yolov3-tiny addition

README.md

@@ -45,13 +45,21 @@ HS**V** Intensity | +/- 50%
 
 # Inference
 
-Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Download official YOLOv3 weights:
-
-- PyTorch format: https://storage.googleapis.com/ultralytics/yolov3.pt
-- Darknet format: https://pjreddie.com/media/files/yolov3.weights
-
+Run `detect.py` to apply trained weights to an image and visualize results, such as `zidane.jpg` from the `data/samples` folder, shown here. 
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
 
+# Pretrained Weights
+Download official YOLOv3 weights:
+
+**Darknet** format: 
+- https://pjreddie.com/media/files/yolov3.weights
+- https://pjreddie.com/media/files/yolov3-tiny.weights
+
+**PyTorch** format:
+- https://storage.googleapis.com/ultralytics/yolov3.pt 
+- https://storage.googleapis.com/ultralytics/yolov3-tiny.pt 
+
+
 # Validation mAP
 
 Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).

weights/download_yolov3_weights.sh

@@ -1,6 +1,8 @@
 #!/bin/bash
 
-wget https://pjreddie.com/media/files/darknet53.conv.74
 wget https://pjreddie.com/media/files/yolov3.weights
 wget https://pjreddie.com/media/files/yolov3-tiny.weights
+
+wget https://pjreddie.com/media/files/darknet53.conv.74
+
 wget https://storage.googleapis.com/ultralytics/yolov3.pt