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readme.md

MobileNetV2 Quantized

Use cases

MobileNetV2(https://arxiv.org/abs/1801.04381) improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. This model is the quantized classification model based on imagenet1K training dataset.

Description

This is quantized version of mobilenet v2. The original full precision model is trained on Imagenet1K dataset. This is a classification model. We use quantized operators with output scale and output zero point labeled.

Please make sure your Caffe2 has QNNPACK based Int8 operators before running inference on it

Model

init_net: https://s3.amazonaws.com/download.caffe2.ai/models/mobilenet_v2_1.0_224_quant/init_net.pb predict_net: https://s3.amazonaws.com/download.caffe2.ai/models/mobilenet_v2_1.0_224_quant/predict_net.pb

Model Download Format Top-1 accuracy (%) Top-5 accuracy (%)
quant mobilenet v2 1.0 224 init_net.pb, predict_net.pb caffe2 72.10%
fp32 mobilenet v2 tf lite model tensorflow 72%
Tensorflow quant mobilenet v2 1.0 224 tf lite model tensorflow 70.80%

Inference

We used Caffe2 as framework to perform inference. Please make sure your Caffe2 has QNNPACK based Int8 operators before running inference on it

Input

(N x 3 x 224 x 224) The pre-trained model expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are height and width of the image.

Preprocessing

The images have to be loaded in to a range of [0, 255] and then normalized using mean = [0.406, 0.456, 0.485] and std = [0.225, 0.224, 0.229]. The transformation should preferrably happen at preprocessing. The following code shows how to preprocess a NCHW tensor and prepare the input to the model:

from caffe2.python import brew, core, model_helper

test_data_db = <Input data path>
test_data_db_type = "lmdb"

test_model = model_helper.ModelHelper(name="test")
with core.NameScope("gpu_0"):
    reader = test_model.CreateDB(
        "test_reader", db=test_data_db, db_type=test_data_db_type
    )

mean_per_channel = [0.406 * 255, 0.456 * 255, 0.485 * 255]
std_per_channel = [0.225 * 255, 0.224 * 255, 0.229 * 255]

namescope = "gpu_0/"
data = namescope + "data_0"
label = namescope + "label"

data, label = brew.image_input(
    test_model,
    reader,
    [data, label],
    batch_size=args.batch_size,
    mean_per_channel=mean_per_channel,
    std_per_channel=std_per_channel,
    scale=256,
    crop=224,
    mirror=0,
    use_caffe_datum=True,
    is_test=1,
    color=3,
)

Output

The model outputs image probablility scores for each of the 1000 classes of ImageNet (https://github.com/onnx/models/blob/master/models/image_classification/synset.txt) calculated using softmax.

Postprocessing

Since the output of the model is the the softmax probablility scores for each class, no post-processing is needed. You can directly sort the output to report the most probable classes.

Dataset

Dataset used for train and validation: ImageNet (ILSVRC2012) (http://www.image-net.org/challenges/LSVRC/2012/). Check imagenet_prep (https://github.com/onnx/models/blob/master/models/image_classification/imagenet_prep.md) for guidelines on preparing the dataset.

Validation accuracy

The accuracies obtained by the models on the validation set are mentioned above.

Training

We used Caffe2 as framework to perform training.

Validation

We used Caffe2 as framework to perform validation.

References

Contributors

  • Yiming Wu
  • Peizhao Zhang
  • Yanghan Wang
  • houseroad

License

Apache 2.0