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Video-MAE

Video Classification
PyTorch
backbone
android
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v0.59.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.59.0 for changelog.

Files changed (2) hide show
  1. README.md +35 -36
  2. release_assets.json +8 -9
README.md CHANGED
@@ -15,7 +15,7 @@ pipeline_tag: video-classification
15
  Video MAE (Masked Auto Encoder) is a network for doing video classification that uses the ViT (Vision Transformer) backbone.
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  This is based on the implementation of Video-MAE found [here](https://github.com/MCG-NJU/VideoMAE).
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- This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/video_mae) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
19
 
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  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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@@ -28,23 +28,23 @@ Below are pre-exported model assets ready for deployment.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
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  |---|---|---|---|---|
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- | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-onnx-float.zip)
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- | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-qnn_dlc-float.zip)
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- | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[Video-MAE on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/video_mae)**.
36
 
37
 
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  ### Option 2: Export with Custom Configurations
39
 
40
- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/video_mae) Python library to compile and export the model with your own:
41
  - Custom weights (e.g., fine-tuned checkpoints)
42
  - Custom input shapes
43
  - Target device and runtime configurations
44
 
45
  This option is ideal if you need to customize the model beyond the default configuration provided here.
46
 
47
- See our repository for [Video-MAE on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/video_mae) for usage instructions.
48
 
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  ## Model Details
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@@ -59,36 +59,35 @@ See our repository for [Video-MAE on GitHub](https://github.com/qualcomm/ai-hub-
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
61
  |---|---|---|---|---|---|---
62
- | Video-MAE | ONNX | float | Snapdragon® X2 Elite | 1579.018 ms | 46 - 46 MB | NPU
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- | Video-MAE | ONNX | float | Snapdragon® X Elite | 2876.701 ms | 192 - 192 MB | NPU
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- | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2815.561 ms | 47 - 51 MB | NPU
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- | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 6805.927 ms | 46 - 95 MB | NPU
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- | Video-MAE | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1515.987 ms | 0 - 5708 MB | NPU
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- | Video-MAE | ONNX | float | Snapdragon® 8 Elite Mobile | 1645.755 ms | 1 - 5502 MB | NPU
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- | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 1645.755 ms | 1 - 5502 MB | NPU
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- | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 2876.701 ms | 192 - 192 MB | NPU
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- | Video-MAE | QNN_DLC | float | Snapdragon® X2 Elite | 1967.699 ms | 46 - 46 MB | NPU
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- | Video-MAE | QNN_DLC | float | Snapdragon® X Elite | 3234.8 ms | 46 - 46 MB | NPU
72
- | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5140.112 ms | 46 - 49 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® SA8775P | 5351.378 ms | 38 - 6133 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® SA8650P | 5351.378 ms | 38 - 6133 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® SA8255P | 5351.378 ms | 38 - 6133 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 5288.535 ms | 46 - 94 MB | NPU
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- | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4069.479 ms | 8 - 6276 MB | NPU
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- | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 3552.571 ms | 147 - 6236 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® SA8295P | 3815.309 ms | 46 - 5747 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 3552.571 ms | 147 - 6236 MB | NPU
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- | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 3234.8 ms | 46 - 46 MB | NPU
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- | Video-MAE | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3512.692 ms | 1 - 6669 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5143.197 ms | 1 - 4 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® SA8775P | 5320.509 ms | 2 - 5971 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® SA8650P | 5320.509 ms | 2 - 5971 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® SA8255P | 5320.509 ms | 2 - 5971 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5258.82 ms | 0 - 279 MB | NPU
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- | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4088.26 ms | 1 - 6101 MB | NPU
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- | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Mobile | 3540.576 ms | 1 - 5951 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® SA8295P | 3736.9 ms | 1 - 5604 MB | NPU
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- | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 3540.576 ms | 1 - 5951 MB | NPU
92
 
93
  ## License
94
  * The license for the original implementation of Video-MAE can be found
 
15
  Video MAE (Masked Auto Encoder) is a network for doing video classification that uses the ViT (Vision Transformer) backbone.
16
 
17
  This is based on the implementation of Video-MAE found [here](https://github.com/MCG-NJU/VideoMAE).
18
+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/video_mae) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
19
 
20
  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
21
 
 
28
 
29
  | Runtime | Precision | Chipset | SDK Versions | Download |
30
  |---|---|---|---|---|
31
+ | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-onnx-float.zip)
32
+ | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-qnn_dlc-float.zip)
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+ | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-tflite-float.zip)
34
 
35
  For more device-specific assets and performance metrics, visit **[Video-MAE on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/video_mae)**.
36
 
37
 
38
  ### Option 2: Export with Custom Configurations
39
 
40
+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/video_mae) Python library to compile and export the model with your own:
41
  - Custom weights (e.g., fine-tuned checkpoints)
42
  - Custom input shapes
43
  - Target device and runtime configurations
44
 
45
  This option is ideal if you need to customize the model beyond the default configuration provided here.
46
 
47
+ See our repository for [Video-MAE on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/video_mae) for usage instructions.
48
 
49
  ## Model Details
50
 
 
59
  ## Performance Summary
60
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
61
  |---|---|---|---|---|---|---
62
+ | Video-MAE | ONNX | float | Snapdragon® X2 Elite | 1580.262 ms | 46 - 46 MB | NPU
63
+ | Video-MAE | ONNX | float | Snapdragon® X Elite | 2859.465 ms | 192 - 192 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2838.231 ms | 1 - 213 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 4901.136 ms | 46 - 95 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 2859.465 ms | 192 - 192 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 1643.62 ms | 1 - 5495 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® 8 Elite Mobile | 1643.62 ms | 1 - 5495 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1515.805 ms | 0 - 5707 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® X2 Elite | 1967.524 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® X Elite | 3232.14 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5141.062 ms | 46 - 49 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8775P | 5350.709 ms | 36 - 6136 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8650P | 5350.709 ms | 36 - 6136 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8255P | 5350.709 ms | 36 - 6136 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 5288.941 ms | 46 - 94 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 3232.14 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 3552.153 ms | 1 - 6092 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8295P | 3815.178 ms | 36 - 5739 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 3552.153 ms | 1 - 6092 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4073.389 ms | 7 - 6274 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5170.295 ms | 1 - 5 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8775P | 5319.772 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8650P | 5319.772 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8255P | 5319.772 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5259.479 ms | 0 - 279 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 3540.118 ms | 3 - 5954 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8295P | 3741.165 ms | 2 - 5604 MB | NPU
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+ | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Mobile | 3540.118 ms | 3 - 5954 MB | NPU
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+ | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4085.554 ms | 1 - 6105 MB | NPU
 
91
 
92
  ## License
93
  * The license for the original implementation of Video-MAE can be found
release_assets.json CHANGED
@@ -1,27 +1,26 @@
1
  {
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- "version": "0.58.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
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- "tflite": {
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  "tool_versions": {
8
  "qairt": "2.45.0.260326154327",
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- "litert": "1.4.4"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-tflite-float.zip"
12
  },
13
  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-qnn_dlc-float.zip"
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  },
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- "onnx": {
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  "tool_versions": {
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- "qairt": "2.45.0.260326154327",
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- "onnx_runtime": "1.25.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.58.0/video_mae-onnx-float.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.59.0",
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  "precisions": {
4
  "float": {
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  "universal_assets": {
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+ "onnx": {
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  "tool_versions": {
8
  "qairt": "2.45.0.260326154327",
9
+ "onnx_runtime": "1.27.1"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-onnx-float.zip"
12
  },
13
  "qnn_dlc": {
14
  "tool_versions": {
15
  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-qnn_dlc-float.zip"
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  },
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+ "tflite": {
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  "tool_versions": {
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+ "qairt": "2.45.0.260326154327"
 
22
  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/video_mae/releases/v0.59.0/video_mae-tflite-float.zip"
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  }
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  }
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  }