Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2018-12-03 21:08:45 +01:00
parent dc704edf17
commit 10cca39934
4 changed files with 6 additions and 14 deletions
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4
cfg/coco.data
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@ -1,6 +1,6 @@
classes=80 classes=80
train=/Users/glennjocher/Downloads/DATA/coco/trainvalno5k.txt train=../coco/trainvalno5k.txt
valid=/Users/glennjocher/Downloads/DATA/coco/5k.txt valid=../coco/5k.txt
names=data/coco.names names=data/coco.names
backup=backup/ backup=backup/
eval=coco eval=coco

5
train.py
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@ -44,10 +44,7 @@ def main(opt):
# Configure run # Configure run
data_config = parse_data_config(opt.data_config_path) data_config = parse_data_config(opt.data_config_path)
num_classes = int(data_config['classes']) num_classes = int(data_config['classes'])
if platform == 'darwin': # MacOS (local) train_path = '../coco/trainvalno5k.txt'
train_path = data_config['train']
else: # linux (cloud, i.e. gcp)
train_path = '../coco/trainvalno5k.part'
# Initialize model # Initialize model
model = Darknet(opt.cfg, opt.img_size) model = Darknet(opt.cfg, opt.img_size)

9
utils/datasets.py
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@ -66,12 +66,7 @@ class load_images_and_labels(): # for training
with open(path, 'r') as file: with open(path, 'r') as file:
self.img_files = file.readlines() self.img_files = file.readlines()
if platform == 'darwin': # MacOS (local) self.img_files = [path.replace('\n', '') for path in self.img_files]
self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/data/coco/images')
for path in self.img_files]
else: # linux (gcp cloud)
self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]
self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
self.img_files] self.img_files]
@ -287,7 +282,7 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
return imw return imw
def convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'): def convert_tif2bmp(p='../xview/val_images_bmp'):
import glob import glob
import cv2 import cv2
files = sorted(glob.glob('%s/*.tif' % p)) files = sorted(glob.glob('%s/*.tif' % p))

2
utils/utils.py
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@ -424,7 +424,7 @@ def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
torch.save(a, filename.replace('.pt', '_lite.pt')) torch.save(a, filename.replace('.pt', '_lite.pt'))
def coco_class_count(path='/Users/glennjocher/downloads/DATA/coco/labels/train2014/'): def coco_class_count(path='../coco/labels/train2014/'):
import glob import glob
nC = 80 # number classes nC = 80 # number classes