fix some typos (#182)

Conda/01 - Miniconda install instructions.md

@@ -43,6 +43,6 @@ conda update conda
 
 That's it! You have successfully installed Miniconda on your system.
 
-Now when you open up a terminal you should see a prompt and (base) to indicate no conda enviroment is active.
+Now when you open up a terminal you should see a prompt and (base) to indicate no conda environment is active.
 
 ![Your console](imgs/console.jpg)

README.md

@@ -127,7 +127,7 @@ Follow these sparkly steps to get your TensorRT ready for action! 🛠️✨
    You can use one of the .engine models we supply. But if it doesn't work, then you will need to re-export it. Grab the `.pt` file here for the model you want. We recommend `yolov5s.py` or `yolov5m.py` [HERE 🔗](https://github.com/ultralytics/yolov5/releases/tag/v7.0).
 
 12. **Run the Export Script** 🏃‍♂️💻
-   Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 mintues.
+   Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 minutes.
    
    ```
    python .\export.py --weights ./yolov5s.pt --include engine --half --imgsz 320 320 --device 0

customScripts/AimAssist/readme.md

@@ -1,7 +1,7 @@
 # Performance optimizations
 
 This version aimes to achieve the best performance possible on AMD hardware.
-To achieve this, the script acts more as an aim assist insted of a full fledged aimbot.
+To achieve this, the script acts more as an aim assist instead of a full fledged aimbot.
 The user will still need to do most on the aim
 
 Changes that have been made:

ultralytics1/utils/augmentations.py

@@ -330,7 +330,7 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, saturation, 0 hue
                     T += [A.ColorJitter(*color_jitter, 0)]
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]

ultralytics1/utils/loggers/wandb/README.md

@@ -68,7 +68,7 @@ Run information streams from your environment to the W&B cloud console as you tr
 You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
 
 <details open>
- <h3> 1: Train and Log Evaluation simultaneousy </h3>
+ <h3> 1: Train and Log Evaluation simultaneously </h3>
    This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
    Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
    so no images will be uploaded from your system more than once.
@@ -102,7 +102,7 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
 </details>
 
 <h3> 4: Save model checkpoints as artifacts </h3>
-  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
+  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base command, where `n` represents checkpoint interval.
   You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
 
 <details>

utils/augmentations.py

@@ -330,7 +330,7 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, saturation, 0 hue
                     T += [A.ColorJitter(*color_jitter, 0)]
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]

utils/loggers/wandb/README.md

@@ -68,7 +68,7 @@ Run information streams from your environment to the W&B cloud console as you tr
 You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
 
 <details open>
- <h3> 1: Train and Log Evaluation simultaneousy </h3>
+ <h3> 1: Train and Log Evaluation simultaneously </h3>
    This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
    Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
    so no images will be uploaded from your system more than once.
@@ -102,7 +102,7 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
 </details>
 
 <h3> 4: Save model checkpoints as artifacts </h3>
-  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
+  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base command, where `n` represents checkpoint interval.
   You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
 
 <details>