Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -34,10 +34,10 @@ More details on model performance across various devices, can be found
|
|
34 |
|
35 |
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
36 |
| ---|---|---|---|---|---|---|---|
|
37 |
-
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.
|
38 |
-
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.
|
39 |
-
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.
|
40 |
-
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library |
|
41 |
|
42 |
|
43 |
## Installation
|
@@ -45,11 +45,10 @@ More details on model performance across various devices, can be found
|
|
45 |
This model can be installed as a Python package via pip.
|
46 |
|
47 |
```bash
|
48 |
-
pip install
|
49 |
```
|
50 |
|
51 |
|
52 |
-
|
53 |
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
|
54 |
|
55 |
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
|
@@ -98,31 +97,31 @@ python -m qai_hub_models.models.mediapipe_hand.export
|
|
98 |
```
|
99 |
Profile Job summary of MediaPipeHandDetector
|
100 |
--------------------------------------------------
|
101 |
-
Device: Samsung Galaxy
|
102 |
-
Estimated Inference Time: 0.
|
103 |
-
Estimated Peak Memory Range: 0.01-
|
104 |
Compute Units: NPU (151) | Total (151)
|
105 |
|
106 |
Profile Job summary of MediaPipeHandLandmarkDetector
|
107 |
--------------------------------------------------
|
108 |
-
Device: Samsung Galaxy
|
109 |
-
Estimated Inference Time:
|
110 |
-
Estimated Peak Memory Range: 0.02-
|
111 |
Compute Units: NPU (158) | Total (158)
|
112 |
|
113 |
Profile Job summary of MediaPipeHandDetector
|
114 |
--------------------------------------------------
|
115 |
-
Device: Samsung Galaxy
|
116 |
-
Estimated Inference Time: 0.
|
117 |
-
Estimated Peak Memory Range: 0.
|
118 |
-
Compute Units: NPU (
|
119 |
|
120 |
Profile Job summary of MediaPipeHandLandmarkDetector
|
121 |
--------------------------------------------------
|
122 |
-
Device: Samsung Galaxy
|
123 |
-
Estimated Inference Time:
|
124 |
-
Estimated Peak Memory Range: 0.
|
125 |
-
Compute Units: NPU (
|
126 |
|
127 |
|
128 |
```
|
@@ -227,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
|
|
227 |
## License
|
228 |
- The license for the original implementation of MediaPipe-Hand-Detection can be found
|
229 |
[here](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE).
|
230 |
-
- The license for the compiled assets for on-device deployment can be found [here](
|
231 |
|
232 |
## References
|
233 |
* [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
|
|
|
34 |
|
35 |
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
36 |
| ---|---|---|---|---|---|---|---|
|
37 |
+
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.765 ms | 0 - 12 MB | FP16 | NPU | [MediaPipeHandDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite)
|
38 |
+
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.047 ms | 0 - 2 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite)
|
39 |
+
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.763 ms | 0 - 2 MB | FP16 | NPU | [MediaPipeHandDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.so)
|
40 |
+
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.996 ms | 0 - 10 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.so)
|
41 |
|
42 |
|
43 |
## Installation
|
|
|
45 |
This model can be installed as a Python package via pip.
|
46 |
|
47 |
```bash
|
48 |
+
pip install qai-hub-models
|
49 |
```
|
50 |
|
51 |
|
|
|
52 |
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
|
53 |
|
54 |
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
|
|
|
97 |
```
|
98 |
Profile Job summary of MediaPipeHandDetector
|
99 |
--------------------------------------------------
|
100 |
+
Device: Samsung Galaxy S24 (14)
|
101 |
+
Estimated Inference Time: 0.57 ms
|
102 |
+
Estimated Peak Memory Range: 0.01-49.27 MB
|
103 |
Compute Units: NPU (151) | Total (151)
|
104 |
|
105 |
Profile Job summary of MediaPipeHandLandmarkDetector
|
106 |
--------------------------------------------------
|
107 |
+
Device: Samsung Galaxy S24 (14)
|
108 |
+
Estimated Inference Time: 0.75 ms
|
109 |
+
Estimated Peak Memory Range: 0.02-51.85 MB
|
110 |
Compute Units: NPU (158) | Total (158)
|
111 |
|
112 |
Profile Job summary of MediaPipeHandDetector
|
113 |
--------------------------------------------------
|
114 |
+
Device: Samsung Galaxy S24 (14)
|
115 |
+
Estimated Inference Time: 0.55 ms
|
116 |
+
Estimated Peak Memory Range: 0.01-49.65 MB
|
117 |
+
Compute Units: NPU (151) | Total (151)
|
118 |
|
119 |
Profile Job summary of MediaPipeHandLandmarkDetector
|
120 |
--------------------------------------------------
|
121 |
+
Device: Samsung Galaxy S24 (14)
|
122 |
+
Estimated Inference Time: 0.75 ms
|
123 |
+
Estimated Peak Memory Range: 0.01-51.44 MB
|
124 |
+
Compute Units: NPU (158) | Total (158)
|
125 |
|
126 |
|
127 |
```
|
|
|
226 |
## License
|
227 |
- The license for the original implementation of MediaPipe-Hand-Detection can be found
|
228 |
[here](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE).
|
229 |
+
- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
|
230 |
|
231 |
## References
|
232 |
* [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
|