Update default parameters

README.md

@@ -29,10 +29,10 @@ python -m utils.updated_checkpoint [--checkpoint_path ./yolov5s.pt]
 
 ### 🔥 Loading via `torch.hub`
 
-The models are also available via torch hub, to load `yolov5` with pretrained weights simply do:
+The models are also available via torch hub, to load `yolov5s` with pretrained weights simply do:
 
 ```python
-model = torch.hub.load('zhiqwang/yolov5-rt-stack', 'yolov5', pretrained=True)
+model = torch.hub.load('zhiqwang/yolov5-rt-stack', 'yolov5s', pretrained=True)
 ```
 
 ### ✨ Inference on `PyTorch` backend
@@ -52,8 +52,7 @@ conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
 To read a source image and detect its objects run:
 
 ```bash
-python -m detect [--model_cfg yolov5s.yaml]
-                 [--input_source YOUR_IMAGE_SOURCE_DIR]
+python -m detect [--input_source YOUR_IMAGE_SOURCE_DIR]
                  [--labelmap ./notebooks/assets/coco.names]
                  [--output_dir ./data-bin/output]
                  [--min_size 640]

detect.py

@@ -10,7 +10,7 @@
 from torchvision.ops import box_convert
 
 from utils.image_utils import plot_one_box
-from hubconf import yolov5
+from hubconf import yolov5s
 
 
 def load_names(category_path):
@@ -86,7 +86,7 @@ def main(args):
     print(args)
     device = torch.device("cuda") if torch.cuda.is_available() and args.gpu else torch.device("cpu")
 
-    model = yolov5(
+    model = yolov5s(
         pretrained=True,
         min_size=args.min_size,
         max_size=args.max_size,
@@ -132,8 +132,6 @@ def main(args):
     import argparse
     parser = argparse.ArgumentParser(description=__doc__)
 
-    parser.add_argument('--model_cfg', type=str, default='yolov5s.yaml',
-                        help='path where the model cfg in')
     parser.add_argument('--labelmap', type=str, default='./notebooks/assets/coco.names',
                         help='path where the coco category in')
     parser.add_argument('--input_source', type=str, default='./notebooks/assets/zidane.jpg',

hubconf.py

@@ -1,5 +1,4 @@
 # Optional list of dependencies required by the package
 dependencies = ['yaml', 'torch', 'torchvision']
 
-from models import yolov5, yolov5s, yolov5m, yolov5l
-from models import yolov5_onnx
+from models import yolov5s, yolov5m, yolov5l

notebooks/export-onnx-inference-onnxruntime.ipynb

@@ -77,8 +77,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path0 = 'notebooks/assets/bus.jpg'\n",
-    "path1 = 'notebooks/assets/zidane.jpg'\n",
+    "path0 = './notebooks/assets/bus.jpg'\n",
+    "path1 = './notebooks/assets/zidane.jpg'\n",
     "\n",
     "img_test0 = read_image(path0, is_half=False)\n",
     "img_test0 = img_test0.to(device)\n",
@@ -350,4 +350,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

notebooks/inference-pytorch-export-libtorch.ipynb

@@ -17,7 +17,7 @@
     "import torch\n",
     "from torch import nn\n",
     "\n",
-    "from hubconf import yolov5\n",
+    "from hubconf import yolov5s\n",
     "\n",
     "from detect import read_image, load_names, overlay_boxes\n",
     "from utils.image_utils import cv2_imshow"
@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = yolov5(pretrained=True, min_size=320, max_size=416, score_thresh=0.5)"
+    "model = yolov5s(pretrained=True, min_size=320, max_size=416, score_thresh=0.5)"
    ]
   },
   {

notebooks/visualize-jit-models.ipynb

@@ -10,7 +10,7 @@
     "import torchvision\n",
     "\n",
     "from utils.graph_utils import make_graph\n",
-    "from hubconf import yolov5"
+    "from hubconf import yolov5s"
    ]
   },
   {
@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = yolov5(pretrained=True)\n",
+    "model = yolov5s(pretrained=True)\n",
     "model = model.eval()"
    ]
   },
@@ -88,7 +88,7 @@
    ],
    "source": [
     "d = make_graph(tm.m, classes_to_visit={})\n",
-    "d.render('yolov5.highlevel')"
+    "d.render('./data-bin/output/yolov5.highlevel')"
    ]
   },
   {
@@ -247,7 +247,7 @@
    ],
    "source": [
     "d = make_graph(tm.m, classes_to_visit={'YoloHead'})\n",
-    "d.render(\"yolov5.detail\")"
+    "d.render(\"./data-bin/output/yolov5.detail\")"
    ]
   },
   {
@@ -476,13 +476,6 @@
    "source": [
     "d"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -506,4 +499,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

requirements.txt

@@ -1,7 +1,6 @@
 Cython
 matplotlib>=3.2.2
 numpy>=1.18.5
-opencv-python>=4.1.2
 pillow
 PyYAML>=5.3
 scipy>=1.4.1