Welcome to the ultimate guide for implementing the SSD (Single Shot MultiBox Detector) using PyTorch 1.0 or higher. This high-quality, fast, and modular reference implementation allows you to add your custom modules without hassle, making it ideal for researchers and developers alike.

Understanding SSD

Think of deploying SSD as setting up a high-tech security system in your home. Just as a diligent guard scans all the rooms simultaneously for any intruders, SSD enables the computer vision model to identify multiple objects in an image quickly. With single-shot detection, it offers speed and efficiency that any home security system needs!

Installation Guide

Requirements

• Python 3
• PyTorch 1.0 or higher
• yacs
• Vizer
• GCC ≥ 4.9
• OpenCV

Step-by-step Installation

Follow these commands for a seamless setup:

git clone https://github.com/lufficc/SSD.git
cd SSD
# Required packages:
pip install -r requirements.txt
# Done! Thats ALL! No BUILD! No bothering SETUP!

Training Your Model

Setting Up Datasets

Prepare your dataset structure as follows:

For Pascal VOC:

VOC_ROOT/
    └── VOC2007
        ├── JPEGImages
        ├── Annotations
        └── ImageSets
    └── VOC2012
        ├── JPEGImages
        ├── Annotations
        └── ImageSets

For COCO:

COCO_ROOT/
    ├── annotations
        ├── instances_valminusminival2014.json
        ├── instances_minival2014.json
        ├── instances_train2014.json
        └── instances_val2014.json
    ├── train2014
        ├── im-1-name.jpg
        ├── ...
    └── val2014
        ├── im-1-name.jpg
        ├── ...

Single GPU Training

Use the following command to train:

python train.py --config-file configs/vgg_ssd300_voc0712.yaml

Multi-GPU Training

To train using multiple GPUs:

export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --config-file configs/vgg_ssd300_voc0712.yaml

Evaluating Your Model

Single GPU Evaluation

To evaluate using single GPU:

python test.py --config-file configs/vgg_ssd300_voc0712.yaml

Multi-GPU Evaluation

For evaluating with multiple GPUs:

export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS test.py --config-file configs/vgg_ssd300_voc0712.yaml

Running a Demo

To predict an image in a folder, run:

python demo.py --config-file configs/vgg_ssd300_voc0712.yaml --images_dir demo --ckpt https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_voc0712.pth

This command will automatically download the necessary weights and save predicted images with bounding boxes in the demo result folder.

Troubleshooting

If you experience issues while compiling this code, we have a list of common problems in TROUBLESHOOTING.md. If your issue is not addressed, please open a new issue. For more insights, updates, or to collaborate on AI development projects, stay connected with [fxis.ai](https://fxis.ai).

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.