Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -31,9 +31,12 @@ More details on model performance across various devices, can be found
|
|
31 |
- Model size: 24.4 MB
|
32 |
|
33 |
|
|
|
|
|
34 |
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
35 |
| ---|---|---|---|---|---|---|---|
|
36 |
-
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 15.
|
|
|
37 |
|
38 |
|
39 |
## Installation
|
@@ -91,9 +94,11 @@ device. This script does the following:
|
|
91 |
python -m qai_hub_models.models.yolov7.export
|
92 |
```
|
93 |
|
|
|
|
|
94 |
## How does this work?
|
95 |
|
96 |
-
This [export script](https://
|
97 |
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
|
98 |
on-device. Lets go through each step below in detail:
|
99 |
|
@@ -170,6 +175,7 @@ spot check the output with expected output.
|
|
170 |
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
|
171 |
|
172 |
|
|
|
173 |
## Run demo on a cloud-hosted device
|
174 |
|
175 |
You can also run the demo on-device.
|
@@ -206,7 +212,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
|
|
206 |
## License
|
207 |
- The license for the original implementation of Yolo-v7 can be found
|
208 |
[here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md).
|
209 |
-
- The license for the compiled assets for on-device deployment can be found [here](
|
210 |
|
211 |
## References
|
212 |
* [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
|
|
|
31 |
- Model size: 24.4 MB
|
32 |
|
33 |
|
34 |
+
|
35 |
+
|
36 |
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
37 |
| ---|---|---|---|---|---|---|---|
|
38 |
+
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 15.912 ms | 0 - 23 MB | FP16 | NPU | [Yolo-v7.tflite](https://huggingface.co/qualcomm/Yolo-v7/blob/main/Yolo-v7.tflite)
|
39 |
+
|
40 |
|
41 |
|
42 |
## Installation
|
|
|
94 |
python -m qai_hub_models.models.yolov7.export
|
95 |
```
|
96 |
|
97 |
+
|
98 |
+
|
99 |
## How does this work?
|
100 |
|
101 |
+
This [export script](https://aihub.qualcomm.com/models/yolov7/qai_hub_models/models/Yolo-v7/export.py)
|
102 |
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
|
103 |
on-device. Lets go through each step below in detail:
|
104 |
|
|
|
175 |
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
|
176 |
|
177 |
|
178 |
+
|
179 |
## Run demo on a cloud-hosted device
|
180 |
|
181 |
You can also run the demo on-device.
|
|
|
212 |
## License
|
213 |
- The license for the original implementation of Yolo-v7 can be found
|
214 |
[here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md).
|
215 |
+
- The license for the compiled assets for on-device deployment can be found [here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md)
|
216 |
|
217 |
## References
|
218 |
* [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
|