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How Yolov5 calculate mAP@.5 and mAP@.5:.95 and plot images? #4052
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👋 Hello @GamuzaLu, 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. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. RequirementsPython>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started: $ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. |
@GamuzaLu 👋 Hello, thank you for asking about the differences between train.py, detect.py and test.py in YOLOv5. These 3 files are designed for different purposes and utilize different dataloaders with different settings. train.py dataloaders are designed for a speed-accuracy compromise, test.py is designed to obtain the best mAP on a validation dataset, and detect.py is designed for best real-world inference results. A few important aspects of each:
|
# Trainloader | |
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, | |
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, | |
world_size=opt.world_size, workers=opt.workers, | |
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) |
Lines 199 to 202 in fca5e2a
testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader | |
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, | |
world_size=opt.world_size, workers=opt.workers, | |
pad=0.5, prefix=colorstr('val: '))[0] |
640
False
0.001
0.6
True
None
test.py
- dataloader LoadImagesAndLabels(): designed to load train, val, test dataset images and labels. Augmentation capable but disabled.
Lines 89 to 90 in fca5e2a
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, prefix=colorstr(f'{task}: '))[0] - image size:
640
- rectangular inference:
True
- confidence threshold:
0.001
- iou threshold:
0.6
- multi-label:
True
- padding:
0.5 * maximum stride
detect.py
- dataloaders (multiple): designed for loading multiple types of media (images, videos, globs, directories, streams).
Lines 46 to 53 in fca5e2a
# Set Dataloader vid_path, vid_writer = None, None if webcam: view_img = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride) else: dataset = LoadImages(source, img_size=imgsz, stride=stride) - image size:
640
- rectangular inference:
True
- confidence threshold:
0.25
- iou threshold:
0.45
- multi-label:
False
- padding:
None
YOLOv5 PyTorch Hub
models.autoShape()
class used for image loading, preprocessing, inference and NMS. For more info see YOLOv5 PyTorch Hub Tutorial
Lines 225 to 250 in fca5e2a
class autoShape(nn.Module): | |
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS | |
conf = 0.25 # NMS confidence threshold | |
iou = 0.45 # NMS IoU threshold | |
classes = None # (optional list) filter by class | |
def __init__(self, model): | |
super(autoShape, self).__init__() | |
self.model = model.eval() | |
def autoshape(self): | |
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() | |
return self | |
@torch.no_grad() | |
@torch.cuda.amp.autocast(torch.cuda.is_available()) | |
def forward(self, imgs, size=640, augment=False, profile=False): | |
# Inference from various sources. For height=640, width=1280, RGB images example inputs are: | |
# filename: imgs = 'data/samples/zidane.jpg' | |
# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' | |
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) | |
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3) | |
# numpy: = np.zeros((640,1280,3)) # HWC | |
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) | |
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | |
- image size:
640
- rectangular inference:
True
- confidence threshold:
0.25
- iou threshold:
0.45
- multi-label:
False
- padding:
None
👋 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. Access additional YOLOv5 🚀 resources:
Access additional Ultralytics ⚡ resources:
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 ⭐! |
❔Question
I have three question:
When I am using
val.py
, no matter what the--iou-thres
and--conf-thres
I set. Thetest_batch0_pred.jpg
will plot the bbox only with bbox conf-thres greater then default value 0.25.So if I set the
--iou-thres
and--conf-thres
by myself, it seems that mAP calculation and plot function will under different criteria. Is it normal?While the record in txt did follow the
--conf-thres
I set. Buttest_batch0_pred.jpg
is not.Where can I control the threshold of plot iou?
Sometimes val.py output a
test_batch0_pred.jpg
with mosaic image. Did anyone face this problem too?Is it normal that I apply
val.py
anddetect.py
without any customize parameter to the same dataset, but get different result?Thank you!
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