README updated

README.md

@@ -4,17 +4,29 @@
 
 ## Training
 
-Loss
+<!-- Loss
+
+heads = 10 all = 40 
 
 ![loss](./assets/loss_40.png)
 
+heads = 40 all = 100
+
 ![loss](./assets/loss2.png)
 
 Validation loss
 
 ![val_loss](./assets/val_loss_40.png)
 
-![val_loss](./assets/val_loss2.png)
+![val_loss](./assets/val_loss2.png) -->
+
+
+| heads Epoch | all Epoch | loss | val_loss |
+|:-----------:|:---------:|:----:|:--------:|
+| 10 		  | 40		  | ![loss](./assets/loss_40.png) | ![val_loss](./assets/val_loss_40.png) |
+| 40 		  | 100		  | ![loss](./assets/loss2.png) | ![val_loss](./assets/val_loss2.png) |
+
+
 
 ## Reference
 https://github.com/matterport/Mask_RCNN

inference.py

@@ -35,12 +35,12 @@ class ShapesConfig(Config):
     to the toy shapes dataset.
     """
     # Give the configuration a recognizable name
-    NAME = "shapes"
+    NAME = "lyft_perception_challenge"
 
     # Train on 1 GPU and 8 images per GPU. We can put multiple images on each
     # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
     GPU_COUNT = 1
-    IMAGES_PER_GPU = 1
+    IMAGES_PER_GPU = 4
 
     # Number of classes (including background)
     NUM_CLASSES = 1 + 2  # background + 3 shapes
@@ -80,19 +80,14 @@ class InferenceConfig(ShapesConfig):
                           config=inference_config,
                           model_dir=MODEL_DIR)
 
-# Get path to saved weights
-# Either set a specific path or find last trained weights
-# model_path = os.path.join('./logs/shapes20180529T0826', "mask_rcnn_shapes_0040.h5")
 model_path = os.path.join('./', "mask_rcnn_lyft.h5")
-# model_path = model.find_last()[1]
 
 # Load trained weights (fill in path to trained weights here)
 assert model_path != "", "Provide path to trained weights"
 print("Loading weights from ", model_path)
 model.load_weights(model_path, by_name=True)
 
 
-# # Test on a random image
 
 RED = (255,0,0)
 GREEN = (0,255,0)
@@ -116,9 +111,7 @@ def segment_images(original_image):
             color_id=0
         else:
             color_id=1
-        # print('id:',_id)
         mask_1 = f_mask[:,:,ch]
-        # print(mask_1)
         mask1 = np.dstack([mask_1*colors[color_id][0],
                             mask_1*colors[color_id][1],
                             mask_1*colors[color_id][2]])