Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -36,30 +36,29 @@ More details on model performance across various devices, can be found
|
|
36 |
|
37 |
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
38 |
|---|---|---|---|---|---|---|---|---|
|
39 |
-
| EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN |
|
40 |
-
| EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX |
|
41 |
-
| EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN |
|
42 |
-
| EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX |
|
43 |
-
| EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN |
|
44 |
-
| EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX |
|
45 |
-
| EfficientViT-l2-seg | QCS8550 (Proxy) | QCS8550 Proxy | QNN |
|
46 |
-
| EfficientViT-l2-seg | QCS8450 (Proxy) | QCS8450 Proxy | QNN |
|
47 |
-
| EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN |
|
48 |
-
| EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX |
|
49 |
|
50 |
|
51 |
|
52 |
|
53 |
## Installation
|
54 |
|
55 |
-
This model can be installed as a Python package via pip.
|
56 |
|
|
|
57 |
```bash
|
58 |
-
pip install "qai-hub-models[
|
59 |
```
|
60 |
|
61 |
|
62 |
-
|
63 |
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
|
64 |
|
65 |
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
|
@@ -110,8 +109,8 @@ Profiling Results
|
|
110 |
EfficientViT-l2-seg
|
111 |
Device : Samsung Galaxy S23 (13)
|
112 |
Runtime : QNN
|
113 |
-
Estimated inference time (ms) :
|
114 |
-
Estimated peak memory usage (MB): [24,
|
115 |
Total # Ops : 775
|
116 |
Compute Unit(s) : NPU (775 ops)
|
117 |
```
|
@@ -138,7 +137,7 @@ from qai_hub_models.models.efficientvit_l2_seg import Model
|
|
138 |
torch_model = Model.from_pretrained()
|
139 |
|
140 |
# Device
|
141 |
-
device = hub.Device("Samsung Galaxy
|
142 |
|
143 |
# Trace model
|
144 |
input_shape = torch_model.get_input_spec()
|
@@ -230,7 +229,8 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
|
|
230 |
|
231 |
|
232 |
## License
|
233 |
-
* The license for the original implementation of EfficientViT-l2-seg can be found
|
|
|
234 |
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
|
235 |
|
236 |
|
|
|
36 |
|
37 |
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|
38 |
|---|---|---|---|---|---|---|---|---|
|
39 |
+
| EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 22178.805 ms | 24 - 124 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
|
40 |
+
| EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1921.957 ms | 5 - 354 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
|
41 |
+
| EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16480.291 ms | 11 - 455 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
|
42 |
+
| EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1995.319 ms | 132 - 775 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
|
43 |
+
| EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 19442.548 ms | 24 - 516 MB | FP16 | NPU | Use Export Script |
|
44 |
+
| EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1445.969 ms | 101 - 809 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
|
45 |
+
| EfficientViT-l2-seg | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 15540.11 ms | 26 - 30 MB | FP16 | NPU | Use Export Script |
|
46 |
+
| EfficientViT-l2-seg | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 21630.243 ms | 16 - 262 MB | FP16 | NPU | Use Export Script |
|
47 |
+
| EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 16477.289 ms | 24 - 24 MB | FP16 | NPU | Use Export Script |
|
48 |
+
| EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2870.496 ms | 144 - 144 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
|
49 |
|
50 |
|
51 |
|
52 |
|
53 |
## Installation
|
54 |
|
|
|
55 |
|
56 |
+
Install the package via pip:
|
57 |
```bash
|
58 |
+
pip install "qai-hub-models[efficientvit-l2-seg]"
|
59 |
```
|
60 |
|
61 |
|
|
|
62 |
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
|
63 |
|
64 |
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
|
|
|
109 |
EfficientViT-l2-seg
|
110 |
Device : Samsung Galaxy S23 (13)
|
111 |
Runtime : QNN
|
112 |
+
Estimated inference time (ms) : 22178.8
|
113 |
+
Estimated peak memory usage (MB): [24, 124]
|
114 |
Total # Ops : 775
|
115 |
Compute Unit(s) : NPU (775 ops)
|
116 |
```
|
|
|
137 |
torch_model = Model.from_pretrained()
|
138 |
|
139 |
# Device
|
140 |
+
device = hub.Device("Samsung Galaxy S24")
|
141 |
|
142 |
# Trace model
|
143 |
input_shape = torch_model.get_input_spec()
|
|
|
229 |
|
230 |
|
231 |
## License
|
232 |
+
* The license for the original implementation of EfficientViT-l2-seg can be found
|
233 |
+
[here](https://github.com/CVHub520/efficientvit/blob/main/LICENSE).
|
234 |
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
|
235 |
|
236 |
|