MediaDroid Models
Pre-converted models for MediaDroid — an Android media player that plays movies and shows from local storage and network shares.
MediaDroid offers them only where a self-test in its Settings finds the device fast enough for playback; elsewhere
the setting switches itself off again and says how long the device took. The ones too large to carry in the app
(dain/) are fetched from here when they are switched on, each file checked against its SHA-256 digest.
Models
| File | Size | Purpose | Source | Input | Output |
|---|---|---|---|---|---|
rife-4.25-lite-576p.tflite |
~29 MB | Frame interpolation — the picture halfway between two frames (RIFE 4.25 lite), TensorFlow Lite, float32 | hzwer/Practical-RIFE (MIT) | img0, img1: 1×640×768×3 float32 [0,1] (NHWC) |
middle: 1×640×768×3 float32 [0,1] |
rife-4.25-lite-576p.onnx |
~72 MB | The same network as ONNX (opset 17), before the TFLite conversion | Same as above | img0, img1: 1×3×640×768 float32 [0,1] (NCHW) |
middle: 1×3×640×768 float32 |
768 × 640 is 576p (720 × 576) padded to a multiple of 128, as Practical-RIFE pads; other sizes can be resampled to it. The in-between is at t = 0.5.
How it was made, and checked
The 4.25.lite release's weights (flownet.pkl, under the same MIT licence as Practical-RIFE) were loaded as tensors
only into the network written out from its published architecture (tools/rife_net.py), exported to ONNX and
converted with onnx2tf. For the conversion — each checked to give the same result — the resampling uses sizes
instead of scale factors, the identity grid and the time plane are constants, and the warp's border padding is
clamped coordinates with zero padding (the same with align_corners).
Against PyTorch on a test pair, the TFLite in-between differs by 0.00008 on average; 0.08 % of values differ by
more than 1/255. A float16 conversion came out wrong and is not offered. Steps in tools/CONVERSION.md.
On a Galaxy S20 (Snapdragon 865) one in-between takes about 1.7 s on the CPU, its fastest unit for this network — far from real time there; newer devices with stronger NPUs and GPUs are where it is meant to run.
DAIN for ncnn (dain/)
Frame interpolation weighing depth — DAIN (Depth-Aware Video Frame Interpolation, CVPR 2019) as nihui converted
it for ncnn in dain-ncnn-vulkan
(models/best), copied unchanged. Four networks, each as .param (structure) and .bin (weights), about 51 MB:
| Network | Input | Output |
|---|---|---|
depthnet |
input: a frame, 3 planes BGR in 0..1 |
depth |
flownet |
input0, input1: two frames |
flow (from the first to the second) |
ctxnet |
input: a frame |
ctx |
interpolation |
both frames, both depths, both flows, flow0_w = t and flow1_w = 1 − t, both contexts |
output_rectified: the in-between, 3 planes BGR in 0..1 |
Any size, padded to a multiple of 32 with the edges repeated. The networks call four layers of DAIN's own —
dain.Correlation, dain.OpticalFlowWarp, dain.DepthFlowProjection, dain.FilterInterpolation — which
dain-ncnn-vulkan implements, and MediaDroid after it (CPU and Vulkan).
Licences: DAIN by Wenbo Bao (LICENSE-DAIN, MIT), dain-ncnn-vulkan by nihui (LICENSE-dain-ncnn-vulkan, MIT).
| File | Bytes | SHA-256 |
|---|---|---|
dain/depthnet.param |
23625 | e5b2824a8efd13fcc8875456df47f723682f3b38ebac897c7ad06c4450cd15c0 |
dain/depthnet.bin |
10728564 | 4d063853f50ecb7835e9c28c04087f9256541a276478925d5b062e4ab8e07355 |
dain/flownet.param |
17852 | ce1e6c4e86e21de63e6e94ddf37d8bc1f01ae1a3d7a983b5513df338ccc54d91 |
dain/flownet.bin |
22092552 | f6d5f47e84d0b25d9fff06c5acb382bef8281097188fd3a240501e754e95e2cb |
dain/ctxnet.param |
1254 | 85fc9a1fbf2d398b02edceaae2081ff1716edb44afd980c4928dacb44856f929 |
dain/ctxnet.bin |
313748 | 957faa2e2cd475648166f056f65963136d24e4ccfcc665e367d7868b2f56d3e4 |
dain/interpolation.param |
9066 | 9475d3368d092e8074189c44baa11fd5e357bf930188797fb2b4b899333cfdc1 |
dain/interpolation.bin |
18291116 | 485ca7ffb0eb86e9468e9841356c0af5c55070dde1901a3628ce98baf5d28b6d |
Other models used by MediaDroid
| Model | Purpose | Source |
|---|---|---|
| RIFE 4.25 lite (ncnn) | Frame interpolation at any size, carried in the app | TNTwise/rife-ncnn-vulkan (MIT, after nihui's rife-ncnn-vulkan) |
| Real-ESRGAN General x4v3 (TFLite, 8-bit) | Neural upscaling, 4× | qualcomm/Real-ESRGAN-General-x4v3 (BSD-3-Clause, from xinntao/Real-ESRGAN) |
Licence
MIT throughout: LICENSE-RIFE (Copyright (c) 2021 hzwer), LICENSE-DAIN (Copyright (c) 2019 Wenbo Bao),
LICENSE-dain-ncnn-vulkan (Copyright (c) 2020 nihui).
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