Update README.md

Author: RaivoKoot

README.md

@@ -120,7 +120,7 @@ the `imagefile_template` parameter as "img_{:05d}.jpg", is all that it takes to
 
 ### 3. Video Frame Sampling Method
 When loading a video, only a number of its frames are loaded. They are chosen in the following way:
-1. The frame indices [1,N] are divided into NUM_SEGMENTS even segments. From each segment, FRAMES_PER_SEGMENT consecutive indices are chosen at random.
+1. The frame indices [1,N] are divided into NUM_SEGMENTS even segments. From each segment, a random start-index is sampled from which FRAMES_PER_SEGMENT consecutive indices are loaded.
 This results in NUM_SEGMENTS*FRAMES_PER_SEGMENT chosen indices, whose frames are loaded as PIL images and put into a list and returned when calling
 `dataset[i]`.
 ![alt text](https://github.com/RaivoKoot/images/blob/main/Sparse_Temporal_Sampling.jpg "Sparse-Temporal-Sampling-Strategy")
@@ -141,8 +141,8 @@ As of `torchvision 0.8.0`, all torchvision transforms can now also operate on ba
 on the batch identically on all images of the batch. Therefore, any torchvision transform can be used here to apply video-uniform preprocessing and augmentation.
 
 REMEMBER:  
-Pytorch transforms are applied to individual dataset samples (in this case a video frame PIL list, or a frame tensor after `imglist_totensor()`) before
-batching. So, any transforms used here must expect its input to be a frame tensor of shape `FRAMES x CHANNELS x HEIGHT x WIDTH` or a list of PIL images if `imglist_totensor()` is not used.
+Pytorch transforms are applied to individual dataset samples (in this case a video frame PIL list, or a frame tensor after `ImglistToTensor()`) before
+batching. So, any transforms used here must expect its input to be a frame tensor of shape `FRAMES x CHANNELS x HEIGHT x WIDTH` or a list of PIL images if `ImglistToTensor()` is not used.
 ### 6. Conclusion
 A proper code-based explanation on how to use VideoFrameDataset for training is provided in `demo.py`