Closed AgnesMO95 closed 2 years ago
👋 Hello @AgnesMO95, thank you for your interest in YOLOv5 🚀! 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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Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
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$ cd yolov5
$ pip install -r requirements.txt
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@AgnesMO95 👋 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.
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.
--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
--weights ''
argument:
python train.py --data custom.yaml --weights '' --cfg yolov5s.yaml
yolov5m.yaml
yolov5l.yaml
yolov5x.yaml
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.
--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
that your hardware allows for. Small batch sizes produce poor batchnorm statistics and should be avoided.hyp['obj']
will help reduce overfitting in those specific loss components. For an automated method of optimizing these hyperparameters, see our Hyperparameter Evolution Tutorial.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/](https://github.com/ultralytics/yolov5/issues/2844#issuecomment-851338384)
I already looked at the documentation and the different tutorials, but it does not help. I have a very small dataset around 200 images. And therefore I wonder I it is a problem that my label index of the different object (car, person etc) do not correspond to the coco.yaml file label index, when I finetune the weights?
@AgnesMO95 that's a non-issue.
This is my output, i would expect it to be better since inference was done before fine-tuning the model, and it was able to make some predictions on cars and persons
👋 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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@AgnesMO95 any improvement?
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Question
Hi, I have a dataset that includes car, truck, bus, motorcycle, bicycle and person, same categories as in the Coco dataset but fewer. My dataset is very small (images from videos taken from a self driving car). Before training I did inference with coco weights, but it did not perform very good, therefore I want to do transfer learning on my dataset. When I train my model with the pretrained model it performs even worse, get a mAP on 0.019. My label id's for the categories do not correspond with the Coco dataset, for instance is my label id for car 0 but for coco it is 2. I have created a custom yaml file with my own categories in the right order for my data. Is it a issue that the labels does not correspond, should I rather include all the labels from coco dataset and use include_class = [] in datasets.py to include my classes and relabel my label indexes to correspond to Coco? Or is it some other solution?
Additional
No response