Update readme instructions

README.md

@@ -8,3 +8,52 @@ The COCO images were transformed to grayscale using PIL. The hyperparameters and
 
 Can be used as pretrained model for multispectral imaging as suggested in this [paper](https://ceur-ws.org/Vol-2771/AICS2020_paper_50.pdf).
 
+The file is given as a state_dict. Thus to initialize the model run:
+
+```
+# Load pretrained weights
+state_dict = torch.load(model_path, map_location=torch.device('cpu'))['model']
+# Load torchvision model
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT')
+# Adapt input convolution
+model.backbone.body.conv1 = torch.nn.Conv2d(1, 64,
+                            kernel_size=(7, 7), stride=(2, 2),
+                            padding=(3, 3), bias=False).requires_grad_(True)
+model.load_state_dict(state_dict)
+```
+
+If its going to be used for multispectral data, edit the first layer and duplicate the weights:
+
+```
+state_dict = torch.load(model_path, map_location=torch.device('cpu'))['model']
+
+# Duplicate the weights
+conv1_weight = state_dict['backbone.body.conv1.weight']
+conv1_type = conv1_weight.dtype
+conv1_weight = conv1_weight.float()
+repeat = int(math.ceil(in_chans / 3))
+conv1_weight = conv1_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
+#conv1_weight *= (3 / float(in_chans))
+conv1_weight = conv1_weight.to(conv1_type)
+state_dict['backbone.body.conv1.weight'] = conv1_weight
+
+model.backbone.body.conv1 = torch.nn.Conv2d(in_chans, 64,
+                                            kernel_size=(7, 7), stride=(2, 2),
+                                            padding=(3, 3), bias=False).requires_grad_(True)
+model.load_state_dict(state_dict)
+```
+
+For Faster-RCNN the input transform may need to be adapted. Here is an example:
+
+```
+coco_mean = [0.5] * in_chans
+coco_std = [0.25] * in_chans
+if in_chans > 3:
+    coco_mean[:3] = [0.485, 0.456, 0.406]
+    coco_std[:3] = [0.229, 0.224, 0.225]
+transform = torchvision.models.detection.transform.GeneralizedRCNNTransform(min_size=800,
+                                                                            max_size=1333,
+                                                                            image_mean=coco_mean,
+                                                                            image_std=coco_std)
+```
+