https://github.com/truongnv-hut/aicandy_ssd300_objectdetection_urentmnt
SSD300 for Object Detection
https://github.com/truongnv-hut/aicandy_ssd300_objectdetection_urentmnt
aicandy artificial-intelligence computer-vision deep-learning machine-learning object-detection ssd300
Last synced: 5 months ago
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SSD300 for Object Detection
- Host: GitHub
- URL: https://github.com/truongnv-hut/aicandy_ssd300_objectdetection_urentmnt
- Owner: TruongNV-hut
- Created: 2024-09-18T07:26:36.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-09-18T07:26:57.000Z (9 months ago)
- Last Synced: 2024-11-16T10:12:04.037Z (7 months ago)
- Topics: aicandy, artificial-intelligence, computer-vision, deep-learning, machine-learning, object-detection, ssd300
- Language: Python
- Homepage: https://aicandy.vn/
- Size: 645 KB
- Stars: 35
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SSD300 and Object detection
SSD300 (Single Shot MultiBox Detector)
is a deep learning model designed for object detection tasks. It uses a single feed-forward convolutional network to predict object classes and bounding box locations directly from an input image, without needing a separate region proposal stage. The "300" in SSD300 refers to the input image size of 300x300 pixels, making it faster and more efficient while maintaining high accuracy. SSD300 is known for its balance between speed and performance, making it suitable for real-time applications.
## Object detection
Object detection is a computer vision technique in machine learning that involves identifying and locating objects within an image or video. Unlike image classification, which assigns a label to an entire image, object detection not only classifies objects but also draws bounding boxes around them to specify their exact locations. This task is crucial for applications like autonomous driving, surveillance, and image analysis, where understanding the context and position of objects is essential.
## ❤️❤️❤️
```bash
If you find this project useful, please give it a star to show your support and help others discover it!
```
## Getting Started
### Clone the Repository
To get started with this project, clone the repository using the following command:
```bash
git clone https://github.com/TruongNV-hut/AIcandy_SSD300_ObjectDetection_urentmnt.git
```
### Install Dependencies
Before running the scripts, you need to install the required libraries. You can do this using pip:
```bash
pip install -r requirements.txt
```
### Training the Model
To train the model, use the following command:
```bash
python aicandy_ssd300_train_haykkxnu.py --train_dir /aicandy/datasets/aicandy_voc_nskpbsgv --num_epochs 500 --batch_size 8 --last_checkpoint 'aicandy_model_out_gnloibxd/aicandy_checkpoint_ssd300.pth'
```
### Testing the Model
After training, you can test the model using:
```bash
python aicandy_ssd300_test_qgtlqrlv.py --image_path image_test.jpg --checkpoint aicandy_model_out_gnloibxd/aicandy_checkpoint_ssd300.pth
```
### Converting to ONNX Format
To convert the model to ONNX format, run:
```bash
python aicandy_ssd300_convert_rqicuvtl.py --model_path aicandy_model_out_gnloibxd/aicandy_checkpoint_ssd300.pth --onnx_path aicandy_model_out_gnloibxd/aicandy_model_kqgmngun.onnx
```
### More Information
To learn more about this project, [see here](https://aicandy.vn/ung-dung-mang-ssd300-vao-phat-hien-doi-tuong).
To learn more about knowledge and real-world projects on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), visit the website [aicandy.vn](https://aicandy.vn/).
❤️❤️❤️