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Video-MAE

Video Classification
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android
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v0.60.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.60.0 for changelog.

Files changed (2) hide show
  1. README.md +41 -37
  2. release_assets.json +4 -4
README.md CHANGED
@@ -15,7 +15,7 @@ pipeline_tag: video-classification
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  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/v0.59.0/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
 
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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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@@ -28,66 +28,70 @@ Below are pre-exported model assets ready for deployment.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
30
  |---|---|---|---|---|
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- | 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)
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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/v0.59.0/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)
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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
 
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
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  **Model Type:** Model_use_case.video_classification
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  **Model Stats:**
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- - Model checkpoint: Kinectics-400
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  - Input resolution: 224x224
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- - Number of parameters: 87.7M
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  - Model size (float): 335 MB
 
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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
  |---|---|---|---|---|---|---
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- | 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
 
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/v0.60.0/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/v0.60.0/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/v0.60.0/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/v0.60.0/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/v0.60.0/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/v0.60.0/src/qai_hub_models/models/video_mae) for usage instructions.
48
 
49
  ## Model Details
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51
  **Model Type:** Model_use_case.video_classification
52
 
53
  **Model Stats:**
 
54
  - Input resolution: 224x224
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+ - Model checkpoint: Kinectics-400
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  - Model size (float): 335 MB
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+ - Number of parameters: 87.7M
58
 
59
  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
61
  |---|---|---|---|---|---|---
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+ | Video-MAE | ONNX | float | Snapdragon® X2 Elite | 1518.863 ms | 46 - 46 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® X Elite | 2675.374 ms | 191 - 191 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 2537.857 ms | 1 - 6278 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 2930.774 ms | 46 - 95 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 3824.221 ms | 0 - 212 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 2756.919 ms | 46 - 95 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 2675.374 ms | 191 - 191 MB | NPU
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+ | Video-MAE | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 2327.454 ms | 1 - 5481 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® 8 Elite Mobile | 2327.454 ms | 1 - 5481 MB | NPU
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+ | Video-MAE | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2594.863 ms | 1 - 5688 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® X2 Elite | 1961.284 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® X Elite | 3246.968 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 4725.75 ms | 46 - 94 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5151.275 ms | 46 - 50 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8775P | 5352.202 ms | 46 - 6139 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8650P | 5352.202 ms | 46 - 6139 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8255P | 5352.202 ms | 46 - 6139 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 7276.539 ms | 48 - 96 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 3246.968 ms | 46 - 46 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 3556.566 ms | 26 - 6118 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Qualcomm® SA8295P | 3813.452 ms | 35 - 5737 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 3556.566 ms | 26 - 6118 MB | NPU
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+ | Video-MAE | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4071.688 ms | 3 - 6270 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 4622.577 ms | 0 - 280 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5166.465 ms | 1 - 4 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8775P | 5322.161 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8650P | 5322.161 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8255P | 5322.161 ms | 2 - 5973 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5258.49 ms | 0 - 279 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 3540.224 ms | 1 - 5953 MB | NPU
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+ | Video-MAE | TFLITE | float | Qualcomm® SA8295P | 3731.908 ms | 2 - 5603 MB | NPU
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+ | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Mobile | 3540.224 ms | 1 - 5953 MB | NPU
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+ | Video-MAE | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4089.075 ms | 1 - 6110 MB | NPU
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96
  ## License
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  * The license for the original implementation of Video-MAE can be found
release_assets.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "version": "0.59.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
@@ -8,19 +8,19 @@
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  "qairt": "2.45.0.260326154327",
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  "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"
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  },
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  "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/v0.59.0/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"
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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-tflite-float.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.60.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
 
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  "qairt": "2.45.0.260326154327",
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  "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/v0.60.0/video_mae-onnx-float.zip"
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  },
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  "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/v0.60.0/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"
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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/v0.60.0/video_mae-tflite-float.zip"
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  }
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  }
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  }