Help training shapes

[bkbilly-intrack]

❔Question

I am trying to create a model that can recognize shapes (cylinder, sphere, box). I have followed the guide for training custom data, but the results are really disappointing. Can someone guide me on what I can change to have better results?

Additional context

shapes.yaml

train: ../data_shapes/images/train  # 175 images
val: ../data_shapes/images/val  # 26 images
test: ../data_shapes/images/test  # 49 images
nc: 3
names: [ 'cylinder', 'sphere', 'box' ]

data structure:

../data_shapes/
├── images
│   ├── test
│   │   ├── set31.jpg
│   │   ├── ...
│   │   └── set39.jpg
│   ├── train
│   │   ├── my_image_10.jpg
│   │   ├── ...
│   │   └── test_image_whitebackground_hard_9.jpg
│   └── val
│       ├── my_image_105.jpg
│       ├── ...
│       └── test_image_whitebackground_hard_2.jpg
└── labels
    ├── test
    │   ├── set31.txt
    │   ├── ...
    │   └── set39.txt
    ├── train
    │   ├── my_image_10.txt
    │   ├── ...
    │   └── test_image_whitebackground_hard_9.txt
    └── val
        ├── my_image_105.txt
        ├── ...
        └── test_image_whitebackground_hard_2.txt

Results

I have visualized the results here: https://wandb.ai/bkbilly/YOLOv5/runs/13v83iij?workspace=user-bkbilly

[github-actions[bot]]

👋 Hello @koulis-probotek, thank you for your interest in YOLOv3 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

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• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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[glenn-jocher]

@koulis-probotek 👋 Hello! Thanks for asking about improving 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. ≥1.5k images per class
• Instances per class. ≥10k instances (labeled objects) per class total
• 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.
• 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).

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

• 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.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/

[bkbilly-intrack]

@glenn-jocher I didn't try the YoloV5 instructions, though if you think that it will help, I will give it a try. Also I have a small dataset with a sample of images: 175 train, 26 val, 49 test. It's difficult to find annotated images for shapes, so is it going to be a problem? I have to mention that I did a train for only 5 epochs just like the tutorial for YoloV3 like so:

$ python train.py --img 640 --batch 16 --epochs 5 --data shapes.yaml --weights yolov3.pt

I've uploaded the output images here on github, though I have them visualized at the WanDB: https://wandb.ai/bkbilly/YOLOv5/runs/13v83iij?workspace=user-bkbilly

R_curve: results: test_batch0_labels: test_batch0_pred: train_batch0: train_batch1: train_batch2: confusion_matrix: F1_curve:

[glenn-jocher]

@koulis-probotek you're clearly not following the guidance I pasted above. I would recommend you read it closely and follow it for best results.

[bkbilly-intrack]

The results I posted are from the YoloV3 guidance. I will take a look on the one you posted for the YoloV5. I am kind of new on training new datasets, so thanks for pointing me to the right direction.

[glenn-jocher]

@bkbilly-intrack you're welcome! Training custom datasets can be challenging, but the YOLOv5 guide should help you optimize your results. If you have any more questions as you work through it, feel free to ask. Good luck!