We provide a set of efficient pre-trained pytorch models for FBNet/FBNetV2. We also provide quantized FBNet models in TorchScript format.
Here is an example code to create a pretrained FBNet model:
from mobile_cv.model_zoo.models.fbnet_v2 import fbnet
from mobile_cv.model_zoo.models.preprocess import get_preprocess
model_name = "dmasking_l3"
model = fbnet(model_name, pretrained=True)
model.eval()
# get preprocess function
preprocess = get_preprocess(model.arch_def.get("input_size", 224))The input image requires the following preprocessing:
# load and process input
# `input_inage` in RGB format in the range of [0..255]
input_image = _get_input()
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)The model could be run on the image as follow:
with torch.no_grad():
output = model(input_batch)
output_softmax = torch.nn.functional.softmax(output[0], dim=0)
print(output_softmax.max(0))A full example code is available here.
The following FBNet/FBNetV2 pre-trained models are provided. The models are trained and evaluated using ImageNet 1k (ILSVRC2012) dataset. Validation top-1 and top-5 accuracy for fp32 models are reported.
| Model | Resolution | Flops (M) | Params (M) | Top-1 Accuracy | Top-5 Accuracy |
|---|---|---|---|---|---|
| fbnet_a | 224x224 | 244.5 | 4.3 | 73.3 | 90.9 |
| fbnet_b | 224x224 | 291.1 | 4.8 | 74.5 | 91.8 |
| fbnet_c | 224x224 | 378.2 | 5.5 | 75.2 | 92.3 |
| dmasking_f1 | 128x128 | 56.3 | 6.0 | 68.3 | 87.8 |
| dmasking_f4 | 224x224 | 235.9 | 7.0 | 75.5 | 92.5 |
| dmasking_l2_hs | 256x256 | 419.1 | 8.4 | 77.7 | 93.7 |
| dmasking_l3 | 288x288 | 753.1 | 9.4 | 78.9 | 94.3 |
The following FBNet quantized pre-trained models are provided. The models are trained and evaluated using ImageNet 1k (ILSVRC2012) dataset. Validation top-1 and top-5 accuracy for the int8 models are reported.
| Model | Resolution | Flops (M) | Params (M) | Top-1 Accuracy | Top-5 Accuracy |
|---|---|---|---|---|---|
| fbnet_a_i8f_int8_jit | 224x224 | 244.5 | 4.3 | 72.2 | 90.3 |
| fbnet_b_i8f_int8_jit | 224x224 | 291.1 | 4.8 | 73.2 | 91.1 |
| fbnet_c_i8f_int8_jit | 224x224 | 378.2 | 5.5 | 74.2 | 91.8 |
The quantized models could be loaded in a similar way:
from mobile_cv.model_zoo.models.model_jit import model_jit
from mobile_cv.model_zoo.models.preprocess import get_preprocess
# the model is quantized with qnnpack backend
torch.backends.quantized.engine = "qnnpack"
model_name = "fbnet_c_i8f_int8_jit"
model = model_jit(model_name)
model.eval()
# get preprocess function
preprocess = get_preprocess(224)A full example code is available here.