Deeplab new dataset not finding new class

Question

Hey I am attempting to add a new class to the pascal dataset to make it 21 detectable classes. I have followed these steps:

1. Segment Images. I picked chickens and made 4 separate chicken segementations. Here's an example:

2. place corresponding images in Voc2012 SegementationClass, SegmentationClassRaw, SegmentationObject, and JPEGImages

3. Add Annotation XML

4. Add new image to all txt files in "VOC2012\ImageSets\Main"

5. Add 3 new txt files in "VOC2012\ImageSets\Main": chicken_train.txt, chicken_trainval.txt, and chicken_val.txt. Add a 1 for the file name with chickens in it. Add 2 chicken files to VOC2012\ImageSets\Segmentation\train.txt and 2 others to trainval + val.

6. Create a new dataset in "data_generator.py":

   _EXTENDED_SEG_INFORMATION = DatasetDescriptor( splits_to_sizes={ 'train': 1466, 'train_aug': 10583, 'trainval': 2915, 'val': 1451, }, num_classes=22, ignore_label=255, )

7. create a new colormap for "extended" dataset:

   def create_extended_label_colormap(): return np.asarray([ [0, 0, 0], #background [128, 0, 0], #aeroplane [0, 128, 0], #bike [128, 128, 0], #bird [0, 0, 128], #boat [128, 0, 128], #bottle [0, 128, 128], #bus [128, 128, 128], #car [64, 0, 0], #cat [192, 0, 0], #chair [64, 128, 0], #cow [64, 0, 128], #diningtable [64, 0, 128], #dog [192, 0, 128], #horse [64, 128, 128], #motorbike [192, 128, 128], #person [0, 64, 0], #pottedplant [128, 64, 0], #sheep [0, 192, 0], #sofa [128, 192, 0], #train [0, 64, 128], #tv [128, 64, 128], #chicken ])

8. Generate TFRecord with command:

   py "research\deeplab\datasets\build_voc2012_data.py" --image_folder="D:\DeepLab\models-master\research\deeplab\datasets\pascal_voc_seg\VOCdevkit\VOC2012\JPEGImages" --semantic_segmentation_folder="research\deeplab\datasets\pascal_voc_seg\VOCdevkit\VOC2012\SegmentationClassRaw" --list_folder="research\deeplab\datasets\pascal_voc_seg\VOCdevkit\VOC2012\ImageSets\Segmentation" --image_format="jpg" --output_dir="research\deeplab\datasets\extended\tfrecord"

9. Artificially, set weights towards new chicken class to get it to show up. Train with command:

   python "D:\DeepLab\models-master\research\deeplab\train.py" --logtostderr --train_split="trainval" --model_variant="xception_65" --atrous_rates=6 --atrous_rates=12 --atrous_rates=18 --output_stride=16 --decoder_output_stride=4 --train_crop_size="513,513" --train_batch_size=2 --dataset="extended" --training_number_of_steps=1000 --fine_tune_batch_norm=true --tf_initial_checkpoint="D:\DeepLab\models-master\research\deeplab\datasets\pascal_voc_seg\init_models\deeplabv3_pascal_train_aug\model.ckpt" --train_logdir="D:\DeepLab\models-master\research\deeplab\datasets\extended\exp\train_on_trainval_set\train" --dataset_dir="D:\DeepLab\models-master\research\deeplab\datasets\extended\tfrecord" --label_weights=.001 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=.01 --label_weights=100

10. exporting the model and testing it give me this which isn't great:

11. eval command on the image also produces poor results:

    eval/miou_1.0_class_1[0.00715755112] eval/miou_1.0_class_16[0.00142123573]eval/miou_1.0_class_17[0.000168795494] eval/miou_1.0_class_12[0] eval/miou_1.0_class_14[0] eval/miou_1.0_class_0[0.126532018] eval/miou_1.0_class_5[0] eval/miou_1.0_class_13[0.0135749253] eval/miou_1.0_class_15[0.0179808438] eval/miou_1.0_class_3[0.000226198768] eval/miou_1.0_class_8[0.229160205]

    eval/miou_1.0_class_2[8.26003941e-07] eval/miou_1.0_class_7[0.000108588596] eval/miou_1.0_class_4[0] eval/miou_1.0_class_19[0] eval/miou_1.0_class_18[0.00898866542] eval/miou_1.0_class_6[4.23762758e-05] eval/miou_1.0_overall[0.0191962905] eval/miou_1.0_class_21[0] eval/miou_1.0_class_10[0.00145707454] eval/miou_1.0_class_9[4.08720407e-05] eval/miou_1.0_class_20[0.00886027142] eval/miou_1.0_class_11[0.00659790076]

I have tried training 10,000 iterations to see if that fixes it and it does populate every other class but chicken class (class_21) still stays at 0 miou. What am I doing wrong here?!?

ADDITIONAL NOTES: I do have everything working correctly if I stick to the base pascal classes I can get a pretty good model at like .81 miou overall. Issues only show up when I try to introduce the new class.

I am unsure if i need to do another to substitute out "pascal_label_map" in "research\object_detection\data" or if that is unneeded.