sramirez
commented
4 years ago Hi! First of all, congratulations for your work. Any plan on releasing pre-trained weights for YOLOv5 with xView?
Open glenn-jocher opened 4 years ago
sramirez
commented
4 years ago Hi! First of all, congratulations for your work. Any plan on releasing pre-trained weights for YOLOv5 with xView?
glenn-jocher
commented
4 years ago @sramirez no, but you are free to train YOLOv5 on xView yourself :) See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
bartekrdz
commented
3 years ago @sramirez no, but you are free to train YOLOv5 on xView yourself :) See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
Is there a way to convert xView GeoJSON annotation file to YOLO format?
glenn-jocher
commented
3 years ago @bartekrdz yes of course. You'd probably want to write your own conversion script and then use YOLOv5 to get started. The only thing missing from YOLOv5 that's used here is a sliding window inference system to run very high res images at native resolution on smaller graphics cards, and a corresponding chip dataloader to train chips at native resolution. The YOLO label format is pretty simple, it's described in https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
pounde
commented
2 years ago Hello, I'm curious if anyone had trained an xView model on Yolo? I may go down that path if it hasn't been accomplished yet.
glenn-jocher
commented
2 years ago @pounde we've made it super to train YOLOv5 on xView. Instructions are in xView.yaml in the YOLOv5 repo. First download dataset zips as indicated and then run python train.py --data xView.yaml.
https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Example usage: python train.py --data xView.yaml
# parent
# ├── yolov5
# └── datasets
# └── xView ← downloads herePerfect, thank you. I just wanted to be sure no one had accomplished it before I set down that path. Thanks for all the hard work.
QuentinAndre11
commented
2 years ago Hello! I was wondering if someone (like @pounde for example) had reached good results for xView dataset or done any kind of hyperparameters optimization. I'm actually looking for pretrained weights to use to a more specific project on aerial images and I am wondering if I could use transfer learning or if I should train for xView at first.
glenn-jocher
commented
2 years ago @QuentinAndre11 xView is available on YOLOv5 now, I'd recommend just training it directly there:
python train.py --data xView.yamlFollow directions in yaml first to download: https://github.com/ultralytics/yolov5/blob/7cef03dddd6fba26fff6748ed1cfdd18208c193e/data/xView.yaml#L1-L9
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Example usage: python train.py --data xView.yaml
# parent
# ├── yolov5
# └── datasets
# └── xView ← downloads here (20.7 GB)@glenn-jocher Yes I followed it and used the script (I cannot login the xview website tho so I used kaggle to download the data) but I reach a MAP@0.5 score of 0.026 after 300 epochs, so I was wondering if the default settings were not really accurate here... I have 847-127 for train-val split so I guess it's the same as the original dataset.
glenn-jocher
commented
2 years ago @QuentinAndre11 👋 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.
train_batch*.jpg on train start to verify your labels appear correct, i.e. see example mosaic.
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.yamlBefore 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/
Good luck 🍀 and let us know if you have any other questions!
pounde
commented
2 years ago @QuentinAndre11 I have not set down the path of training xView on YOLO. The weights are available from the DIU S3 bucket. You can also take a look at the repo here for an implementation that may fit your needs.
ShaashvatShetty
commented
2 years ago Hello, I have been trying to implement an xView dataset using yolov5 and I followed the instructions. But I keep getting an error where it cannot find the labels. It seems to be able to find the images though. Any ideas?
glenn-jocher
commented
2 years ago @ShaashvatShetty I'd recommend going to the YOLOv5 repo as we have an xView.yaml all set up to start training with instructions on dataset download: https://github.com/ultralytics/yolov5
tanya-suri
commented
2 years ago @pounde we've made it super to train YOLOv5 on xView. Instructions are in xView.yaml in the YOLOv5 repo. First download dataset zips as indicated and then run
python train.py --data xView.yaml.https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license # DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA) # -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! -------- # Example usage: python train.py --data xView.yaml # parent # ├── yolov5 # └── datasets # └── xView ← downloads here
Can you please share the dataset file in utils folder as well.
glenn-jocher
commented
2 years ago @tanya-suri I don't quite understand your question, but perhaps you are asking about utils/datasets.py. This file has been renamed to utils/dataloaders.py recently in YOLOv5.
ShaashvatShetty
commented
2 years ago `path: ../datasets/xView # dataset root dir train: /content/drive/MyDrive/datasets/images/train # train images (relative to 'path') 90% of 847 train images val: /content/drive/MyDrive/datasets/images/val # train images (relative to 'path') 10% of 847 train images
nc: 60 # number of classes names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus', 'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer', 'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car', 'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge', 'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane', 'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck', 'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed', 'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad', 'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names
download: | import json import os from pathlib import Path import numpy as np from PIL import Image from tqdm import tqdm from utils.datasets import autosplit from utils.general import download, xyxy2xywhn def convert_labels(fname=Path('xView/xView_train.geojson')):
path = fname.parent
with open(fname) as f:
print(f'Loading {fname}...')
data = json.load(f)
# Make dirs
labels = Path(path / 'labels' / 'train')
os.system(f'rm -rf {labels}')
labels.mkdir(parents=True, exist_ok=True)
# xView classes 11-94 to 0-59
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
shapes = {}
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
p = feature['properties']
if p['bounds_imcoords']:
id = p['image_id']
file = path / 'train_images' / id
if file.exists(): # 1395.tif missing
try:
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
cls = p['type_id']
cls = xview_class2index[int(cls)] # xView class to 0-60
assert 59 >= cls >= 0, f'incorrect class index {cls}'
# Write YOLO label
if id not in shapes:
shapes[id] = Image.open(file).size
box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
with open((labels / id).with_suffix('.txt'), 'a') as f:
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
except Exception as e:
print(f'WARNING: skipping one label for {file}: {e}')dir = Path(yaml['/content/drive/MyDrive/datasets']) # dataset root dir urls = ['/content/drive/MyDrive/datasets/labels/train_labels.zip', # train labels 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images '/content/drive/MyDrive/datasets/images/val'] # 5G, 282 val images (no labels) download(urls, dir=dir, delete=False)
convert_labels(dir / 'xView_train.geojson')
images = Path(dir / 'images') images.mkdir(parents=True, exist_ok=True) Path(dir / 'train_images').rename(dir / 'images' / 'train') Path(dir / 'val_images').rename(dir / 'images' / 'val')
autosplit(dir / 'images' / 'train')`
Godofnothing
commented
2 years ago @ShaashvatShetty
you should have the following structure in your xView directory:
# parent
# xView
# └── train_images
# └── val_images (may be empty directory)
# └── xView_train.geojson@QuentinAndre11 300 epochs may not suffice, since the dataset is quite small in terms of number of images, but the images themselves contain many instances. I think, one should train withimage_size at least 1280 or even higher to obtain good results.
👋 Hello! Thanks for visiting! Ultralytics has open-sourced YOLOv5 🚀 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
YOLOv5-P5 640 Figure (click to expand)
Figure Notes (click to expand)
* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`Pretrained Checkpoints
(pixels)
0.5:0.95
0.5:0.95
0.5
V100 (ms)
(M)
640 (B)
Table Notes (click to expand)
* APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`For more information and to get started with YOLOv5 🚀 please visit https://github.com/ultralytics/yolov5. Thank you!