Object detection bdd100k with faster-rcnn
- Pytorch >= 1.1
- torchvision >= 0.5
directory structure :
-| BDD100k
-| images
-| 100k
-| train
-| test
-| val
-| labels
.......
By default, all paths and hyperparameters are loaded from cfg.py.
bdd_path is a data path, which might need to be changed depending on the user profile.
idx=0 means, bdd100k datasets will be used during the training.
########## User specific settings ##########################
bdd_path = "/mnt2/datasets/bdd100k" # datapath
batch_size = 64
num_epochs = 25
lr = 0.001
ckpt = False
model_name = "bdd100k_24.pth"
##############################################################
idx = 0
dset_list = ["bdd100k", "Cityscapes"]
ds = dset_list[idx]if idx=0, you can get the bdd dataset list under the datalist folder.
python get_datalist.py
-| datalists
-| bdd100k_train_images_path.txt
-| bdd100k_val_images_path.txt
You can modify the transform composition.
Refer to bdd.py under datasets folder.
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
transforms.append(T.resize((256,512)))
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)Support for baseline has been added. Domain adaptive features will be added later.
python train_baseline.py
Note that, you have to change the min, max value of size if user wants to use resize transform.
Default option provided by torchvision, has min_size=800, max_size=1333.
def get_model(num_classes):
#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, min_size=256, max_size=512, image_mean=[0.5,0.5,0.5], image_std=[0.5,0.5,0.5])
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = torchvision.models.detection.faster_rcnn.FastRCNNPredictor(
in_features, num_classes
) # replace the pre-trained head with a new one
return model.cuda()Evaluation in performed in COCO format. Users need to specify saved model_name in cfg.pyon which evaluation is supposed to occur.
CocoAPI needs to be compiled. first download it from here
$ cd cocoapi/PythonAPI
$ python setup.py build_ext install
Now evaluation can be performed.
$ python3 evaluation_script.py
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.175 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.117 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.071 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
Not supported, yet.
Not supported, yet.