diff --git a/ImageClassification/CIFAR10/All_CNN/readme.md b/ImageClassification/CIFAR10/All_CNN/readme.md
index 246acee..002baec 100644
--- a/ImageClassification/CIFAR10/All_CNN/readme.md
+++ b/ImageClassification/CIFAR10/All_CNN/readme.md
@@ -13,7 +13,7 @@ The trained weights file can be downloaded from AWS
 
 
 ### neon version
-The model weight file above has been generated using neon version tag [v1.4.0]((https://github.com/NervanaSystems/neon/releases/tag/v1.4.0).
+The model weight file above has been generated using neon version tag [v2.3.0]((https://github.com/NervanaSystems/neon/releases/tag/v2.3.0).
 It may not work with other versions.
 
 ### Performance
diff --git a/ImageClassification/CIFAR10/All_CNN/test.sh b/ImageClassification/CIFAR10/All_CNN/test.sh
index af3c27f..a6dc2f8 100755
--- a/ImageClassification/CIFAR10/All_CNN/test.sh
+++ b/ImageClassification/CIFAR10/All_CNN/test.sh
@@ -24,7 +24,7 @@ echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
 python -u $TEST_SCRIPT -i ${EXECUTOR_NUMBER} -vvv \
-           --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+           --model_file $WEIGHTS_FILE --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/CIFAR10/DeepResNet/readme.md b/ImageClassification/CIFAR10/DeepResNet/readme.md
index a7a2216..dcc80ff 100644
--- a/ImageClassification/CIFAR10/DeepResNet/readme.md
+++ b/ImageClassification/CIFAR10/DeepResNet/readme.md
@@ -24,10 +24,10 @@ Training this model with the options described below should be able to achieve a
 accuracy using only mean subtraction, random cropping, and random flips.
 
 ## Instructions
-This script was tested with [neon version 1.5.0](https://github.com/NervanaSystems/neon/tree/v1.5.0).
+This script was tested with [neon version 2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).
 Make sure that your local repo is synced to this commit and run the [installation
 procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
-Commit SHA for v1.5.0 is  `8a5b2c45784499cd3aba3c322ea10b3661c2a2a9`
+Commit SHA for v2.2.0 is  `5843e7116d880dfc59c8fb558beb58dd2ef421d0`
 
 This example uses the `DataLoader` module to load the images for consumption while applying random
 cropping, flipping, and shuffling.  To use the DataLoader, the script will generate PNG files from
diff --git a/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py b/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
index 133a651..91adec8 100644
--- a/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
+++ b/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ----------------------------------------------------------------------------
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.initializers import Kaiming, IdentityInit
 from neon.layers import Conv, Pooling, GeneralizedCost, Affine, Activation
@@ -20,63 +21,59 @@
 from neon.optimizers import GradientDescentMomentum, Schedule
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
 from neon.models import Model
-from neon.data import ImageLoader, ImageParams, DataLoader
 from neon.callbacks.callbacks import Callbacks
 
 import os
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=10)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def config(manifest_filename, manifest_root, batch_size, subset_pct):
+    image_config = {"type": "image",
+                    "height": 32,
+                    "width": 32}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+
+def make_train_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    train_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    train_config['augmentation'][0]['center'] = False
+    train_config['augmentation'][0]['flip_enable'] = True
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+
+def make_val_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    val_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 parser.add_argument('--depth', type=int, default=9,
                     help='depth of each stage (network depth will be 6n+2)')
 args = parser.parse_args()
 
-
-def extract_images(out_dir, padded_size):
-    '''
-    Save CIFAR-10 dataset as PNG files
-    '''
-    import numpy as np
-    from neon.data import load_cifar10
-    from PIL import Image
-    dataset = dict()
-    dataset['train'], dataset['val'], _ = load_cifar10(out_dir, normalize=False)
-    pad_size = (padded_size - 32) // 2 if padded_size > 32 else 0
-    pad_width = ((0, 0), (pad_size, pad_size), (pad_size, pad_size))
-
-    for setn in ('train', 'val'):
-        data, labels = dataset[setn]
-
-        img_dir = os.path.join(out_dir, setn)
-        ulabels = np.unique(labels)
-        for ulabel in ulabels:
-            subdir = os.path.join(img_dir, str(ulabel))
-            if not os.path.exists(subdir):
-                os.makedirs(subdir)
-
-        for idx in range(data.shape[0]):
-            im = np.pad(data[idx].reshape((3, 32, 32)), pad_width, mode='mean')
-            im = np.uint8(np.transpose(im, axes=[1, 2, 0]).copy())
-            im = Image.fromarray(im)
-            path = os.path.join(img_dir, str(labels[idx][0]), str(idx) + '.png')
-            im.save(path, format='PNG')
-
 # setup data provider
-train_dir = os.path.join(args.data_dir, 'train')
-test_dir = os.path.join(args.data_dir, 'val')
-if not (os.path.exists(train_dir) and os.path.exists(test_dir)):
-    extract_images(args.data_dir, 40)
-
-# setup data provider
-shape = dict(channel_count=3, height=32, width=32)
-train_params = ImageParams(center=False, flip=True, **shape)
-test_params = ImageParams(**shape)
-common = dict(target_size=1, nclasses=10)
-
-train = DataLoader(set_name='train', repo_dir=train_dir, media_params=train_params,
-                   shuffle=True, **common)
-test = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common)
-
+train = make_train_config(args.manifest['train'], args.manifest_root, args.batch_size)
+test = make_val_config(args.manifest['val'], args.manifest_root, args.batch_size)
 
 def conv_params(fsize, nfm, stride=1, relu=True):
     return dict(fshape=(fsize, fsize, nfm), strides=stride, padding=(1 if fsize > 1 else 0),
diff --git a/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py b/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
index 8528515..9c1687c 100644
--- a/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
+++ b/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
@@ -15,24 +15,50 @@
 # ----------------------------------------------------------------------------
 import os
 
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.util.persist import load_obj
 from neon.transforms import Misclassification, CrossEntropyMulti
 from neon.optimizers import GradientDescentMomentum
 from neon.layers import GeneralizedCost
 from neon.models import Model
-from neon.data import DataLoader, ImageParams
+
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
 
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 args = parser.parse_args()
 
-# setup data provider
-test_dir = os.path.join(args.data_dir, 'val')
-shape = dict(channel_count=3, height=32, width=32)
-test_params = ImageParams(center=True, flip=False, **shape)
-common = dict(target_size=1, nclasses=10)
-test_set = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common)
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=10)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def config(manifest_filename, manifest_root, batch_size, subset_pct):
+    image_config = {"type": "image",
+                    "height": 32,
+                    "width": 32}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "crop_enable": True,
+                    "center": True,
+                    "flip_enable": False}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_val_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    val_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
+test_set = make_val_config(args.manifest["val"], args.manifest_root, batch_size=args.batch_size)
 
 model = Model(load_obj(args.model_file))
 cost = GeneralizedCost(costfunc=CrossEntropyMulti())
diff --git a/ImageClassification/CIFAR10/DeepResNet/test.sh b/ImageClassification/CIFAR10/DeepResNet/test.sh
index 8f0591c..48a78ac 100755
--- a/ImageClassification/CIFAR10/DeepResNet/test.sh
+++ b/ImageClassification/CIFAR10/DeepResNet/test.sh
@@ -23,14 +23,14 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u $TEST_SCRIPT  -i ${EXECUTOR_NUMBER} -vvv --model_file $WEIGHTS_FILE --no_progress_bar -w /usr/local/data/CIFAR10/macrobatches | tee output.dat 2>&1
+python -u $TEST_SCRIPT  -i ${EXECUTOR_NUMBER} -vvv --model_file $WEIGHTS_FILE --manifest val:/data/CIFAR/val-index.csv --manifest_root /data/CIFAR -b gpu -z 32 --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
 fi
 
 # get the top-1 misclass
-top1=`tail -n 1 output.dat | sed "s/.*Accuracy: //" | sed "s/ \% (Top-1).*//"`
+top1=`cat output.dat | sed -n "s/.*Accuracy: \(.*\) \% (Top-1).*/\1/p"`
 
 top1pass=0
 top1pass=`echo $top1'>'85 | bc -l`
diff --git a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
index 057c951..e77b30a 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
+++ b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
@@ -27,16 +27,52 @@
     alexnet_neon.py -w <path-to-saved-batches> --test_only \
             --model_file <saved weights file>
 """
-
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.initializers import Constant, Gaussian
 from neon.layers import Conv, Dropout, Pooling, GeneralizedCost, Affine, LRN
 from neon.optimizers import GradientDescentMomentum, MultiOptimizer, Schedule
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
 from neon.callbacks.callbacks import Callbacks
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size):
+#    cache_root = get_data_cache_or_nothing('i1k-cache/')
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": [0.875, 0.875],
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_train_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    train_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 parser.add_argument('--subset_pct', type=float, default=100,
@@ -49,14 +85,8 @@
     if args.model_file is None:
         raise ValueError('To test model, trained weights need to be provided')
 
-# setup data provider
-img_set_options = dict(repo_dir=args.data_dir,
-                       inner_size=224,
-                       subset_pct=args.subset_pct)
-train = ImageLoader(set_name='train', scale_range=(256, 256),
-                    shuffle=True, **img_set_options)
-test = ImageLoader(set_name='validation', scale_range=(256, 256),
-                   do_transforms=False, **img_set_options)
+train = make_train_config(args.subset_pct, args.manifest["train"], args.manifest_root, batch_size=args.batch_size)
+val = make_val_config(args.subset_pct, args.manifest["val"], args.manifest_root, batch_size=args.batch_size)
 
 init_g1 = Gaussian(scale=0.01)
 init_g2 = Gaussian(scale=0.005)
@@ -105,15 +135,15 @@
 
 # configure callbacks
 valmetric = TopKMisclassification(k=5)
-callbacks = Callbacks(model, eval_set=test, metric=valmetric, **args.callback_args)
+callbacks = Callbacks(model, eval_set=val, metric=valmetric, **args.callback_args)
 
 if args.model_file is not None:
-    model.load_params(args.model_file)
+    model.load_params(args.model_file, load_states=False)
 if not args.test_only:
     cost = GeneralizedCost(costfunc=CrossEntropyMulti())
     model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
 
-mets = model.eval(test, metric=valmetric)
+mets = model.eval(val, metric=valmetric)
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                      (1.0-mets[1])*100,
diff --git a/ImageClassification/ILSVRC2012/Alexnet/readme.md b/ImageClassification/ILSVRC2012/Alexnet/readme.md
index 10c3233..7175be7 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/readme.md
+++ b/ImageClassification/ILSVRC2012/Alexnet/readme.md
@@ -16,7 +16,7 @@ The model run script is included below [alexnet_neon.py](./alexnet_neon.py).
 ### Trained weights
 The trained weights file can be downloaded from AWS using the following link:
 [trained Alexnet model weights][S3_WEIGHTS_FILE]
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/alexnet.p
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/alexnet_fused_conv_bias.p
 
 ### Performance
 This model is acheiving 58.6% top-1 and 81.1% top-5 accuracy on the validation
@@ -33,8 +33,7 @@ Note there has been some changes to the format of the mean data subtraction;
 users with the old format may be prompted to run an update script before proceeding.
 
 
-This script was tested with the [neon release v1.4.0](https://github.com/NervanaSystems/neon/tree/v1.4.0)
-(commit SHA bc196cb).
+This script was tested with the [neon release v2.3.0](https://github.com/NervanaSystems/neon/tree/v2.3.0).
 Make sure that your local repo is synced to this commit and run the
 [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html)
 before proceeding.
diff --git a/ImageClassification/ILSVRC2012/Alexnet/test.sh b/ImageClassification/ILSVRC2012/Alexnet/test.sh
index e81fb41..f8113b7 100755
--- a/ImageClassification/ILSVRC2012/Alexnet/test.sh
+++ b/ImageClassification/ILSVRC2012/Alexnet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted/ --manifest_root /data/i1k-extracted --manifest train:/data/i1k-extracted/train-index.csv --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 256 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
index fe815de..ee75662 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
+++ b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
@@ -18,6 +18,7 @@
 """
 
 import os
+import numpy as np
 
 from neon.util.argparser import NeonArgparser
 from neon.layers import Conv, Pooling, MergeBroadcast, BranchNode
@@ -28,7 +29,35 @@
 from neon.optimizers import GradientDescentMomentum, MultiOptimizer
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
+
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": [0.875, 0.875]}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    return wrap_dataloader(AeonDataLoader(val_config))
 
 parser = NeonArgparser(__doc__)
 parser.add_argument('--subset_pct', type=float, default=100,
@@ -38,10 +67,7 @@
 args = parser.parse_args()
 
 # setup data provider
-img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
-                       subset_pct=args.subset_pct)
-test = ImageLoader(set_name='validation', scale_range=(256, 256),
-                   do_transforms=False, **img_set_options)
+val = make_val_config(args.subset_pct, args.manifest['val'], args.manifest_root, args.batch_size)
 
 init1 = Xavier(local=False)
 initx = Xavier(local=True)
@@ -122,16 +148,16 @@ def aux_branch(bnode):
                  weights=[1, 0., 0.])  # We only want to consider the CE of the main path
 
 assert os.path.exists(args.model_file), 'script requires the trained weights file'
-model.load_params(args.model_file)
-model.initialize(test, cost)
+model.load_params(args.model_file, load_states=False)
+model.initialize(val, cost)
 
 
 print 'running speed benchmark...'
-model.benchmark(test, cost, opt)
+model.benchmark(val, cost, opt)
 
 print '\nCalculating performance on validation set...'
-test.reset()
-mets = model.eval(test, metric=valmetric)
+val.reset()
+mets = model.eval(val, metric=valmetric)
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                      (1.0-mets[1])*100,
diff --git a/ImageClassification/ILSVRC2012/Googlenet/readme.md b/ImageClassification/ILSVRC2012/Googlenet/readme.md
index 66e30cd..62665c6 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/readme.md
+++ b/ImageClassification/ILSVRC2012/Googlenet/readme.md
@@ -14,7 +14,7 @@ The model run script is included here [googlenet_neon.py](./googlenet_neon.py).
 The trained weights file can be downloaded from AWS using the following link:
 [trained googlenet model weights][S3_WEIGHTS_FILE].
 
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/googlenet/googlenet.p
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/googlenet/googlenet_fused_conv_bias.p
 
 ### Performance
 This model is acheiving 64% top-1 and 85.5% top-5 accuracy on the validation data set.
@@ -26,7 +26,7 @@ During training, the images were randomly cropped and flipped horizontally but s
 To run the model, first the ImageNet data set needs to be uploaded and converted to the format compatible with neon (see  [instructions](http://neon.nervanasys.com/docs/latest/datasets.html#imagenet)).  Note there has been some changes to the format of the mean data subtraction; users with the old format may be prompted to run an update script before proceeding.
 
 
-This script works with the [neon commit SHA 66846b409](https://github.com/NervanaSystems/neon/commit/66846b4097d256e176cd76559dfa4e0bc54ab6dc).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
+This script works with the [neon release v2.3.0](https://github.com/NervanaSystems/neon/tree/v2.3.0).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
 
 
 If neon is installed into a `virtualenv`, make sure that it is activated before running the commands below.  Also, the commands below use the GPU backend by default so add `-b cpu` if you are running on a system without a compatible GPU.
diff --git a/ImageClassification/ILSVRC2012/Googlenet/test.sh b/ImageClassification/ILSVRC2012/Googlenet/test.sh
index 530a750..fbca2ed 100755
--- a/ImageClassification/ILSVRC2012/Googlenet/test.sh
+++ b/ImageClassification/ILSVRC2012/Googlenet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/ILSVRC2012/VGG/readme.md b/ImageClassification/ILSVRC2012/VGG/readme.md
index 1c69957..5b54a2d 100644
--- a/ImageClassification/ILSVRC2012/VGG/readme.md
+++ b/ImageClassification/ILSVRC2012/VGG/readme.md
@@ -7,8 +7,8 @@ The model run script is included here [vgg_neon.py](./vgg_neon.py).  This script
 
 ### Trained weights
 The trained weights file can be downloaded from AWS using the following links:
-[VGG_D.p]( https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_D.p) and [VGG_E.p][S3_WEIGHTS_FILE].
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_E.p
+[VGG_D.p]( https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_D_fused_conv_bias.p) and [VGG_E.p][S3_WEIGHTS_FILE].
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_E_fused_conv_bias.p
 
 ## Performance
 
@@ -95,7 +95,7 @@ The batch size is set to 64 in the examples above because with larger batch size
 
 ### Version compatibility
 
-Neon version: commit SHA [e7ab2c2e2](https://github.com/NervanaSystems/neon/commit/e7ab2c2e27f113a4d36d17ba8c79546faed7d916).
+Neon version: v2.3.0 (https://github.com/NervanaSystems/neon/tree/v2.3.0).
 
 ## Citation
 
diff --git a/ImageClassification/ILSVRC2012/VGG/test.sh b/ImageClassification/ILSVRC2012/VGG/test.sh
index 3778627..46f3c0b 100755
--- a/ImageClassification/ILSVRC2012/VGG/test.sh
+++ b/ImageClassification/ILSVRC2012/VGG/test.sh
@@ -22,7 +22,8 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 64 --vgg_version E | tee output.dat 2>&1
+python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 64 --vgg_version E 2>&1 | tee output.dat
+
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
index 6faf18f..94a6bfd 100644
--- a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
+++ b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
@@ -20,7 +20,7 @@
     K. Simonyan, A. Zisserman
     arXiv:1409.1556
 """
-
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.backends import gen_backend
 from neon.initializers import Constant, GlorotUniform, Xavier
@@ -28,9 +28,44 @@
 from neon.optimizers import GradientDescentMomentum, Schedule, MultiOptimizer
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
 from neon.callbacks.callbacks import Callbacks
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": scale,
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_train_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    train_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale)
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments
 parser = NeonArgparser(__doc__)
 parser.add_argument('--vgg_version', default='D', choices=['D', 'E'],
@@ -41,12 +76,11 @@
                     help='skip fitting - evaluate metrics on trained model weights')
 args = parser.parse_args()
 
-img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
-                       subset_pct=args.subset_pct)
-train = ImageLoader(set_name='train', scale_range=(256, 384),
-                    shuffle=True, **img_set_options)
-test = ImageLoader(set_name='validation', scale_range=(256, 256), do_transforms=False,
-                   shuffle=False, **img_set_options)
+# Training set has different scale configuration (256, 384) than testing set (256, 256). Why?
+# Commenting it out gives the folowing runtime error:
+# RuntimeError: Unable to create internal loader object: value for 'scale' out of range
+#train = make_train_config(args.subset_pct, args.manifest["train"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.583])
+val = make_val_config(args.subset_pct, args.manifest["val"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.875])
 
 init1 = Xavier(local=True)
 initfc = GlorotUniform()
@@ -83,10 +117,10 @@
 
 # configure callbacks
 top5 = TopKMisclassification(k=5)
-callbacks = Callbacks(model, eval_set=test, metric=top5, **args.callback_args)
+callbacks = Callbacks(model, eval_set=val, metric=top5, **args.callback_args)
 
-model.load_params(args.model_file)
-mets=model.eval(test, metric=TopKMisclassification(k=5))
+model.load_params(args.model_file, load_states=False)
+mets=model.eval(val, metric=TopKMisclassification(k=5))
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                     (1.0-mets[1])*100,