diff --git a/README.md b/README.md
index 5275e9d..eec9bcb 100755
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with
 
 ## Inference
 
-Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`.
+detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `inference/output`.
 ```bash
 $ python detect.py --source 0  # webcam
                             file.jpg  # image 
@@ -81,22 +81,43 @@ $ python detect.py --source 0  # webcam
                             http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream
 ```
 
-To run inference on examples in the `./inference/images` folder:
-
+To run inference on example images in `inference/images`:
 ```bash
-$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
+$ python detect.py --source inference/images --weights yolov5s.pt --conf 0.25
 
-Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
-Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
+Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='inference/output', save_conf=False, save_txt=False, source='inference/images', update=False, view_img=False, weights='yolov5s.pt')
+Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
 
-Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
+Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
 
-image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
-image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
-Results saved to /content/yolov5/inference/output
+Fusing layers... 
+Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
+image 1/2 yolov5/inference/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
+image 2/2 yolov5/inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
+Results saved to yolov5/inference/output
+Done. (0.124s)
 ```
+<img src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500">  
 
-<img src="https://user-images.githubusercontent.com/26833433/83082816-59e54880-a039-11ea-8abe-ab90cc1ec4b0.jpeg" width="500">  
+### PyTorch Hub
+
+To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
+```python
+import torch
+from PIL import Image
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).fuse().eval()  # yolov5s.pt
+model = model.autoshape()  # for autoshaping of PIL/cv2/np inputs and NMS
+
+# Images
+img1 = Image.open('zidane.jpg')
+img2 = Image.open('bus.jpg')
+imgs = [img1, img2]  # batched list of images
+
+# Inference
+prediction = model(imgs, size=640)  # includes NMS
+```
 
 
 ## Training