diff --git a/Dockerfile b/Dockerfile
index 8840596483fc..59b1a03d210c 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,5 @@
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:20.08-py3
+FROM nvcr.io/nvidia/pytorch:20.09-py3
 
 # Install dependencies
 RUN pip install --upgrade pip
diff --git a/data/hyp.finetune.yaml b/data/hyp.finetune.yaml
index fe9cd55019f7..1b84cff95c2c 100644
--- a/data/hyp.finetune.yaml
+++ b/data/hyp.finetune.yaml
@@ -15,7 +15,7 @@ weight_decay: 0.00036
 warmup_epochs: 2.0
 warmup_momentum: 0.5
 warmup_bias_lr: 0.05
-giou: 0.0296
+box: 0.0296
 cls: 0.243
 cls_pw: 0.631
 obj: 0.301
diff --git a/data/hyp.scratch.yaml b/data/hyp.scratch.yaml
index 9f53e86dd3ab..43354316c095 100644
--- a/data/hyp.scratch.yaml
+++ b/data/hyp.scratch.yaml
@@ -10,7 +10,7 @@ weight_decay: 0.0005  # optimizer weight decay 5e-4
 warmup_epochs: 3.0  # warmup epochs (fractions ok)
 warmup_momentum: 0.8  # warmup initial momentum
 warmup_bias_lr: 0.1  # warmup initial bias lr
-giou: 0.05  # box loss gain
+box: 0.05  # box loss gain
 cls: 0.5  # cls loss gain
 cls_pw: 1.0  # cls BCELoss positive_weight
 obj: 1.0  # obj loss gain (scale with pixels)
diff --git a/data/scripts/get_coco.sh b/data/scripts/get_coco.sh
index 7f86377070a5..157a0b04cf86 100755
--- a/data/scripts/get_coco.sh
+++ b/data/scripts/get_coco.sh
@@ -8,14 +8,17 @@
 #     /yolov5
 
 # Download/unzip labels
-echo 'Downloading COCO 2017 labels ...'
 d='../' # unzip directory
-f='coco2017labels.zip' && curl -L https://github.com/ultralytics/yolov5/releases/download/v1.0/$f -o $f
-unzip -q $f -d $d && rm $f
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f='coco2017labels.zip'                                                                 # 68 MB
+echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
 
 # Download/unzip images
-echo 'Downloading COCO 2017 images ...'
 d='../coco/images' # unzip directory
-f='train2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f # 19G, 118k images
-f='val2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f   # 1G, 5k images
-# f='test2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f  # 7G,  41k images
+url=http://images.cocodataset.org/zips/
+f1='train2017.zip' # 19G, 118k images
+f2='val2017.zip'   # 1G, 5k images
+f3='test2017.zip'  # 7G, 41k images (optional)
+for f in $f1 $f2; do
+  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+done
diff --git a/data/scripts/get_voc.sh b/data/scripts/get_voc.sh
index 5658864f2251..5e488c827c59 100644
--- a/data/scripts/get_voc.sh
+++ b/data/scripts/get_voc.sh
@@ -8,79 +8,23 @@
 #     /yolov5
 
 start=$(date +%s)
-
-# handle optional download dir
-if [ -z "$1" ]; then
-  # navigate to ~/tmp
-  echo "navigating to ../tmp/ ..."
-  mkdir -p ../tmp
-  cd ../tmp/
-else
-  # check if is valid directory
-  if [ ! -d $1 ]; then
-    echo $1 "is not a valid directory"
-    exit 0
-  fi
-  echo "navigating to" $1 "..."
-  cd $1
-fi
-
-echo "Downloading VOC2007 trainval ..."
-# Download data
-curl -LO http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
-echo "Downloading VOC2007 test data ..."
-curl -LO http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
-echo "Done downloading."
-
-# Extract data
-echo "Extracting trainval ..."
-tar -xf VOCtrainval_06-Nov-2007.tar
-echo "Extracting test ..."
-tar -xf VOCtest_06-Nov-2007.tar
-echo "removing tars ..."
-rm VOCtrainval_06-Nov-2007.tar
-rm VOCtest_06-Nov-2007.tar
-
-end=$(date +%s)
-runtime=$((end - start))
-
-echo "Completed in" $runtime "seconds"
-
-start=$(date +%s)
-
-# handle optional download dir
-if [ -z "$1" ]; then
-  # navigate to ~/tmp
-  echo "navigating to ../tmp/ ..."
-  mkdir -p ../tmp
-  cd ../tmp/
-else
-  # check if is valid directory
-  if [ ! -d $1 ]; then
-    echo $1 "is not a valid directory"
-    exit 0
-  fi
-  echo "navigating to" $1 "..."
-  cd $1
-fi
-
-echo "Downloading VOC2012 trainval ..."
-# Download data
-curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
-echo "Done downloading."
-
-# Extract data
-echo "Extracting trainval ..."
-tar -xf VOCtrainval_11-May-2012.tar
-echo "removing tar ..."
-rm VOCtrainval_11-May-2012.tar
+mkdir -p ../tmp
+cd ../tmp/
+
+# Download/unzip images and labels
+d='.' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
+f2=VOCtest_06-Nov-2007.zip     # 438MB, 4953 images
+f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
+for f in $f1 $f2 $f3; do
+  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+done
 
 end=$(date +%s)
 runtime=$((end - start))
-
 echo "Completed in" $runtime "seconds"
 
-cd ../tmp
 echo "Spliting dataset..."
 python3 - "$@" <<END
 import xml.etree.ElementTree as ET
diff --git a/detect.py b/detect.py
index 67e4248a9509..a98fe7394854 100644
--- a/detect.py
+++ b/detect.py
@@ -1,6 +1,5 @@
 import argparse
 import os
-import platform
 import shutil
 import time
 from pathlib import Path
@@ -150,8 +149,8 @@ def detect(save_img=False):
     parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
     parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
diff --git a/hubconf.py b/hubconf.py
index 168b40502f25..94208e064784 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -10,7 +10,6 @@
 
 import torch
 
-from models.common import NMS
 from models.yolo import Model
 from utils.google_utils import attempt_download
 
@@ -36,9 +35,7 @@ def create(name, pretrained, channels, classes):
             state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict()  # to FP32
             state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
             model.load_state_dict(state_dict, strict=False)  # load
-
-            model.add_nms()  # add NMS module
-            model.eval()
+            # model = model.autoshape()  # cv2/PIL/np/torch inference:  predictions = model(Image.open('image.jpg'))
         return model
 
     except Exception as e:
diff --git a/models/common.py b/models/common.py
index 314c31f91aac..022ad00ba43e 100644
--- a/models/common.py
+++ b/models/common.py
@@ -1,9 +1,12 @@
 # This file contains modules common to various models
-import math
 
+import math
+import numpy as np
 import torch
 import torch.nn as nn
-from utils.general import non_max_suppression
+
+from utils.datasets import letterbox
+from utils.general import non_max_suppression, make_divisible, scale_coords
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -101,17 +104,68 @@ def forward(self, x):
 
 class NMS(nn.Module):
     # Non-Maximum Suppression (NMS) module
-    conf = 0.3  # confidence threshold
-    iou = 0.6  # IoU threshold
+    conf = 0.25  # confidence threshold
+    iou = 0.45  # IoU threshold
     classes = None  # (optional list) filter by class
 
-    def __init__(self, dimension=1):
+    def __init__(self):
         super(NMS, self).__init__()
 
     def forward(self, x):
         return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
 
 
+class autoShape(nn.Module):
+    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    img_size = 640  # inference size (pixels)
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self, model):
+        super(autoShape, self).__init__()
+        self.model = model
+
+    def forward(self, x, size=640, augment=False, profile=False):
+        # supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
+        #   opencv:     x = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
+        #   PIL:        x = Image.open('image.jpg')  # HWC x(720,1280,3)
+        #   numpy:      x = np.zeros((720,1280,3))  # HWC
+        #   torch:      x = torch.zeros(16,3,720,1280)  # BCHW
+        #   multiple:   x = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        p = next(self.model.parameters())  # for device and type
+        if isinstance(x, torch.Tensor):  # torch
+            return self.model(x.to(p.device).type_as(p), augment, profile)  # inference
+
+        # Pre-process
+        if not isinstance(x, list):
+            x = [x]
+        shape0, shape1 = [], []  # image and inference shapes
+        batch = range(len(x))  # batch size
+        for i in batch:
+            x[i] = np.array(x[i])[:, :, :3]  # up to 3 channels if png
+            s = x[i].shape[:2]  # HWC
+            shape0.append(s)  # image shape
+            g = (size / max(s))  # gain
+            shape1.append([y * g for y in s])
+        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
+        x = [letterbox(x[i], new_shape=shape1, auto=False)[0] for i in batch]  # pad
+        x = np.stack(x, 0) if batch[-1] else x[0][None]  # stack
+        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
+        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
+
+        # Inference
+        x = self.model(x, augment, profile)  # forward
+        x = non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+
+        # Post-process
+        for i in batch:
+            if x[i] is not None:
+                x[i][:, :4] = scale_coords(shape1, x[i][:, :4], shape0[i])
+        return x
+
+
 class Flatten(nn.Module):
     # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
     @staticmethod
diff --git a/models/export.py b/models/export.py
index 5eb885ddbcde..c5e96f13cee8 100644
--- a/models/export.py
+++ b/models/export.py
@@ -29,9 +29,6 @@
     set_logging()
     t = time.time()
 
-    # Input
-    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection
-
     # Load PyTorch model
     model = attempt_load(opt.weights, map_location=torch.device('cpu'))  # load FP32 model
     labels = model.names
@@ -40,6 +37,9 @@
     gs = int(max(model.stride))  # grid size (max stride)
     opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
 
+    # Input
+    img = torch.zeros(opt.batch_size, 3, *opt.img_size)  # image size(1,3,320,192) iDetection
+
     # Update model
     for k, m in model.named_modules():
         m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
diff --git a/models/yolo.py b/models/yolo.py
index 46f1375e50aa..0d46054ed21c 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -1,21 +1,22 @@
 import argparse
 import logging
-import math
 import sys
 from copy import deepcopy
 from pathlib import Path
 
+import math
+
 sys.path.append('./')  # to run '$ python *.py' files in subdirectories
 logger = logging.getLogger(__name__)
 
 import torch
 import torch.nn as nn
 
-from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS
+from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
 from models.experimental import MixConv2d, CrossConv, C3
 from utils.general import check_anchor_order, make_divisible, check_file, set_logging
-from utils.torch_utils import (
-    time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
+from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
+    select_device, copy_attr
 
 
 class Detect(nn.Module):
@@ -140,6 +141,7 @@ def forward_once(self, x, profile=False):
         return x
 
     def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
+        # https://arxiv.org/abs/1708.02002 section 3.3
         # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
         m = self.model[-1]  # Detect() module
         for mi, s in zip(m.m, m.stride):  # from
@@ -162,7 +164,7 @@ def _print_biases(self):
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         print('Fusing layers... ')
         for m in self.model.modules():
-            if type(m) is Conv and hasattr(Conv, 'bn'):
+            if type(m) is Conv and hasattr(m, 'bn'):
                 m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                 delattr(m, 'bn')  # remove batchnorm
@@ -170,15 +172,26 @@ def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         self.info()
         return self
 
-    def add_nms(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        if type(self.model[-1]) is not NMS:  # if missing NMS
-            print('Adding NMS module... ')
+    def nms(self, mode=True):  # add or remove NMS module
+        present = type(self.model[-1]) is NMS  # last layer is NMS
+        if mode and not present:
+            print('Adding NMS... ')
             m = NMS()  # module
             m.f = -1  # from
             m.i = self.model[-1].i + 1  # index
             self.model.add_module(name='%s' % m.i, module=m)  # add
+            self.eval()
+        elif not mode and present:
+            print('Removing NMS... ')
+            self.model = self.model[:-1]  # remove
         return self
 
+    def autoshape(self):  # add autoShape module
+        print('Adding autoShape... ')
+        m = autoShape(self)  # wrap model
+        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
+        return m
+
     def info(self, verbose=False):  # print model information
         model_info(self, verbose)
 
@@ -263,10 +276,6 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
     # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
     # y = model(img, profile=True)
 
-    # ONNX export
-    # model.model[-1].export = True
-    # torch.onnx.export(model, img, opt.cfg.replace('.yaml', '.onnx'), verbose=True, opset_version=11)
-
     # Tensorboard
     # from torch.utils.tensorboard import SummaryWriter
     # tb_writer = SummaryWriter()
diff --git a/requirements.txt b/requirements.txt
index 2ed5f48e94f6..0871ed666685 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -18,7 +18,7 @@ tqdm>=4.41.0
 
 # export --------------------------------------
 # packaging  # for coremltools
-# coremltools==4.0b4
+# coremltools==4.0
 # onnx>=1.7.0
 # scikit-learn==0.19.2  # for coreml quantization
 
diff --git a/sotabench.py b/sotabench.py
index daef5168b213..9507d0754e95 100644
--- a/sotabench.py
+++ b/sotabench.py
@@ -1,6 +1,5 @@
 import argparse
 import glob
-import json
 import os
 import shutil
 from pathlib import Path
@@ -8,19 +7,17 @@
 import numpy as np
 import torch
 import yaml
+from sotabencheval.object_detection import COCOEvaluator
+from sotabencheval.utils import is_server
 from tqdm import tqdm
 
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import (
     coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
-    xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
+    xyxy2xywh, clip_coords, set_logging)
 from utils.torch_utils import select_device, time_synchronized
 
-
-from sotabencheval.object_detection import COCOEvaluator
-from sotabencheval.utils import is_server
-
 DATA_ROOT = './.data/vision/coco' if is_server() else '../coco'  # sotabench data dir
 
 
@@ -113,7 +110,7 @@ def test(data,
 
             # Compute loss
             if training:  # if model has loss hyperparameters
-                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # GIoU, obj, cls
+                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
             t = time_synchronized()
diff --git a/test.py b/test.py
index e0bb7726f7d1..9e79a769f884 100644
--- a/test.py
+++ b/test.py
@@ -106,7 +106,7 @@ def test(data,
 
             # Compute loss
             if training:  # if model has loss hyperparameters
-                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # GIoU, obj, cls
+                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
             t = time_synchronized()
diff --git a/train.py b/train.py
index 4060a5701a8b..42774b880fc6 100644
--- a/train.py
+++ b/train.py
@@ -1,12 +1,13 @@
 import argparse
 import logging
-import math
 import os
 import random
 import shutil
 import time
 from pathlib import Path
+from warnings import warn
 
+import math
 import numpy as np
 import torch.distributed as dist
 import torch.nn.functional as F
@@ -195,7 +196,7 @@ def train(hyp, opt, device, tb_writer=None):
     hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
     model.nc = nc  # attach number of classes to model
     model.hyp = hyp  # attach hyperparameters to model
-    model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
+    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
     model.names = names
 
@@ -204,10 +205,11 @@ def train(hyp, opt, device, tb_writer=None):
     nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
     # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
+    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
-    logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n'
+    logger.info('Image sizes %g train, %g test\n'
+                'Using %g dataloader workers\nLogging results to %s\n'
                 'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
@@ -234,7 +236,7 @@ def train(hyp, opt, device, tb_writer=None):
         if rank != -1:
             dataloader.sampler.set_epoch(epoch)
         pbar = enumerate(dataloader)
-        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
+        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
         if rank in [-1, 0]:
             pbar = tqdm(pbar, total=nb)  # progress bar
         optimizer.zero_grad()
@@ -245,7 +247,7 @@ def train(hyp, opt, device, tb_writer=None):
             # Warmup
             if ni <= nw:
                 xi = [0, nw]  # x interp
-                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
+                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
                 accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
                 for j, x in enumerate(optimizer.param_groups):
                     # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
@@ -319,21 +321,21 @@ def train(hyp, opt, device, tb_writer=None):
 
             # Write
             with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
+                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
             if len(opt.name) and opt.bucket:
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Tensorboard
             if tb_writer:
-                tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
+                tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                         'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                        'val/giou_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
+                        'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                         'x/lr0', 'x/lr1', 'x/lr2']  # params
                 for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
                     tb_writer.add_scalar(tag, x, epoch)
 
             # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # fitness_i = weighted combination of [P, R, mAP, F1]
+            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
 
@@ -393,7 +395,7 @@ def train(hyp, opt, device, tb_writer=None):
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
     parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
     parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
+    parser.add_argument('--name', default='', help='renames experiment folder exp{N} to exp{N}_{name} if supplied')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
@@ -429,9 +431,8 @@ def train(hyp, opt, device, tb_writer=None):
         opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
         log_dir = increment_dir(Path(opt.logdir) / 'exp', opt.name)  # runs/exp1
 
-    device = select_device(opt.device, batch_size=opt.batch_size)
-
     # DDP mode
+    device = select_device(opt.device, batch_size=opt.batch_size)
     if opt.local_rank != -1:
         assert torch.cuda.device_count() > opt.local_rank
         torch.cuda.set_device(opt.local_rank)
@@ -440,15 +441,20 @@ def train(hyp, opt, device, tb_writer=None):
         assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
         opt.batch_size = opt.total_batch_size // opt.world_size
 
-    logger.info(opt)
+    # Hyperparameters
     with open(opt.hyp) as f:
         hyp = yaml.load(f, Loader=yaml.FullLoader)  # load hyps
+        if 'box' not in hyp:
+            warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %
+                 (opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))
+            hyp['box'] = hyp.pop('giou')
 
     # Train
+    logger.info(opt)
     if not opt.evolve:
         tb_writer = None
         if opt.global_rank in [-1, 0]:
-            logger.info('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir)
+            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
             tb_writer = SummaryWriter(log_dir=log_dir)  # runs/exp0
 
         train(hyp, opt, device, tb_writer)
@@ -463,7 +469,7 @@ def train(hyp, opt, device, tb_writer=None):
                 'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
                 'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
                 'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-                'giou': (1, 0.02, 0.2),  # GIoU loss gain
+                'box': (1, 0.02, 0.2),  # box loss gain
                 'cls': (1, 0.2, 4.0),  # cls loss gain
                 'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
                 'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
@@ -488,7 +494,7 @@ def train(hyp, opt, device, tb_writer=None):
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        yaml_file = Path('runs/evolve/hyp_evolved.yaml')  # save best result here
+        yaml_file = Path(opt.logdir) / 'evolve' / 'hyp_evolved.yaml'  # save best result here
         if opt.bucket:
             os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
 
@@ -532,5 +538,5 @@ def train(hyp, opt, device, tb_writer=None):
 
         # Plot results
         plot_evolution(yaml_file)
-        print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these '
-              'hyperparameters: $ python train.py --hyp %s' % (yaml_file, yaml_file))
+        print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
+              f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
diff --git a/utils/datasets.py b/utils/datasets.py
index 29ee4b051e85..9192dec4b7d9 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -1,5 +1,4 @@
 import glob
-import math
 import os
 import random
 import shutil
@@ -8,6 +7,7 @@
 from threading import Thread
 
 import cv2
+import math
 import numpy as np
 import torch
 from PIL import Image, ExifTags
diff --git a/utils/general.py b/utils/general.py
index da016631e589..f8415feef999 100755
--- a/utils/general.py
+++ b/utils/general.py
@@ -1,9 +1,9 @@
 import glob
 import logging
-import math
 import os
 import platform
 import random
+import re
 import shutil
 import subprocess
 import time
@@ -12,6 +12,7 @@
 from pathlib import Path
 
 import cv2
+import math
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
@@ -142,7 +143,7 @@ def check_dataset(dict):
     if val and len(val):
         val = [os.path.abspath(x) for x in (val if isinstance(val, list) else [val])]  # val path
         if not all(os.path.exists(x) for x in val):
-            print('\nWARNING: Dataset not found, nonexistant paths: %s' % [*val])
+            print('\nWARNING: Dataset not found, nonexistent paths: %s' % [*val])
             if s and len(s):  # download script
                 print('Downloading %s ...' % s)
                 if s.startswith('http') and s.endswith('.zip'):  # URL
@@ -157,7 +158,7 @@ def check_dataset(dict):
 
 
 def make_divisible(x, divisor):
-    # Returns x evenly divisble by divisor
+    # Returns x evenly divisible by divisor
     return math.ceil(x / divisor) * divisor
 
 
@@ -168,9 +169,9 @@ def labels_to_class_weights(labels, nc=80):
 
     labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
     classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
-    weights = np.bincount(classes, minlength=nc)  # occurences per class
+    weights = np.bincount(classes, minlength=nc)  # occurrences per class
 
-    # Prepend gridpoint count (for uCE trianing)
+    # Prepend gridpoint count (for uCE training)
     # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
     # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
 
@@ -509,11 +510,11 @@ def compute_loss(p, targets, model):  # predictions, targets, model
             pxy = ps[:, :2].sigmoid() * 2. - 0.5
             pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
             pbox = torch.cat((pxy, pwh), 1).to(device)  # predicted box
-            giou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # giou(prediction, target)
-            lbox += (1.0 - giou).mean()  # giou loss
+            iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
+            lbox += (1.0 - iou).mean()  # iou loss
 
             # Objectness
-            tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype)  # giou ratio
+            tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
 
             # Classification
             if model.nc > 1:  # cls loss (only if multiple classes)
@@ -528,7 +529,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
         lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
 
     s = 3 / np  # output count scaling
-    lbox *= h['giou'] * s
+    lbox *= h['box'] * s
     lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
     lcls *= h['cls'] * s
     bs = tobj.shape[0]  # batch size
@@ -819,7 +820,7 @@ def print_results(k):
     k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
     k *= s
     wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unflitered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
     k = print_results(k)
 
     # Plot
@@ -952,9 +953,12 @@ def increment_dir(dir, comment=''):
     # Increments a directory runs/exp1 --> runs/exp2_comment
     n = 0  # number
     dir = str(Path(dir))  # os-agnostic
-    d = sorted(glob.glob(dir + '*'))  # directories
-    if len(d):
-        n = max([int(x[len(dir):x.rfind('_') if '_' in Path(x).name else None]) for x in d]) + 1  # increment
+    dirs = sorted(glob.glob(dir + '*'))  # directories
+    if dirs:
+        matches = [re.search(r"exp(\d+)", d) for d in dirs]
+        idxs = [int(m.groups()[0]) for m in matches if m]
+        if idxs:
+            n = max(idxs) + 1  # increment
     return dir + str(n) + ('_' + comment if comment else '')
 
 
@@ -1234,7 +1238,7 @@ def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general im
 def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
     # Plot training 'results*.txt', overlaying train and val losses
     s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
-    t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
+    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
     for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
         results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
         n = results.shape[1]  # number of rows
@@ -1254,13 +1258,13 @@ def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_
         fig.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
-def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
-                 save_dir=''):  # from utils.general import *; plot_results()
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
+    # from utils.general import *; plot_results()
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()
-    s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
-         'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
+    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
+         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
     if bucket:
         # os.system('rm -rf storage.googleapis.com')
         # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
@@ -1277,7 +1281,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
             for i in range(10):
                 y = results[i, x]
                 if i in [0, 1, 2, 5, 6, 7]:
-                    y[y == 0] = np.nan  # dont show zero loss values
+                    y[y == 0] = np.nan  # don't show zero loss values
                     # y /= y[0]  # normalize
                 label = labels[fi] if len(labels) else Path(f).stem
                 ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index c587617b821c..f6818238452f 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -1,9 +1,9 @@
 import logging
-import math
 import os
 import time
 from copy import deepcopy
 
+import math
 import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn