updates

README.md

@@ -79,18 +79,19 @@ HS**V** Intensity | +/- 50%
 https://cloud.google.com/deep-learning-vm/  
 **Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)  
 **CPU platform:** Intel Skylake  
-**GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.80/hr) CUDA with Nvidia Apex FP16/32 
+**GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.80/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32  
 **HDD:** 100 GB SSD  
 **Dataset:** COCO train 2014 (117,263 images)
 
 GPUs | `batch_size` | batch time | epoch time | epoch cost
 --- |---| --- | --- | --- 
-1 K80 | 64 (32x2) | 2.9s  | 175min  | $0.58
-1 T4 | 64 (32x2) | 0.80s  | 49min  | $0.29
-2 T4 | 64 (64x1) | 0.52s  | 32min  | $0.36
-1 2080ti | 64 (32x2) | -  | -  | -
-1 V100 | 64 (32x2) | 0.38s | 23min | $0.31
-2 V100 | 64 (64x1) | 0.30s | 18min | $0.46
+K80 | 64 (32x2) | 2.90 s  | 175 min  | $0.58
+T4 | 64 (32x2) | 0.80 s  | 49 min  | $0.29
+T4 x2 | 64 (64x1) | 0.52 s  | 32 min  | $0.36
+V100 | 64 (32x2) | 0.38 s | 23 min | $0.31
+V100 x2 | 64 (64x1) | 0.30 s | 18 min | $0.46
+2080Ti | 64 (32x2) | 0.46 s  | 28 min  | - 
+
 
 # Inference
 

utils/datasets.py

@@ -40,6 +40,7 @@ def exif_size(img):
 
 class LoadImages:  # for inference
     def __init__(self, path, img_size=416):
+        path = str(Path(path))  # os-agnostic
         files = []
         if os.path.isdir(path):
             files = sorted(glob.glob(os.path.join(path, '*.*')))
@@ -154,6 +155,7 @@ class LoadWebcam:  # for inference
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
     def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False):
+        path = str(Path(path))  # os-agnostic
         with open(path, 'r') as f:
             self.img_files = [x for x in f.read().splitlines() if os.path.splitext(x)[-1].lower() in img_formats]