Unexpected output from inference mode.

[hasoweh]

I am working with remote sensing imagery and am getting very strange outputs from the model in ference mode. I am using weights from the crowdai building segmentation challenge which I fine-tuned using my own data. When I loaded those weights into the model and tried to run an inference on a few images I got the following:

My classification only uses 2 classes (background and one target class). Any idea why I am getting this image showing so many other classes?

Here is the code I use to run the inference. The weights I used were not that great (val_loss of 2.96) but this was more just to see how the network was performing before I try and fine-tune network hyperparameters.

# inference code
d_cnn = HedgeCNN(mode=inference, config=InfHedgeConfig(), model_dir=/mnt/dataDL/ahls_st/Data/Data3/Scripts/logs/)
d_cnn.load_weights(os.path.join(root, weights.03-2.96.hdf5), by_name=True)

output_dir = os.path.join(root, output_data)
dataset_dir = root
subset = testing

detect(d_cnn, dataset_dir, subset, output_dir)

And here is my config file:

# 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 = 2

# Number of classes (including background)
NUM_CLASSES = 1 + 1  # background + hedge

# Use small images for faster training. Set the limits of the small side
# the large side, and that determines the image shape.
IMAGE_MIN_DIM = 320
IMAGE_MAX_DIM = 320

# Use smaller anchors because our image and objects are small
RPN_ANCHOR_SCALES = (4, 8, 16, 28, 40)  # anchor side in pixels

# Num of training images / batch size (I add a few more steps because data augmentation creates a few more images?)
STEPS_PER_EPOCH = 70

# Num of valid. images / batch size
VALIDATION_STEPS = 30

#can play with this to see what gives best accuracy
DETECTION_MIN_CONFIDENCE = 0.8

#todo
RPN_ANCHOR_RATIOS = [0.33, 1, 14]

#True is good when using high resolution images. Planet isnt that high?
USE_MINI_MASK = False

#mean pixel values for each band.
MEAN_PIXEL = np.array([241, 511, 477])

MAX_GT_INSTANCES = 50

DETECTION_MAX_INSTANCES = 50

#keep a positive:negative
# ratio of 1:3. You can increase the number of proposals by adjusting
# the RPN NMS threshold.
TRAIN_ROIS_PER_IMAGE = 400

LEARNING_RATE = 0.0001
LEARNING_MOMENTUM = 0.8

# Weight decay regularization
WEIGHT_DECAY = 0.0001`

I made a few other changes due to having troubles with NaN losses. Thus, I have changed the optimizer to adam, and added some epsilons to the rpn losses. If anyone is interested in what I mean about this I can find the code I changed, but I think this isn't related to my current issue.

Any help would be great!!!!

Thanks!