How to Train a Scaled-YOLOv4 Object Detection Model

In this blog, we will show an example of how to train and generalize Scaled-YOLOv4 on your custom dataset to detect custom objects.

4 months ago   •   9 min read

By Jacob Solawetz

Object detection technology recently took a step forward with the publication of Scaled-YOLOv4 – a new state-of-the-art machine learning model for object detection.

In this blogpost we'll look at the breakthroughs involved in the creation of the Scaled-YOLOv4 model and then we'll work through an example of how to generalize and train the model on a custom dataset to detect custom objects.

Detecting lifts and jet skis from above via drone using Scaled-YOLOv4. Training data: public Aerial Maritime dataset.

We've included the following resources in this tutorial:

In this tutorial we'll be leveraging Roboflow for computer vision data management and Paperspace for GPU compute resources.

Why Scaled-YOLOv4?

Scaled-YOLOv4 is now the best model for object detection based on the Microsoft COCO benchmark.

Scaled-YOLOv4 achieves record breaking performance on the COCO benchmark. Not pictured YOLOv4-tiny running at 1774 FPS on the RTX 2080ti (source)

At Roboflow we've found that the Scaled-YOLOv4 family of models tops EfficientDet and all other existing object detection networks (as measured by mean average precision) across the tradeoff continuum of inference speed to network accuracy.

If you'd like to learn more about why Scaled-YOLOv4 is so good, check out the Scaled-YOLOv4 breakdown we wrote over on the Roboflow blog. We also recommend checking out the Scaled-YOLOv4 paper to explore the benchmarks as presented by the original authors.

Once you've read up on the enabling technology, let's get to training!

Assembling Custom Object Detection Data

In order to supervise our custom Scaled-YOLOv4 object detector, we will need to gather object detection training data. If you would like to follow along directly with this tutorial, you can fork the public aerial maritime dataset using the Fork button in the upper right of the dataset page:

To fork the public aerial maritime dataset, use the Fork Dataset feature in Roboflow

Collecting Your Own Images

Alternatively, if you'd like to use your own images, we recommend gathering images that are representative of the conditions that your model will face in deployment. You can get started with a small batch of images to gauge feasibility and then scale-up later – but in general the more diverse the images the better the end result.

Labeling Your Data

In this tutorial, we'll be using images from the public aerial maritime dataset. Once you have the images that you'd like to use to train your model, it's time to label them.

You can now label your data directly in Roboflow as seen below:

Uploading data Roboflow after creating a new dataset
Labeling data in Roboflow

To label images, you will be drawing bounding boxes around objects that you want to detect. Here are some tips on labeling images for this kind of computer vision application:

  1. Label every object of interest in every image
  2. Label the entirety of an object
  3. Label occluded objects
  4. Create tight bounding boxes
  5. Create specific label names
  6. Maintain clear labeling instructions
  7. Use labeling tools like CVAT, LabelImg, RectLabel, and Roboflow

To label data in manually in Roboflow, you will first upload your raw images and create a new dataset. In the new dataset page, all you need to do to start labeling is click on an image and draw bounding boxes. For more details on annotation, check out the labeling docs.

Exporting Data to Paperspace

Once you are satisfied with your labeled dataset you can go ahead and generate a dataset version in Roboflow. To do this select Download from the dataset view in Roboflow. You can also choose any preprocessing and augmentation options you like.

Select Download to generate a dataset
You should end up with a curl link to the dataset

After selecting Download, select TXT > Scaled-YOLOv4 as the output format and then select Get Link to obtain a curl link to you data.

Hold onto this link since you will use it to bring your dataset into your Paperspace notebook in just a minute.

Choose the Scaled-YOLOv4 dataset format
Visualizing our training data within Scaled YOLOv4

Setting up our training Environment on Paperspace

To train our Scaled-YOLOv4 model, we will first need to provision GPU resources to run our training job.

Because Scaled-YOLOv4 training requirements scale-up substantially when using larger networks in the family, Paperspace is a natural place to get started given the variety of on-demand GPU-backed instances available.

You can of course use any GPU resources you have available and still follow along with this tutorial, however.

Create Managed Cluster

To get started you'll first need to create a Paperspace account.

Once you're logged in to Paperspace, navigate to Gradient and then  Clusters, and then select Create a Managed Cluster.

Navigate to the Clusters tab in Paperspace Gradient and select Create a Managed Cluster

You will see your new private cluster in the Provisioning state – the cluster will take a few minutes to provision so please be patient.

It will take a few minutes for the new cluster to provision

Create Notebook

Once your cluster has been provisioned, you can launch a notebook for training in the Notebooks tab.

Select Create Notebook to initialize a new notebook in Paperspace
Name your notebook and select an auto-shutdown interval
Select an instance from your new private cluster

In the notebook setup prompt in Paperspace:

  1. In Step 1, give your notebook a name e.g. YOLOv4 Tutorial. You might also want to set auto-shutdown to 1-hour to prevent overcharging in the event that you forget to turn your machine off later.
  2. In Step 2, we can leave the container selection blank. We will specify a custom container in Step 4.
  3. In Step 3, choose My Private Clusters and then select your private cluster with the GPU that you'd like to train on. Larger GPUs will train your model faster – we used a P4000 for this tutorial.
  4. In Step 4, open the Advanced Options and copy the following NVIDIA PyTorch container into the Container Name field:
nvcr.io/nvidia/pytorch:20.06-py3
Paste this image location in Container Name field under Advanced Options

Advanced options should look like this:

Enter the image location into the Container Name field

Hit Create Notebook and Paperspace will spin-up your training environment on your new Private Cluster. This process may also take a couple minutes.

Installing Scaled YOLOv4 Dependencies

Now it's time to open the Jupyter notebook.

Installing a few dependencies in the new notebook

Once we're in the notebook we need to make a few installs before we are ready for training.

Installing Dependencies

We clone the Scaled-YOLOv4 repo and switch over to the yolov4-large branch.

#clone Scaled_YOLOv4
!git clone https://github.com/WongKinYiu/ScaledYOLOv4.git  # clone repo
%cd ./ScaledYOLOv4/
#checkout the yolov4-large branch
!git checkout yolov4-large
%cd ..

Next we'll install mish-cuda for our GPU so we can run the mish activation functions quickly on our notebook's GPU.

!git clone https://github.com/JunnYu/mish-cuda
%cd mish-cuda
!python setup.py build install
%cd ..

Downloading Data

Finally, import your curl link from Roboflow to bring in your data in the right format. This should be executed one directory up from the ScaledYOLOv4 repo folder.

!curl -L "https://app.roboflow.com/ds/h91wwIw5An?key=[YOUR KEY HERE]" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip
Downloading data into the notebook

Kicking Off Scaled YOLOv4 Training

Now that we have everything set up, we need to only invoke one command to kick off training on our custom data.

cd ./ScaledYOLOv4/
!python train.py --img 416 --batch 16 --epochs 50 --data '../data.yaml' --cfg ./models/yolov4-csp.yaml --weights '' --name yolov4-csp-results  --cache

The following options are possible:

- img: define input image size
- batch: determine batch size
- epochs: define the number of training epochs. (Note: often, 3000+ are common here!)
- data: set the path to our yaml file
- cfg: specify our model configuration
- weights: specify a custom path to weights.
- name: result names
- nosave: only save the final checkpoint
- cache: cache images for faster training

Once training has kicked off, you want to watch the mAP (mean average precision) metric rise, if it levels off you can stop the script.

After training, you can take a look at your Tensorboard metrics, again focusing on the mAP:

# Start tensorboard
# Launch after you have started training
# logs save in the folder "runs"
%load_ext tensorboard
%tensorboard --logdir runs
Scaled YOLOv4 Tensorboard

Scaling up

If you want to use larger version of the network, switch the cfg parameter in training. In the models folder you'll see a variety of options of model configuration including yolov4-p5, yolov4-p6, and the famed yolov4-p7. To train these larger models, the single GPU may not suit you and you may need to spin up a multi-GPU server and train on multi-GPU with a distributed launch:

python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights '' --sync-bn --device 0,1,2,3 --name yolov4-p5

Using Scaled YOLOv4 Models for Inference

Now that you've trained your Scaled YOLOv4 model, you can leverage your model to make inference on new images. To do so, we point the model at our dataset's test set, and point the detection script to our custom weights (you can also specify video here):

!python detect.py --weights ./runs/exp0_yolov4-csp-results/weights/best.pt --img 416 --conf 0.4 --source ../test/images

And inference occurs quickly (especially on GPU)

/content/ScaledYOLOv4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', img_size=416, iou_thres=0.5, output='inference/output', save_txt=False, source='../test/images', update=False, view_img=False, weights=['./runs/exp1_yolov4-csp-results/weights/best.pt'])
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)

Fusing layers... Model Summary: 235 layers, 5.24921e+07 parameters, 5.04494e+07 gradients
image 1/32 /content/test/images/DJI_0262_JPG.rf.3878c367b5f00b7ce0f5c9bdcb4d8486.jpg: 416x416 Done. (0.020s)
image 2/32 /content/test/images/DJI_0262_JPG.rf.47ce7cf6d8e3e310ab9b2b5c15ebba72.jpg: 416x416 Done. (0.020s)
image 3/32 /content/test/images/DJI_0262_JPG.rf.560b36a2e292c1b3dee7eae7e1f3fbf0.jpg: 416x416 1 docks, Done. (0.021s)
image 4/32 /content/test/images/DJI_0262_JPG.rf.5f24b2ccccf544d3bb0c3cb740be0f4b.jpg: 416x416 1 lifts, Done. (0.021s)
image 5/32 /content/test/images/DJI_0262_JPG.rf.66b031d30a28587d2c06f38af05cb4ec.jpg: 416x416 1 docks, Done. (0.021s)
image 6/32 /content/test/images/DJI_0262_JPG.rf.8c378a23b8822f63a44ad24c8787fab3.jpg: 416x416 1 lifts, Done. (0.025s)
Inference speed of YOLOv4-CSP on Colab V100, single batch, 50FPS

Then, we can visualize our networks test inference.

Test inference on an image the model has never seen. 

Exporting Weights and Deployment

Finally, at the end of the notebook we can download our custom model weights. These are currently in PyTorch framework and you can invoke them with the same software we used for training. You can also convert these weights to other frameworks such as Tensor RT, ONNX, TorchScript, TensorFlow Saved Graph, TFLite.

The implementation of these other formats will be new software with new dependencies. The journey has begun!

Next Steps

Once you have gotten a feel for training your network in the notebook, you can consider formalizing your training and inference flow in a repository and utilizing Paperspace's autoscaling capabilities to deploy your network for inference.

Conclusion

Congratulations! You've learned how to train the state of the art on your custom objects with Scaled-YOLOv4.

At Paperspace and Roboflow, we are always excited for what you might build next.

As always, happy training!

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