# MS-YOLO-DLKA

**Repository Path**: nan-zongliang/ms-yolo-dlka

## Basic Information

- **Project Name**: MS-YOLO-DLKA
- **Description**: No description available
- **Primary Language**: Python
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-25
- **Last Updated**: 2025-03-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MS-YOLO-DLKA

#### Introduction
An OD algorithm for track images based on YOLOv5


#### Installation

Installation requirements refer to the “requirements.txt” in the MS-YOLO-DLKA folder

#### Used

1.All modules have been integrated into the project, implemented in MS-YOLO-DLKA/models/yolo.py
2.The trained weights are MS-YOLO-DLKA/MS-YOLO-DLKA(-last).pt
3.The annotated YOLO format database path is MS-YOLO-DLKA/VOCdevkit
4.The YAML configuration file is stored in MS-YOLO-DLKA//data/MS-YOLO-DLKA.yaml


#### Common Command Guide
Here are the Python commands for training, validation, and detection using YOLOv5:

1. **Training**:
   To train a MS-YOLO-DLKA model, you can use the `train.py` script. A typical command looks like this:

   ```
   python train.py --img 640 --batch 16 --epochs 100 --data data/MS-YOLO-DLKA.yaml --cfg models/MS-YOLO-DLKA.yaml --weights MS-YOLO-DLKA.pt
   ```

   - `--img 640`: Sets the input image size to 640x640 pixels.
   - `--batch 16`: Sets the batch size to 16.
   - `--epochs 100`: Sets the number of training epochs to 100.
   - `--data data/coco128.yaml`: Specifies the dataset configuration file.
   - `--weights MS-YOLO-DLKA.pt`: Specifies the pre-trained weights to use (e.g., `MS-YOLO-DLKA.pt` for the small model).

2. **Validation**:
   Validation is usually performed during training, but if you want to run it separately, you can use the `--val` flag with the `train.py` script or the `val.py` script. For example, using `train.py` with validation:

   ```bash
   python train.py --img 640 --batch 16 --epochs 100 --data data/MS-YOLO-DLKA.yaml --weights yolov5s.pt --val
   ```

   Alternatively, using `val.py` for validation:

   ```bash
   python val.py --img 640 --data data/coco128.yaml --weights runs/train/exp/weights/best.pt
   ```

   - `--weights runs/train/exp/weights/best.pt`: Specifies the trained weights to use for validation (e.g., the best weights from a training run).

3. **Detection**:
   To perform object detection on a set of images, you can use the `detect.py` script. A typical command looks like this:

   ```bash
   python detect.py --weights runs/train/exp/weights/best.pt --img 640 --source path/to/images
   ```

   - `--weights runs/train/exp/weights/best.pt`: Specifies the trained weights to use for detection.
   - `--img 640`: Sets the input image size to 640x640 pixels.
   - `--source path/to/images/or/video`: Specifies the source of the images or video to detect objects in.

These commands provide a starting point, and you can adjust the flags and parameters according to your specific needs and hardware configuration.

Example:
python train.py --data data/MS-YOLO-DLKA.yaml --cfg models/MS-YOLO-DLKA.yaml --batch-size 16 --epochs 100 --img-size 640 --name MS-YOLO-DLKAresults

Database Description：
The original database comes from the network and provides the smallest sample for network learning, which may have some deviation from the results.
The database can only be used for academic exchange and discussion, and cannot be used for commercial purposes.
No label information provided, please label according to the purpose.
Please configure the MS-YOLO-DLKA.yaml file according to the tag information by yourself.