Update default --epochs 100 (#10024)

* Update default `--epochs 100`

@AyushExel @kalenmike updating default Detection and Segmentation trainings to 100 epochs

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update train.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update README.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update README_cn.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.github/README_cn.md

@@ -111,11 +111,11 @@ python detect.py --source 0  # 网络摄像头
 数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。
 
 ```bash
-python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
-                                       yolov5s                                64
-                                       yolov5m                                40
-                                       yolov5l                                24
-                                       yolov5x                                16
+python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
+                                                                 yolov5s                    64
+                                                                 yolov5m                    40
+                                                                 yolov5l                    24
+                                                                 yolov5x                    16
 ```
 
 <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">