Push the latest version for training.

models/hand-detection/Dockerfile

 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:20.09-py3
+FROM nvcr.io/nvidia/pytorch:21.03-py3
 
-# Install dependencies
-RUN pip install --upgrade pip
-# COPY requirements.txt .
-# RUN pip install -r requirements.txt
-RUN pip install gsutil
+# Install linux packages
+RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
+
+# Install python dependencies
+COPY requirements.txt .
+RUN python -m pip install --upgrade pip
+RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
+RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -14,11 +17,8 @@ WORKDIR /usr/src/app
 # Copy contents
 COPY . /usr/src/app
 
-# Copy weights
-#RUN python3 -c "from models import *; \
-#attempt_download('weights/yolov5s.pt'); \
-#attempt_download('weights/yolov5m.pt'); \
-#attempt_download('weights/yolov5l.pt')"
+# Set environment variables
+ENV HOME=/usr/src/app
 
 
 # ---------------------------------------------------  Extras Below  ---------------------------------------------------
@@ -28,7 +28,7 @@ COPY . /usr/src/app
 # for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done
 
 # Pull and Run
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
 
 # Pull and Run with local directory access
 # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t
@@ -37,16 +37,16 @@ COPY . /usr/src/app
 # sudo docker kill $(sudo docker ps -q)
 
 # Kill all image-based
-# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest)
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
 
 # Bash into running container
-# sudo docker container exec -it ba65811811ab bash
+# sudo docker exec -it 5a9b5863d93d bash
 
 # Bash into stopped container
-# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
+# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
 
 # Send weights to GCP
-# python -c "from utils.general import *; strip_optimizer('runs/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
+# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
 
 # Clean up
 # docker system prune -a --volumes

models/hand-detection/detect.py

 import argparse
-import os
-import platform
-import shutil
 import time
 from pathlib import Path
 
@@ -12,28 +9,31 @@ from numpy import random
 
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
-from utils.general import (
-    check_img_size, non_max_suppression, apply_classifier, scale_coords,
-    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
 def detect(save_img=False):
-    out, source, weights, view_img, save_txt, imgsz = \
-        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
-    webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
+    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
+    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+    # Directories
+    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
 
     # Initialize
     set_logging()
     device = select_device(opt.device)
-    if os.path.exists(out):
-        shutil.rmtree(out)  # delete output folder
-    os.makedirs(out)  # make new output folder
     half = device.type != 'cpu'  # half precision only supported on CUDA
 
     # Load model
     model = attempt_load(weights, map_location=device)  # load FP32 model
-    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
+    stride = int(model.stride.max())  # model stride
+    imgsz = check_img_size(imgsz, s=stride)  # check img_size
     if half:
         model.half()  # to FP16
 
@@ -41,27 +41,25 @@ def detect(save_img=False):
     classify = False
     if classify:
         modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
-        modelc.to(device).eval()
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
 
     # Set Dataloader
     vid_path, vid_writer = None, None
     if webcam:
-        view_img = True
+        view_img = check_imshow()
         cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
     else:
-        save_img = True
-        dataset = LoadImages(source, img_size=imgsz)
+        dataset = LoadImages(source, img_size=imgsz, stride=stride)
 
     # Get names and colors
     names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
 
     # Run inference
+    if device.type != 'cpu':
+        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
     t0 = time.time()
-    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
-    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
     for path, img, im0s, vid_cap in dataset:
         img = torch.from_numpy(img).to(device)
         img = img.half() if half else img.float()  # uint8 to fp16/32
@@ -84,83 +82,92 @@ def detect(save_img=False):
         # Process detections
         for i, det in enumerate(pred):  # detections per image
             if webcam:  # batch_size >= 1
-                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
+                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
             else:
-                p, s, im0 = path, '', im0s
+                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
 
-            save_path = str(Path(out) / Path(p).name)
-            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # img.jpg
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
             s += '%gx%g ' % img.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            if det is not None and len(det):
+            if len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
 
                 # Print results
                 for c in det[:, -1].unique():
                     n = (det[:, -1] == c).sum()  # detections per class
-                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
 
                 # Write results
                 for *xyxy, conf, cls in reversed(det):
                     if save_txt:  # Write to file
                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                         with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
                     if save_img or view_img:  # Add bbox to image
-                        label = '%s %.2f' % (names[int(cls)], conf)
+                        label = f'{names[int(cls)]} {conf:.2f}'
                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
 
             # Print time (inference + NMS)
-            print('%sDone. (%.3fs)' % (s, t2 - t1))
+            print(f'{s}Done. ({t2 - t1:.3f}s)')
 
             # Stream results
             if view_img:
-                cv2.imshow(p, im0)
-                if cv2.waitKey(1) == ord('q'):  # q to quit
-                    raise StopIteration
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
 
             # Save results (image with detections)
             if save_img:
-                if dataset.mode == 'images':
+                if dataset.mode == 'image':
                     cv2.imwrite(save_path, im0)
-                else:
+                else:  # 'video' or 'stream'
                     if vid_path != save_path:  # new video
                         vid_path = save_path
                         if isinstance(vid_writer, cv2.VideoWriter):
                             vid_writer.release()  # release previous video writer
-
-                        fourcc = 'mp4v'  # output video codec
-                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                            save_path += '.mp4'
+                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                     vid_writer.write(im0)
 
     if save_txt or save_img:
-        print('Results saved to %s' % Path(out))
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        print(f"Results saved to {save_dir}{s}")
 
-    print('Done. (%.3fs)' % (time.time() - t0))
+    print(f'Done. ({time.time() - t0:.3f}s)')
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
-    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('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
     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')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
     parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     opt = parser.parse_args()
     print(opt)
+    check_requirements(exclude=('pycocotools', 'thop'))
 
     with torch.no_grad():
         if opt.update:  # update all models (to fix SourceChangeWarning)

models/hand-detection/hubconf.py

-"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
+"""File for accessing YOLOv5 models via PyTorch Hub https://pytorch.org/hub/ultralytics_yolov5/
 
 Usage:
     import torch
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
 """
 
-dependencies = ['torch', 'yaml']
-import os
+from pathlib import Path
 
 import torch
 
-from models.common import NMS
 from models.yolo import Model
+from utils.general import check_requirements, set_logging
 from utils.google_utils import attempt_download
+from utils.torch_utils import select_device
+
+dependencies = ['torch', 'yaml']
+check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop'))
+set_logging()
 
 
-def create(name, pretrained, channels, classes):
+def create(name, pretrained, channels, classes, autoshape):
     """Creates a specified YOLOv5 model
 
     Arguments:
@@ -27,77 +31,107 @@ def create(name, pretrained, channels, classes):
     Returns:
         pytorch model
     """
-    config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name)  # model.yaml path
+    config = Path(__file__).parent / 'models' / f'{name}.yaml'  # model.yaml path
     try:
         model = Model(config, channels, classes)
         if pretrained:
-            ckpt = '%s.pt' % name  # checkpoint filename
-            attempt_download(ckpt)  # download if not found locally
-            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()
-        return model
+            fname = f'{name}.pt'  # checkpoint filename
+            attempt_download(fname)  # download if not found locally
+            ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
+            msd = model.state_dict()  # model state_dict
+            csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
+            csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
+            model.load_state_dict(csd, strict=False)  # load
+            if len(ckpt['model'].names) == classes:
+                model.names = ckpt['model'].names  # set class names attribute
+            if autoshape:
+                model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
+        device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
+        return model.to(device)
 
     except Exception as e:
         help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = 'Cache maybe be out of date, deleting cache and retrying may solve this. See %s for help.' % help_url
+        s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
         raise Exception(s) from e
 
 
-def yolov5s(pretrained=False, channels=3, classes=80):
-    """YOLOv5-small model from https://github.com/ultralytics/yolov5
+def custom(path_or_model='path/to/model.pt', autoshape=True):
+    """YOLOv5-custom model https://github.com/ultralytics/yolov5
 
-    Arguments:
-        pretrained (bool): load pretrained weights into the model, default=False
-        channels (int): number of input channels, default=3
-        classes (int): number of model classes, default=80
+    Arguments (3 options):
+        path_or_model (str): 'path/to/model.pt'
+        path_or_model (dict): torch.load('path/to/model.pt')
+        path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
 
     Returns:
         pytorch model
     """
-    return create('yolov5s', pretrained, channels, classes)
+    model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
+    if isinstance(model, dict):
+        model = model['ema' if model.get('ema') else 'model']  # load model
 
+    hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
+    hub_model.load_state_dict(model.float().state_dict())  # load state_dict
+    hub_model.names = model.names  # class names
+    if autoshape:
+        hub_model = hub_model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
+    device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
+    return hub_model.to(device)
 
-def yolov5m(pretrained=False, channels=3, classes=80):
-    """YOLOv5-medium model from https://github.com/ultralytics/yolov5
 
-    Arguments:
-        pretrained (bool): load pretrained weights into the model, default=False
-        channels (int): number of input channels, default=3
-        classes (int): number of model classes, default=80
+def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-small model https://github.com/ultralytics/yolov5
+    return create('yolov5s', pretrained, channels, classes, autoshape)
 
-    Returns:
-        pytorch model
-    """
-    return create('yolov5m', pretrained, channels, classes)
 
+def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-medium model https://github.com/ultralytics/yolov5
+    return create('yolov5m', pretrained, channels, classes, autoshape)
 
-def yolov5l(pretrained=False, channels=3, classes=80):
-    """YOLOv5-large model from https://github.com/ultralytics/yolov5
 
-    Arguments:
-        pretrained (bool): load pretrained weights into the model, default=False
-        channels (int): number of input channels, default=3
-        classes (int): number of model classes, default=80
+def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-large model https://github.com/ultralytics/yolov5
+    return create('yolov5l', pretrained, channels, classes, autoshape)
 
-    Returns:
-        pytorch model
-    """
-    return create('yolov5l', pretrained, channels, classes)
 
+def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
+    return create('yolov5x', pretrained, channels, classes, autoshape)
 
-def yolov5x(pretrained=False, channels=3, classes=80):
-    """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5
 
-    Arguments:
-        pretrained (bool): load pretrained weights into the model, default=False
-        channels (int): number of input channels, default=3
-        classes (int): number of model classes, default=80
+def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-small model https://github.com/ultralytics/yolov5
+    return create('yolov5s6', pretrained, channels, classes, autoshape)
 
-    Returns:
-        pytorch model
-    """
-    return create('yolov5x', pretrained, channels, classes)
+
+def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-medium model https://github.com/ultralytics/yolov5
+    return create('yolov5m6', pretrained, channels, classes, autoshape)
+
+
+def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-large model https://github.com/ultralytics/yolov5
+    return create('yolov5l6', pretrained, channels, classes, autoshape)
+
+
+def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True):
+    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
+    return create('yolov5x6', pretrained, channels, classes, autoshape)
+
+
+if __name__ == '__main__':
+    model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example
+    # model = custom(path_or_model='path/to/model.pt')  # custom example
+
+    # Verify inference
+    import numpy as np
+    from PIL import Image
+
+    imgs = [Image.open('data/images/bus.jpg'),  # PIL
+            'data/images/zidane.jpg',  # filename
+            'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg',  # URI
+            np.zeros((640, 480, 3))]  # numpy
+
+    results = model(imgs)  # batched inference
+    results.print()
+    results.save()

models/hand-detection/models/common.py

-# This file contains modules common to various models
+# YOLOv5 common modules
+
 import math
+from copy import copy
+from pathlib import Path
 
+import numpy as np
+import pandas as pd
+import requests
 import torch
 import torch.nn as nn
-from utils.general import non_max_suppression
+from PIL import Image
+from torch.cuda import amp
+
+from utils.datasets import letterbox
+from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
+from utils.plots import color_list, plot_one_box
+from utils.torch_utils import time_synchronized
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -24,7 +36,7 @@ class Conv(nn.Module):
         super(Conv, self).__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.Hardswish() if act else nn.Identity()
+        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
 
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
@@ -33,6 +45,52 @@ class Conv(nn.Module):
         return self.act(self.conv(x))
 
 
+class TransformerLayer(nn.Module):
+    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    def __init__(self, c, num_heads):
+        super().__init__()
+        self.q = nn.Linear(c, c, bias=False)
+        self.k = nn.Linear(c, c, bias=False)
+        self.v = nn.Linear(c, c, bias=False)
+        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+        self.fc1 = nn.Linear(c, c, bias=False)
+        self.fc2 = nn.Linear(c, c, bias=False)
+
+    def forward(self, x):
+        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+        x = self.fc2(self.fc1(x)) + x
+        return x
+
+
+class TransformerBlock(nn.Module):
+    # Vision Transformer https://arxiv.org/abs/2010.11929
+    def __init__(self, c1, c2, num_heads, num_layers):
+        super().__init__()
+        self.conv = None
+        if c1 != c2:
+            self.conv = Conv(c1, c2)
+        self.linear = nn.Linear(c2, c2)  # learnable position embedding
+        self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
+        self.c2 = c2
+
+    def forward(self, x):
+        if self.conv is not None:
+            x = self.conv(x)
+        b, _, w, h = x.shape
+        p = x.flatten(2)
+        p = p.unsqueeze(0)
+        p = p.transpose(0, 3)
+        p = p.squeeze(3)
+        e = self.linear(p)
+        x = p + e
+
+        x = self.tr(x)
+        x = x.unsqueeze(3)
+        x = x.transpose(0, 3)
+        x = x.reshape(b, self.c2, w, h)
+        return x
+
+
 class Bottleneck(nn.Module):
     # Standard bottleneck
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
@@ -65,6 +123,29 @@ class BottleneckCSP(nn.Module):
         return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
 
 
+class C3(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super(C3, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
+        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+
+class C3TR(C3):
+    # C3 module with TransformerBlock()
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = TransformerBlock(c_, c_, 4, n)
+
+
 class SPP(nn.Module):
     # Spatial pyramid pooling layer used in YOLOv3-SPP
     def __init__(self, c1, c2, k=(5, 9, 13)):
@@ -84,9 +165,39 @@ class Focus(nn.Module):
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
         super(Focus, self).__init__()