Fine-tune dataset preparation

[HerneSong]

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Question

Hello, I have a quick question. I have a pre-trained model with 10 classes. But on one class (pedestrian), the performance is not good enough. I want to continue fine-tine my model. My idea is to use a video, in which we have cars and pedestrians. I know that even though I only want to fine-tune pedestrians, but I still have to label every existing object in the video. But the video doesn't have other classes, say cyclists. Can I train on this dataset? Will this bring bias to the model (decline performance on other classes not appeared in the video)?

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[github-actions[bot]]

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@HerneSong 👋 Hello! Thanks for asking about improving YOLOv5 🚀 training results.

Most of the time good results can be obtained with no changes to the models or training settings, provided your dataset is sufficiently large and well labelled. If at first you don't get good results, there are steps you might be able to take to improve, but we always recommend users first train with all default settings before considering any changes. This helps establish a performance baseline and spot areas for improvement.

If you have questions about your training results we recommend you provide the maximum amount of information possible if you expect a helpful response, including results plots (train losses, val losses, P, R, mAP), PR curve, confusion matrix, training mosaics, test results and dataset statistics images such as labels.png. All of these are located in your project/name directory, typically yolov5/runs/train/exp.

We've put together a full guide for users looking to get the best results on their YOLOv5 trainings below.

Dataset

• Images per class. ≥ 1500 images per class recommended
• Instances per class. ≥ 10000 instances (labeled objects) per class recommended
• Image variety. Must be representative of deployed environment. For real-world use cases we recommend images from different times of day, different seasons, different weather, different lighting, different angles, different sources (scraped online, collected locally, different cameras) etc.
• Label consistency. All instances of all classes in all images must be labelled. Partial labelling will not work.
• Label accuracy. Labels must closely enclose each object. No space should exist between an object and it's bounding box. No objects should be missing a label.
• Label verification. View train_batch*.jpg on train start to verify your labels appear correct, i.e. see example mosaic.
• Background images. Background images are images with no objects that are added to a dataset to reduce False Positives (FP). We recommend about 0-10% background images to help reduce FPs (COCO has 1000 background images for reference, 1% of the total). No labels are required for background images.

Model Selection

Larger models like YOLOv5x and YOLOv5x6 will produce better results in nearly all cases, but have more parameters, require more CUDA memory to train, and are slower to run. For mobile deployments we recommend YOLOv5s/m, for cloud deployments we recommend YOLOv5l/x. See our README table for a full comparison of all models.

• Start from Pretrained weights. Recommended for small to medium sized datasets (i.e. VOC, VisDrone, GlobalWheat). Pass the name of the model to the --weights argument. Models download automatically from the latest YOLOv5 release.

  python train.py --data custom.yaml --weights yolov5s.pt
                                           yolov5m.pt
                                           yolov5l.pt
                                           yolov5x.pt
                                           custom_pretrained.pt

• Start from Scratch. Recommended for large datasets (i.e. COCO, Objects365, OIv6). Pass the model architecture yaml you are interested in, along with an empty --weights '' argument:

  python train.py --data custom.yaml --weights '' --cfg yolov5s.yaml
                                                    yolov5m.yaml
                                                    yolov5l.yaml
                                                    yolov5x.yaml

Training Settings

Before modifying anything, first train with default settings to establish a performance baseline. A full list of train.py settings can be found in the train.py argparser.

• Epochs. Start with 300 epochs. If this overfits early then you can reduce epochs. If overfitting does not occur after 300 epochs, train longer, i.e. 600, 1200 etc epochs.
• Image size. COCO trains at native resolution of --img 640, though due to the high amount of small objects in the dataset it can benefit from training at higher resolutions such as --img 1280. If there are many small objects then custom datasets will benefit from training at native or higher resolution. Best inference results are obtained at the same --img as the training was run at, i.e. if you train at --img 1280 you should also test and detect at --img 1280.
• Batch size. Use the largest --batch-size that your hardware allows for. Small batch sizes produce poor batchnorm statistics and should be avoided.
• Hyperparameters. Default hyperparameters are in hyp.scratch-low.yaml. We recommend you train with default hyperparameters first before thinking of modifying any. In general, increasing augmentation hyperparameters will reduce and delay overfitting, allowing for longer trainings and higher final mAP. Reduction in loss component gain hyperparameters like hyp['obj'] will help reduce overfitting in those specific loss components. For an automated method of optimizing these hyperparameters, see our Hyperparameter Evolution Tutorial.

Further Reading

If you'd like to know more a good place to start is Karpathy's 'Recipe for Training Neural Networks', which has great ideas for training that apply broadly across all ML domains: http://karpathy.github.io/2019/04/25/recipe/

Good luck 🍀 and let us know if you have any other questions!

[HerneSong]

Hello, glenn. I trained my model with 9 backbone layers frozen for 300 epochs just as this tutorial guided. The metrics were good, loss converged. My dataset was VisDrone which should be big enough and trained with default hyperparameters. But since the scale of VisDrone's pedestrian is not exactly the same as our inference video scene, I want to improve specifically class pedestrian.

And as I asked "I have a pre-trained model with 10 classes. But on one class (pedestrian), the performance is not good enough. I want to continue fine-tine my model. My idea is to use a video, in which we have cars and pedestrians. I know that even though I only want to fine-tune pedestrians, but I still have to label every existing object in the video. But the video doesn't have other classes, say cyclists. Can I train on this dataset? Will this bring bias to the model (decline performance on other classes not appeared in the video)?"

Do you have any suggestions?

[glenn-jocher]

@HerneSong yeah sure, sounds like a good idea. You can train on multiple datasets, see link below. As you mentioned though make sure all classes are labelled in both datasets.

https://community.ultralytics.com/t/how-to-combine-weights-to-detect-from-multiple-datasets

[Zephyr69]

As long as the image space of your finetuning dataset diverges from that of the original dataset, ofc you are introducing biases. And in general the effect of finetuning on non-tuned classes will be unpredictable as the weights are only being adapted to classes being tuned. You'd better prepare a new validation set to verify the model performance after finetuning.

Idk if finetuning is strictly needed due to some resource constraints. I'd train from scratch using the whole dataset instead.

[github-actions[bot]]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@Zephyr69 yes, you are absolutely right. Introducing biases when fine-tuning on a dataset with different characteristics is a valid concern.

Consideration of a new validation set is also a smart move to assess the model's performance post fine-tuning.

Training from scratch using the entire dataset might indeed be a preferable approach, as it allows the model to learn from the full dataset characteristics without any biases introduced from the fine-tuning process.

Good luck with your training!