Hi everyone, i have a custom dataset for a binary object detection problem. I use the pytorch model ’ `` fasterrcnn_resnet50_fpn. For this, as it said in the doc i need a target dictionnary containing ‘boxes’ and ‘label’.
There is an error in case my class is 0 (no object to detect), so I have no bbox to put in targets[‘boxes’].
As a temporary solution I set a value of bbox=[0,0,0,0] when there isn’t object in my image.
Is it a correct way to do ?
def my_transform(self,img,bboxes,label):
if self.transforms is not None and self.train:
transformed = self.transforms(image=np.array(img),bboxes=bboxes) # bbox can be an empty list
img = transformed['image']
bboxes = transformed['bboxes']
img = TF.to_tensor(img)
if self.normalize:
img = TF.normalize(img,self.mean,self.std)
label = torch.LongTensor(label)
bboxes = [l[:4] for l in bboxes] #we dont need name of the object anymore
bboxes = torch.FloatTensor(bboxes)
targets = {'boxes':bboxes,'labels':label}
return img, targets
def __getitem__(self,index):
img = Image.open(os.path.join(self.root_images,self.file_names[index]+'.png')).convert('RGB')
with open(os.path.join(self.root_annotation,self.file_names[index]+'.json'),'r') as f:
annotation = json.loads(f.read().strip())
bboxes = []
label = []
try:
for b in annotation['outputs']['object']:
bbox = [i for i in b['bndbox'].values()] #xmin ymin xmax ymax #VOC Format
bbox.append(b['name']) # Le label ici est exclusivement drone pour l'instant
bboxes.append(bbox)
label.append(1)
except:
raise RuntimeError('Something wrong with the annotation format')
if len(annotation['outputs']['object']) == 0:
label = [0] # if no object label is 0
bboxes = [[0,0,0,0]]
img, targets = self.my_transform(img, bboxes,label)
return img,targets
Thank you for your help