Merge branch 'master' into feature/DDP_fixed

README.md

@@ -41,7 +41,7 @@ $ pip install -U -r requirements.txt
 ## Tutorials
 
 * [Notebook](https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-* [Kaggle](https://www.kaggle.com/ultralytics/yolov5-tutorial)
+* [Kaggle](https://www.kaggle.com/ultralytics/yolov5)
 * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
 * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
 * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)

requirements.txt

@@ -1,6 +1,6 @@
 # pip install -U -r requirements.txt
 Cython
-numpy==1.17
+numpy==1.17.3
 opencv-python
 torch>=1.5.1
 matplotlib

train.py

@@ -208,15 +208,15 @@ def train(hyp, tb_writer, opt, device):
     model.names = names
 
     # Class frequency
+    labels = np.concatenate(dataset.labels, 0)
+    c = torch.tensor(labels[:, 0])  # classes
+    # cf = torch.bincount(c.long(), minlength=nc) + 1.
+    # model._initialize_biases(cf.to(device))
+    plot_labels(labels, save_dir=log_dir)
     if tb_writer:
-        labels = np.concatenate(dataset.labels, 0)
-        c = torch.tensor(labels[:, 0])  # classes
-        # cf = torch.bincount(c.long(), minlength=nc) + 1.
-        # model._initialize_biases(cf.to(device))
-        plot_labels(labels)
+        tb_writer.add_hparams(hyp, {})
         tb_writer.add_histogram('classes', c, 0)
 
-
     # Check anchors
     if not opt.noautoanchor:
         check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)