Sync train and test iou_thresh (#1465)

README.md

@@ -14,7 +14,6 @@ This repository represents Ultralytics open-source research into future object d
 - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
 - **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
 - **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
-- **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
 
 
 ## Pretrained Checkpoints
@@ -30,15 +29,15 @@ This repository represents Ultralytics open-source research into future object d
 | | | | | | || |
 | [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases) | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
 
-** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.  
-** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.001`  
-** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img.  **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.1`  
+** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.  
+** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
+** Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img.  **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
 ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). 
-** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce** by `python test.py --data coco.yaml --img 832 --augment` 
+** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` 
 
 ## Requirements
 
-Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.6`. To install run:
+Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
 ```bash
 $ pip install -r requirements.txt
 ```

test.py

@@ -21,7 +21,7 @@ from utils.torch_utils import select_device, time_synchronized
 
 def test(data,
          weights=None,
-         batch_size=16,
+         batch_size=32,
          imgsz=640,
          conf_thres=0.001,
          iou_thres=0.6,  # for NMS
@@ -279,7 +279,7 @@ if __name__ == '__main__':
     parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
+    parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
     parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')

tutorial.ipynb

@@ -727,7 +727,7 @@
       },
       "source": [
         "# Run YOLOv5x on COCO val2017\n",
-        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640"
+        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65"
       ],
       "execution_count": null,
       "outputs": [

utils/google_utils.py

@@ -18,7 +18,7 @@ def gsutil_getsize(url=''):
 def attempt_download(weights):
     # Attempt to download pretrained weights if not found locally
     weights = weights.strip().replace("'", '')
-    file = Path(weights).name
+    file = Path(weights).name.lower()
 
     msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/'
     models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']  # available models