For the export tutorial of the model, please refer to [Model Export](../tutorials/export/overview). + - **Model Export**. After completing model training, in order to deploy on the edge-computing device, it is necessary to first export the model. - **Model Verification**. Model verification can be performed after training or export. The former verifies the correctness of the neural network and training results, while the latter mainly verifies the correctness of the exported model, facilitating deployment and debugging on edge computing devices later. Some methods for model validation have been provided in the documents in the above two steps. -- **Model Deployment**. If you want to deploy the exported training model on edge computing devices, please refer to [ESP32 Deployment Example](../deploy/esp32/deploy) or [Grove Vision AI Deployment Example](../deploy/grove/deploy). +- **Model Deployment**. If you want to deploy the exported training model on edge computing devices, please refer to [ESP32 Deployment Example](../tutorials/deploy/xiao_esp32s3) or [Grove Vision AI Deployment Example](../tutorials/deploy/grove_vision_ai_v2). -- **Custom Datasets**. If you want to train on a custom dataset, please refer to [Datasets](../tutorials/datasets). +- **Custom Datasets**. If you want to train on a custom dataset, please refer to [Datasets](../datasets/custom). -- **Custom Model**. If you want to modify an existing neural network or design your own neural network, please refer to [Model Configuration](../tutorials/config). +- **Custom Model**. If you want to modify an existing neural network or design your own neural network, please refer to [Model Configuration](../custom/basics). ## Necessary Knowledge diff --git a/docs/tutorials/overview.md b/docs/tutorials/overview.md index 84b2d442..ac92f53f 100644 --- a/docs/tutorials/overview.md +++ b/docs/tutorials/overview.md @@ -77,7 +77,7 @@ The learning rate is the step size of the optimizer when updating model paramete :::tip -For more information on training details, please refer to [SSCMA - Customization - Training and Validation Pipeline](../custom/pipeline) and [SSCMA - Customization - Optimizers](../custom/optimizer). +For more information on training details, please refer to [SSCMA - Customization - Training and Validation Pipeline](../custom/pipelines) and [SSCMA - Customization - Optimizers](../custom/optimizer). ::: diff --git a/docs/zh_cn/datasets/custom.md b/docs/zh_cn/datasets/custom.md new file mode 100644 index 00000000..b6bc8a89 --- /dev/null +++ b/docs/zh_cn/datasets/custom.md @@ -0,0 +1,143 @@ +# 自制数据集 + +SSCMA 支持多种不同格式的数据集,您可以按照一定规则,自己制作或将其它数据集转换为支持的格式,以便在 SSCMA 中使用,一般来说,制作一个数据集通常包含以下几个部分: + +```mermaid +graph TD + A[确定目标和需求] --> B[数据收集与清洗] + B --> C[特征工程与数据预处理] + C --> D[数据标注与分割] + D --> E[数据增强与探索性数据分析] + E --> F[数据存储与合规性] +``` + +首先是确定目标和需求,然后进行数据收集与清洗,这一步骤涉及从不同来源获取数据,并对其进行清洗,以去除重复项、处理缺失值和纠正错误。 + +随后,需要通过特征工程与数据预处理,包括从原始数据中提取有用特征、创建新特征以及对数据进行归一化、标准化和编码等预处理操作。进一步,我们进行数据标注与分割,这一步不仅包括对需要的数据进行标注,还包括将数据集划分为训练集、验证集和测试集(如果需要)。 + +最后是数据增强与探索性数据分析,这一阶段旨在通过数据增强提高数据多样性,并通过探索性数据分析来理解数据的分布和特征间的关系。 + +在所有这些步骤完成后,还需要进行数据存储,并确保整个数据集的创建和使用都符合法律法规和伦理标准。这些步骤共同确保了数据集的质量和适用性,为训练有效的机器学习模型打下了坚实的基础。在本节中,我们主要介绍数据集的标注方式。 + +## 标注基础 + +对于传统的由监督学习算法训练的模型来说,标注数据集的过程是非常关键的,它决定了训练出的模型的质量,常见的标注方法有以下几种: + +- **手动标注:** 通过手工对数据集进行标注的方式,对每个样本进行标注,可以确保标注的准确性,但是速度较慢。 +- **半自动标注:** 将人工标注的结果应用到其他数据集中,减少标注时间,但标注的准确性可能有所降低。 +- **自动标注:** 使用一些算法模型对数据进行自动标注,例如关键字提取、文本分类等。虽然可以提高标注效率,但标注的准确性也可能会受到影响。 + +### 边界框标注 + +在目标检测任务中,边界框是一种常见的标注方式,用于标记图像中的目标位置,通常由左上角和右下角的坐标表示。边界框标注的格式通常为 `(x1, y1, x2, y2)`,其中 `(x1, y1)` 是左上角坐标,`(x2, y2)` 是右下角坐标。在进行边界框标注时,需要注意以下几个关键点: + +- **标注框大小合适**:标注框应该刚好将目标物体包围,间隙不能太大也不能太小。太大或太小都可能导致模型在推理时目标框得不准,或者在视频图像检测中目标抖动严重,不能锁定。 +- **标注框位置合适**:标注框的位置需要准确,不能偏太远。位置不准确会影响模型的训练效果。 +- **多视角素材**:需要丰富多视角的素材,涵盖各种摄像机视角,以提高模型的通用性。 +- **遮挡目标也要标注**:即使目标物体被其他物体遮挡,也需要进行标注。 +- **贴边规则**:标注框需紧贴目标物体的边缘进行画框标注,不可框小或框大。 +- **重叠规则**:当两个目标物体有重叠时,只要不是遮挡超过一半的就可以框,允许两个框有重叠的部分。 +- **独立规则**:每一个目标物体都需要单独拉框,不能将多个目标合并在一个框内。 +- **不框规则**:图像模糊不清、太暗、曝光过度或不符合项目特殊规则的不框。 +- **边界检查**:确保框坐标不在图像边界上,防止载入数据或数据扩展时出现越界报错。 +- **小目标规则**:对于小目标,只要人眼能分清,都应该标出来。根据算法的需求,决定是否启用这些样本参与训练。 + +### 关键点标注 + +在指针 Meter 等与 Pose 相关的任务中,关键点标注是一种常见的标注方式,用于标记图像中的关键点位置。关键点标注的格式通常为 `(x, y)`,其中 `(x, y)` 是关键点的坐标。在进行关键点标注时,需要注意以下几个细节: + +- **数据准备**:确保图像清晰无遮挡,关键点可见,以便于后续的标注工作。 +- **标注规范制定**:定义关键点的类型和位置,明确标注标准,如关键点的命名规则、坐标系选择等,以保持标注的一致性和准确性。 +- **标注顺序和一致性**:关键点可以不用固定的顺序,但每张图像中相同部位的关键点标注顺序应保持一致。 +- **处理遮挡点**:被遮挡的点也应标注出来,以便在模型训练时能够考虑到这种情况。 +- **关键点的可见性**:标注工具可能无法直接标注关键点是否可见,需要在后续处理中调整,以符合特定算法的格式要求。 +- **避免重叠**:每一个关键点必须且只能落入一个框中,不落入第二个框中。 +- **质量检查**:完成初步标注后,进行质量检查,确保所有关键点都被正确标注,且无遗漏或错误。 + +### 异常检测数据制作 + +在振动或音频异常检测任务中,数据集不需要进行特殊标注,但仍然需要我们手动确定参数,以保证能够训练出有效的模型。 + +#### N 轴加速度计 + +- 首先需要确定设备的采样频率,通常为 100Hz,然后根据采样频率和数据长度确定时间长度,即 sample_rate,该参数代表一条数据中包含多少数据点,注意该参数对异常检测的效果是决定性的。 +- 离散的加速度计采样数据首先会经过前处理,用于减少高采样率下的信号毛刺,其中 factor 参数比较关键,调整它的 factor 参数对模型敏感性起到重要作用,该参数可以理解为抛去小数点后几位的不精确误差,数值越大则抛掉的小数点越少,越小则抛掉的小数点越多。 + - 风扇异常检测(对挡位的变动也需要检测):由于风扇运动规律,产生的波形较为简单,运动状态空间小,因此不应该舍去误差,数据集制作工具的前处理 factor 需要调大,否则会出现挡位变化无法判断的现象。 + - 机械臂异常检测:由于机械臂的运动过程自带一定机误差扰动,运动的状态空间过多,数据集制作工具的前处理中 factor 应当调小,否则会出现对轻微的抖动干扰不识别的现象。 +- 而后需要根据数据的可视化界面,依次调整 cutoff_freq、resize、scales 相关的参数,得到清晰且稳定的图像,在此不展开讨论。 + +#### 麦克风 + +- 和 N 轴加速度计类似,首先需要确定设备的采样频率,通常为 16kHz,然后根据采样频率和数据长度确定时间长度,即 sample_rate,该参数代表一条数据中包含多少数据点,注意该参数对异常检测的效果是决定性的。 +- 窗口长度(n_fft)决定了 STFT 的窗口大小,影响频率分辨率。较大的窗口可以提供更好的频率分辨率,但会降低时间分辨率,因此需要结合采样频率和数据长度来确定窗口长度。 +- 重叠长度(hop_length)决定了窗口之间的重叠程度。较小的重叠可以提高时间分辨率,但可能会在窗口边界处引入伪影。 +- n_mels 参数决定了梅尔滤波器组的数量,这将影响频谱图的垂直分辨率。增加 n_mels 可以提供更细致的频率信息,但也会增加计算复杂度。 +- 通过调整以上参数,得到清晰且稳定的频谱图,以用于异常检测任务。 + + +## 数据集标注工具 + +常用的数据标注工具包括: + +- [Labelme](https://github.com/wkentaro/labelme): 一个用于图像标注的开源工具,支持多种标注格式,如 COCO、PASCAL VOC 等。 +- [LabelImg](https://github.com/heartexlabs/labelImg): 适用于图像标注的工具,支持多种标注格式,如 PASCAL VOC、YOLO 等。 +- [Labelbox](https://labelbox.com/): 一个在线标注工具,支持图像、文本、视频等格式的标注,具有多种标注模板和自定义标注模板功能。 +- [Doccano](https://github.com/doccano/doccano): 一款用于文本分类和序列标注的开源标注工具,支持多种标注格式,如 NER、POS 等。 +- [Annotator](https://github.com/openannotation/annotator): 一个轻量级的在线标注工具,支持图像、文本、音频等格式的标注。 +- [VGG Image Annotator (VIA)](https://gitlab.com/vgg/via): 一个用于图像标注的开源工具,支持多种标注格式,如 PASCAL VOC、YOLO 等。 +- [COCO Annotator](https://github.com/jsbroks/coco-annotator): 一个基于 Web 的图像和视频注释工具,可用于目标检测、分割、关键点标注等任务。 + +在目标检测、关键点检测等任务中,我们建议使用 Roboflow、Labelme 等专业的标注工具,以提高标注效率和标注质量。 + +### Roboflow 标注 + +1. 首先,您需要[注册 Roboflow 账号](https://app.roboflow.com/login),然后登录 Roboflow 平台。 + +2. 登陆到 Roboflow 后,点击右上角的 `New Project` 创建一个新项目,选择对应的标注模式,如 Object Detection、Keypoint Detection 等。然后进入 Project 页面,点击 Upload Data 上传图像文件。 + +3. 在 Project 页面中,点击 `Annotate`,进入标注界面,选择 Unannotated 图像,然后在图像上拖动鼠标绘制矩形框或标记关键点,然后在左侧的属性栏中输入标签信息。(以 FOMO 为例) + + ![FOMO 标注](images/roboflow_fomo.png) + +4. 标注完成后,在 Project 页面中,点击 `Versions`,然后点击 `Create New Version` 生成版本,在生成版本的过程中,需要选择合适的 Source Images,Train/Test Split,Preprocessing 和 Augmentation 等参数,最后点击 Create 创建版本。 + + ![Roboflow 生成版本](images/roboflow_generate_version.png) + +5. 完成版本创建后,在对应的版本页面中,您可以点击 `Download Dataset` 下载数据集,在弹出的对话框中选择合适的数据集格式,然后选择恰当的下载方式,将数据集下载到本地。 + + ![Roboflow 下载数据集](images/roboflow_download.png) + +:::tip + +在生成版本前,您可以先在 Project 页面中,点击 `Analyze` 分析数据集,查看数据集的统计信息,以便更好地了解数据集的特点,并针对性地调整标注和训练参数,以获得更好的训练效果。在完成以上步骤后,您也可以继续上传更多的图像文件,扩充数据集,生成新的版本,用于训练更加复杂的模型。 + +::: + +### Labelme 标注 + +1. 首先,您需要安装 Labelme,可以通过以下命令进行安装: + +```sh +pip install labelme +``` + +2. 然后,您可以通过以下命令启动 Labelme: + +```sh +labelme +``` + +3. 在 Labelme 中,选择 OpenDir 打开图像文件夹,然后在 Edit 选择对应的标注模式,对于目标检测任务,选择 Create Rectangle,对于关键点检测任务,选择 Create Point。 如图所示,对于 PFLD 单点关键点检测任务,在图像指针的末端标注关键点。 + + ![PFLD 标注](images/labelme_pfld_1p.png) + +4. 在标注过程中,您可以使用鼠标拖动来绘制矩形框或标记关键点,然后在右侧的属性栏中输入标签信息。 + +5. 标注完成后,选择 Save 保存标注结果,Labelme 会生成一个 JSON 文件,其中包含了图像的标注信息。 + + :::tip + 在标注过程中 Labelme 会自动记住上次使用的标注类型,因此可以使用快捷键 `Ctrl + N` 快速信件标注。开启 File -> Save Automatically 自动保存,对于每张图片,Labelme 会在设定的文件夹下生成对应文件名的 JSON 文件。我们建议关闭 File -> Save With Image Data,以减少 JSON 文件的大小。 + ::: + +6. 如果事先未划分训练集、验证集和测试集,您可以手动将标注结果按照一定比例划分为训练集、验证集和测试集,然后将 JSON 文件和图像文件分别放入对应的文件夹中。 + diff --git a/docs/zh_cn/datasets/extension.md b/docs/zh_cn/datasets/extension.md new file mode 100644 index 00000000..6537e0e0 --- /dev/null +++ b/docs/zh_cn/datasets/extension.md @@ -0,0 +1,229 @@ +## 数据集格式 + +SSCMA 支持多种数据集格式,包括 COCO、YOLO 等,您可以根据自己的需求选择合适的数据集格式,下面我们将介绍我们支持的数据集格式。 + +### COCO 格式 + +COCO 数据集的目录结构通常如下: + +``` +COCO/ + annotations/ + instances_val2017.json + instances_train2017.json + instances_val2014.json + instances_train2014.json + ... + img/ + train2017/ + image1.jpg + image2.jpg + ... + val2017/ + image1.jpg + image2.jpg + ... + train2014/ + image1.jpg + image2.jpg + ... + val2014/ + image1.jpg + image2.jpg + ... +``` + +- `annotations/`:包含所有标注文件,通常是 JSON 格式。 +- `img/`:包含实际的图像文件,分为训练集和验证集,以及不同的年份版本。 + +COCO 数据集的标注文件是 JSON 格式的,其结构如下: + +```json +{ + "info": {}, + "licenses": [], + "images": [ + { + "id": int, + "width": int, + "height": int, + "file_name": str, + "license": int, + "flickr_url": str, + "coco_url": str, + "date_captured": str, + "tags": list + } + ... + ], + "annotations": [ + { + "id": int, + "image_id": int, + "category_id": int, + "segmentation": list, + "area": float, + "bbox": [x, y, width, height], + "iscrowd": 0 or 1 + } + ... + ], + "categories": [ + { + "id": int, + "name": str, + "supercategory": str + } + ... + ] +} +``` + +- `info`:包含数据集的信息。 +- `licenses`:包含使用许可的信息。 +- `images`:包含图像的信息,如 ID、尺寸、文件名等。 +- `annotations`:包含标注信息,如对象的 ID、图像 ID、类别 ID、分割信息、面积、边界框等。 +- `categories`:包含类别的信息,如 ID 和名称。 + +在 SSCMA 中,FOMO 数据集的标注文件是 COCO 格式的 JSON 文件,您可以直接使用 COCO 数据集的标注文件进行训练和测试。 + +### YOLO 格式 + +SSCMA 的指针 Meter 数据集使用了类似 YOLO 格式数据集的结构,其目录如下: + +``` +Meter/ + data_root/ + images/ # 存放图像文件 + *.jpg # 图像文件 + annotations/ # 存放标注文件 + *.json or *.txt # 标注文件,可以是 JSON 或 TXT 格式 +``` + +对于,JSON 格式,其标注结构如下: + +```json +{ + "imagePath": "path/to/image.jpg", + "shapes": [ + { + "points": [[x1, y1], [x2, y2], ...] // 关键点坐标 + } + ] +} +``` + +- `imagePath`:图像文件的路径。 +- `shapes`:包含一个或多个形状的列表,每个形状由关键点坐标组成。 + +对于 TXT 格式,其标注结构如下: + +``` +image1.jpg x1 y1 x2 y2 ... xn yn +image2.jpg x1 y1 x2 y2 ... xn yn +... +``` + +- 每行代表一个图像的标注信息,第一列为图像文件名,后续列为关键点坐标。 + +### ImageNet 格式 + +ImageNet 数据集的目录结构通常如下: + +``` +ImageNet/ + train/ + n01440764/ + image1.jpg + image2.jpg + ... + n01443537/ + image1.jpg + image2.jpg + ... + ... + val/ + n01440764/ + image1.jpg + image2.jpg + ... + n01443537/ + image1.jpg + image2.jpg + ... + ... +``` + +- `train/`:包含训练集图像。 +- `val/`:包含验证集图像。 +- `test/`:包含测试集图像。 + +每个类别的图像都被放在以类别 ID 命名的子目录中。 + +ImageNet 数据集的标注通常以纯文本文件的形式存在,每个类别一个文件。标注文件的结构如下: + +``` +n01440764/image1.jpg 0 +n01440764/image1.jpg 1 +... +``` + +- 每一行代表一个图像中的一个标注,第一列为图像文件路径,第二列为类别 ID。 + +### Anomaly 格式 + +Anomaly 数据集的目录结构如下: + +``` +Anomaly/ + data_root/ + anomaly/ # 存放异常音频文件 + *.npy # 异常音频数据文件 + normal/ # 存放正常音频文件 + *.npy # 正常音频数据文件 +``` + +- `data_root/`:数据集的根目录。 +- `anomaly/`:包含所有标记为异常的音频文件。 +- `normal/`:包含所有标记为正常的音频文件。 +- `*.npy`:音频数据文件,以NumPy数组格式存储。 + +默认情况下,我们可以仅使用正常的音频文件进行训练,无需对数据集进行标注。 + +关于数据集类型,分为以下两种情况: + +对于 Signal 数据集,每个 `.npy` 文件都包含了一组 N 轴陀螺仪和加速度计的 X 条数据,其形状为 `(N, W, H)`, 其中 N 是对应传感器每条轴,每个 Channel 下,由 W,H 组成一个二维数组,表示 X 条传感器数据,W 和 H 由小波变换和马尔科夫链确定,每个数据的值为 0-255 的整数。 + +对于 Microphone 数据集,每个 `.npy` 文件都包含了一组 N 个音频数据,其形状为 `(N, W, H)`,其中 N 是音频数据的 Channel 数,单个 Microphone 情况下为 1,W 和 H 由梅尔频谱变换确定,每个数据的值为 0-255 的整数。 + +## 数据集扩展 + +您可以根据自己的需求使用拓展的数据加载器件。 + +### LanceDB + +LanceDB 是一个基于 PyArrow 的数据存储和处理库,它提供了高效的列式存储和处理能力,适用于大规模数据集的存储和访问。在本教程中,我们将介绍如何使用 LanceDB 扩展数据集,使 SSCMA 能够更高效地存储和访问数据。 + + +#### 数据存储结构和方式 + +根据提供的代码,数据库中的数据应该以以下结构和方式存储: + +1. **表结构**:数据被存储在表中,每个表由多个列组成,每列对应一个字段(如 `image`, `filename`, `bbox`, `catid`, `label`)。 + +2. **字段类型**: + - `image`:二进制数据(`pa.binary()`),存储图像的字节数据。 + - `filename`:字符串类型(`pa.string()`),存储图像文件的名称。 + - `bbox`:二进制数据(`pa.binary()`),存储边界框信息的字节数据。 + - `catid`:二进制数据(`pa.binary()`),存储类别ID信息的字节数据。 + - `label`:整型(`pa.int8()`),存储图像的标签或类别。 + +3. **数据编码**:边界框(`bbox`)和类别 ID(`catid`)数据被编码为字节数据并存储在二进制列中。 + +4. **数据批处理**:数据通过 `process_images_detect` 和 `process_images_cls` 函数以批处理的方式生成 PyArrow 的 `RecordBatch` 对象,然后写入 LanceDB。 + +5. **数据读取**:使用 `LanceDataset` 类从 LanceDB 读取数据,其中 `load_data_list` 方法从数据库中读取所有数据,`get_data_info` 方法获取单个数据项的详细信息。 + +6. **数据解码**:在 `get_data_info` 方法中,将存储为字节数据的 `bbox` 和 `catid` 字段解码回 NumPy 数组,以便进一步处理。 + +通过这种方式,数据集在 LanceDB 中的存储结构清晰,且便于高效读取和处理。 \ No newline at end of file diff --git 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支持多种公共数据集,包括互联网上公开的 COCO (Common Objects in Context) 数据集、ImageNet 数据集、由 Seeed 制作的 FOMO、Meter 等数据集等。 + + +## 获取公共数据集 + +您也可以从其它平台如 Roboflow、Kaggle 等下载数据集,只要这些数据集符合 SSCMA 支持的数据集格式即可。 + +### SSCMA + +[SSCMA](https://github.com/Seeed-Studio/ModelAssistant) 目前提供以下官方数据集,用于对应模型的训练和测试。 + +对于使用命令下载的数据集,请确保在运行命令前处于**项目根目录**,命令会自动下载数据集并将其保存在当前目录下的名为 `datasets` 的文件夹中,并完成解压。 + +- [下载指针 Meter 数据集](https://files.seeedstudio.com/sscma/datasets/meter.zip): + + ```sh + wget https://files.seeedstudio.com/sscma/datasets/meter.zip -P datasets -O datasets/meter.zip && \ + mkdir -p datasets/meter && \ + unzip datasets/meter.zip -d datasets + ``` + +- [下载口罩 COCO 数据集](https://files.seeedstudio.com/sscma/datasets/coco_mask.zip): + + ```sh + wget https://files.seeedstudio.com/sscma/datasets/coco_mask.zip -P datasets -O datasets/coco_mask.zip && \ + mkdir -p datasets/coco_mask && \ + unzip datasets/coco_mask.zip -d datasets/coco_mask + ``` + +### Roboflow + +[Roboflow](https://public.roboflow.com/) 是公共计算机视觉数据集的免费托管平台,支持的格式包括包括 CreateML JSON、COCO JSON、Pascal VOC XML、YOLO 和 Tensorflow TFRecords 等,还额外添加了对应数据集的缩小和增强版本。 + +:::tip + +我们十分推荐您在这里寻找数据集,您只需要注册一个账号,就可以免费下载数百个来自互联网的不同数据集,用于满足您的特定需求。 + +::: + +你可以在 Roboflow 上找到一些 SSCMA 可用的的数据集,如下所示: + +| Dataset | Description | +| -- | -- | +| [Digital Meter Water](https://universe.roboflow.com/seeed-studio-dbk14/digital-meter-water/dataset/1) | Digital Meter Water Dataset | +| [Digital Meter Seg7](https://universe.roboflow.com/seeed-studio-dbk14/digital-meter-seg7/dataset/1) | Digital Meter Seg7 Dataset | +| [Digit Seg7 Classification](https://universe.roboflow.com/seeed-studio-ovcjn/digit-seg7/1) | Digit Seg7 Classification Dataset | + +### Kaggle + +[Kaggle](https://www.kaggle.com/) 是一个数据建模和数据分析竞赛平台。企业和研究者可在其上发布数据,统计学者和数据挖掘专家可在其上进行竞赛以产生最好的模型。Kaggle 也提供了数以千计的数据集,您可以访问 [Kaggle 数据集](https://www.kaggle.com/datasets) 挑选适合您需求的数据集。 + +### 其它数据集平台 + +您可以参考 [Datasets List](https://www.datasetlist.com/) 网站,该网站提供了大量的公共数据集,您可以根据自己的需求选择合适的数据集,需要注意的是,这些数据集可能需要经过转换才能在 SSCMA 中使用。 + + +## 常用公开数据集 + +我们在此列举了一些常用的公共数据集,您可以根据自己的需求选择合适的数据集。 + +### COCO 数据集 + +[COCO 数据集](https://cocodataset.org/) 是一个大规模的目标检测、分割和描述数据集,包含 330K 图像、1.5 百万目标实例、80 个目标类别、91 个材质类别、每张图 5 个描述和 25 万个带关键点的人像。 + +#### 应用场景 + +- **计算机视觉研究**:用于开发和测试图像识别、目标检测、图像分割和字幕生成等算法。 +- **自动驾驶**:用于训练车辆识别和定位道路上的行人、车辆和其他物体。 +- **机器人视觉**:用于训练机器人识别和操作环境中的物体。 +- **安防监控**:用于检测监控视频中的异常行为或特定对象。 +- **医疗影像分析**:用于辅助诊断,通过图像分割技术识别病变区域。 +- **增强现实(AR)**:用于在现实世界中叠加虚拟信息,需要精确的目标检测和图像分割技术。 +- **电子商务**:用于商品图像的自动分类和检索。 + +### ImageNet 数据集 + +[ImageNet 数据集](http://www.image-net.org/) 是一个大型的视觉对象识别数据库,包含超过 1400 万张经过标注的图像,涵盖了超过 2 万个类别。 + +#### 应用场景 + +- **计算机视觉研究**:用于开发和测试图像识别和目标定位算法。 +- **深度学习模型训练**:用于训练大规模的深度学习模型,如卷积神经网络(CNN)。 +- **图像搜索引擎**:用于提高图像搜索的准确性和相关性。 +- **安防监控**:用于识别和定位监控视频中的特定对象。 +- **自动驾驶**:用于训练车辆识别道路中的行人、车辆等。 +- **智能监控**:用于识别和分类监控摄像头捕获的图像。 +- **生物信息学**:用于分析和分类生物图像,如细胞和组织。 +- **内容过滤和版权保护**:用于自动识别和过滤不适当的内容或保护版权图像。 + +### FOMO 数据集 + +[FOMO 数据集](https://files.seeedstudio.com/sscma/datasets/coco_mask.zip) 是由 Seeed 制作的一个包含人脸和口罩的数据集,用于训练和测试口罩检测模型,其与 COCO 数据集有相同的目录结构和标注格式。 + +#### 应用场景 + +- **疫情防控**:用于检测人员是否佩戴口罩,确保公共场所的安全。 +- **安防监控**:用于监控视频中的人员佩戴情况,及时发现异常情况。 +- **智能门禁**:用于识别人员是否佩戴口罩,控制门禁系统的开关。 +- **智能巡检**:用于检测工人是否佩戴口罩,确保工作场所的安全。 +- **智能安检**:用于检测乘客是否佩戴口罩,确保交通工具的安全。 + +### Meter 数据集 + +[Meter 数据集](https://files.seeedstudio.com/sscma/datasets/meter.zip) 是由 Seeed 制作的一个包含指针表计和数字表计的数据集,用于训练和测试表计识别模型,其与 YOLO TXT 数据集有相同的目录结构和标注格式。 + +#### 应用场景 + +- **智能仪表读数**:用于自动识别和读取仪表上的指针读数。 +- **智能计量**:用于自动计量仪表上的指针读数,提高计量的准确性。 +- **智能监控**:用于监控仪表的读数,及时发现异常情况。 + + diff --git a/docs/zh_cn/introduction/quick_start.md b/docs/zh_cn/introduction/quick_start.md index 8f489059..966d672b 100644 --- a/docs/zh_cn/introduction/quick_start.md +++ b/docs/zh_cn/introduction/quick_start.md @@ -10,7 +10,7 @@ ### 模型部署 -如果您想在设备上部署模型,请参考[部署](../deploy/overview)章节,了解如何部署模型。 +如果您想在设备上部署模型,请参考[部署](../tutorials/deploy/overview)章节,了解如何部署模型。 ### 模型训练 @@ -48,15 +48,15 @@ - **模型训练**,请参考[模型训练](../tutorials/training/overview)以了解如何使用 [SSCMA](https://github.com/Seeed-Studio/ModelAssistant) 进行模型训练。我们建议您从示例中选择一个模型进行训练。 - - **模型导出**。完成模型训练后,为了在边缘计算设备上部署,首先需要导出模型。有关模型导出的教程,请参考[模型导出](../tutorials/export/overview)。 + - **模型导出**。完成模型训练后,为了在边缘计算设备上部署,首先需要导出模型。 - **模型验证**。模型验证可在训练或导出后进行。前者验证神经网络和训练结果的正确性,而后者主要验证导出模型的正确性,以便后续在边缘计算设备上进行部署和调试。上述两个步骤的文档中已经提供了一些模型验证的方法。 -- **模型部署**。如果您想将导出的训练模型部署在边缘计算设备上,请参考 [ESP32 部署示例](../deploy/esp32/deploy)或 [Grove Vision AI 部署示例](../deploy/grove/deploy)。 +- **模型部署**。如果您想将导出的训练模型部署在边缘计算设备上,请参考 [ESP32 部署示例](../tutorials/deploy/xiao_esp32s3)或 [Grove Vision AI 部署示例](../tutorials/deploy/grove_vision_ai_v2)。 -- **自定义数据集**。如果您想在自定义数据集上进行训练,请参考[数据集](../tutorials/datasets)。 +- **自定义数据集**。如果您想在自定义数据集上进行训练,请参考[数据集](../datasets/custom)。 -- **自定义模型**。如果您想修改现有的神经网络或设计自己的神经网络,请参考[模型配置](../tutorials/config)。 +- **自定义模型**。如果您想修改现有的神经网络或设计自己的神经网络,请参考[模型配置](../custom/basics)。 ## 必备知识 diff --git a/docs/zh_cn/tutorials/overview.md b/docs/zh_cn/tutorials/overview.md index cccadce1..7112a67d 100644 --- a/docs/zh_cn/tutorials/overview.md +++ b/docs/zh_cn/tutorials/overview.md @@ -33,7 +33,7 @@ graph TD - 处理缺失值、异常值和重复数据。 - 进行特征工程,提取有用的信息。 - 关于数据集的收集和处理方法,请参考 [SSCMA - 公共数据集](../datasets/public)。 - + ::: @@ -79,7 +79,7 @@ Batch Size 是指在训练过程中每次迭代更新权重时所使用的样本 :::tip -关于训练细节的更多信息,请参考 [SSCMA - 自定义 - 训练与验证管线](../custom/pipeline) 和 [SSCMA - 自定义 - 优化器](../custom/optimizer)。 +关于训练细节的更多信息,请参考 [SSCMA - 自定义 - 训练与验证管线](../custom/pipelines) 和 [SSCMA - 自定义 - 优化器](../custom/optimizer)。 ::: @@ -110,4 +110,3 @@ Batch Size 是指在训练过程中每次迭代更新权重时所使用的样本 - 定期检查模型的性能,并根据需要进行更新。 ::: -