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@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_farm_ppe_detection_qcs6490_qnn2.31_int8_aidlite/python/test.jpg filter=lfs diff=lfs merge=lfs -text
+model_farm_ppe_detection_qcs6490_qnn2.31_w8a16_aidlite/python/test.jpg filter=lfs diff=lfs merge=lfs -text
+model_farm_ppe_detection_qcs8550_qnn2.31_fp16_aidlite/python/test.jpg filter=lfs diff=lfs merge=lfs -text
+model_farm_ppe_detection_qcs8550_qnn2.31_int8_aidlite/python/test.jpg filter=lfs diff=lfs merge=lfs -text
+model_farm_ppe_detection_qcs8550_qnn2.31_w8a16_aidlite/python/test.jpg filter=lfs diff=lfs merge=lfs -text

model_farm_ppe_detection_qcs6490_qnn2.31_int8_aidlite/README.md

@@ -0,0 +1,40 @@
+## Model Information
+
+### Source model
+- Input shape: [1x3x320x192]
+- Number of parameters: 5.92M
+- Model size: 23.64M
+- Output shape: [[1x21x40x24],[1x21x20x12],[1x21x10x6]]
+
+Source model repository: [PPE-Detection](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net/model.py)
+
+### Converted model
+
+- Precision: INT8
+- Backend: QNN2.31
+- Target Device: FV01 QCS6490
+
+## Inference with AidLite SDK
+
+### SDK installation
+Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
+
+- Install AidLite SDK
+
+```bash
+# Install the appropriate version of the aidlite sdk
+sudo aid-pkg update
+sudo aid-pkg install aidlite-sdk
+# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
+sudo aid-pkg install aidlite-{QNN VERSION}
+```
+
+- Verify AidLite SDK
+
+```bash
+# aidlite sdk c++ check
+python3 -c "import aidlite ; print(aidlite.get_library_version())"
+
+# aidlite sdk python check
+python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
+```

model_farm_ppe_detection_qcs6490_qnn2.31_int8_aidlite/models/ppe_detection_w8a8.qnn231.ctx.bin

@@ -0,0 +1,3 @@
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+size 9912520

model_farm_ppe_detection_qcs6490_qnn2.31_w8a16_aidlite/README.md

@@ -0,0 +1,40 @@
+## Model Information
+
+### Source model
+- Input shape: [1x3x320x192]
+- Number of parameters: 5.92M
+- Model size: 23.64M
+- Output shape: [[1x21x40x24],[1x21x20x12],[1x21x10x6]]
+
+Source model repository: [PPE-Detection](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net/model.py)
+
+### Converted model
+
+- Precision: W8A16
+- Backend: QNN2.31
+- Target Device: FV01 QCS6490
+
+## Inference with AidLite SDK
+
+### SDK installation
+Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
+
+- Install AidLite SDK
+
+```bash
+# Install the appropriate version of the aidlite sdk
+sudo aid-pkg update
+sudo aid-pkg install aidlite-sdk
+# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
+sudo aid-pkg install aidlite-{QNN VERSION}
+```
+
+- Verify AidLite SDK
+
+```bash
+# aidlite sdk c++ check
+python3 -c "import aidlite ; print(aidlite.get_library_version())"
+
+# aidlite sdk python check
+python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
+```

model_farm_ppe_detection_qcs6490_qnn2.31_w8a16_aidlite/models/ppe_detection_w8a16.qnn231.ctx.bin

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+size 9912520

model_farm_ppe_detection_qcs8550_qnn2.31_fp16_aidlite/README.md

@@ -0,0 +1,40 @@
+## Model Information
+
+### Source model
+- Input shape: [1x3x320x192]
+- Number of parameters: 5.92M
+- Model size: 23.64M
+- Output shape: [[1x21x40x24],[1x21x20x12],[1x21x10x6]]
+
+Source model repository: [PPE-Detection](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net/model.py)
+
+### Converted model
+
+- Precision: FP16
+- Backend: QNN2.31
+- Target Device: SNM972 QCS8550
+
+## Inference with AidLite SDK
+
+### SDK installation
+Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
+
+- Install AidLite SDK
+
+```bash
+# Install the appropriate version of the aidlite sdk
+sudo aid-pkg update
+sudo aid-pkg install aidlite-sdk
+# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
+sudo aid-pkg install aidlite-{QNN VERSION}
+```
+
+- Verify AidLite SDK
+
+```bash
+# aidlite sdk c++ check
+python3 -c "import aidlite ; print(aidlite.get_library_version())"
+
+# aidlite sdk python check
+python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
+```

model_farm_ppe_detection_qcs8550_qnn2.31_fp16_aidlite/models/ppe_detection_fp16.qnn231.ctx.bin

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+size 12822008

model_farm_ppe_detection_qcs8550_qnn2.31_int8_aidlite/README.md

@@ -0,0 +1,40 @@
+## Model Information
+
+### Source model
+- Input shape: [1x3x320x192]
+- Number of parameters: 5.92M
+- Model size: 23.64M
+- Output shape: [[1x21x40x24],[1x21x20x12],[1x21x10x6]]
+
+Source model repository: [PPE-Detection](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net/model.py)
+
+### Converted model
+
+- Precision: INT8
+- Backend: QNN2.31
+- Target Device: SNM972 QCS8550
+
+## Inference with AidLite SDK
+
+### SDK installation
+Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
+
+- Install AidLite SDK
+
+```bash
+# Install the appropriate version of the aidlite sdk
+sudo aid-pkg update
+sudo aid-pkg install aidlite-sdk
+# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
+sudo aid-pkg install aidlite-{QNN VERSION}
+```
+
+- Verify AidLite SDK
+
+```bash
+# aidlite sdk c++ check
+python3 -c "import aidlite ; print(aidlite.get_library_version())"
+
+# aidlite sdk python check
+python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
+```

model_farm_ppe_detection_qcs8550_qnn2.31_int8_aidlite/models/ppe_detection_w8a8.qnn231.ctx.bin

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+size 6746248

model_farm_ppe_detection_qcs8550_qnn2.31_w8a16_aidlite/README.md

@@ -0,0 +1,40 @@
+## Model Information
+
+### Source model
+- Input shape: [1x3x320x192]
+- Number of parameters: 5.92M
+- Model size: 23.64M
+- Output shape: [[1x21x40x24],[1x21x20x12],[1x21x10x6]]
+
+Source model repository: [PPE-Detection](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net/model.py)
+
+### Converted model
+
+- Precision: W8A16
+- Backend: QNN2.31
+- Target Device: SNM972 QCS8550
+
+## Inference with AidLite SDK
+
+### SDK installation
+Model Farm uses AidLite SDK as the model inference SDK. For details, please refer to the [AidLite Developer Documentation](https://v2.docs.aidlux.com/en/sdk-api/aidlite-sdk/)
+
+- Install AidLite SDK
+
+```bash
+# Install the appropriate version of the aidlite sdk
+sudo aid-pkg update
+sudo aid-pkg install aidlite-sdk
+# Download the qnn version that matches the above backend. Eg Install QNN2.23 Aidlite: sudo aid-pkg install aidlite-qnn223
+sudo aid-pkg install aidlite-{QNN VERSION}
+```
+
+- Verify AidLite SDK
+
+```bash
+# aidlite sdk c++ check
+python3 -c "import aidlite ; print(aidlite.get_library_version())"
+
+# aidlite sdk python check
+python3 -c "import aidlite ; print(aidlite.get_py_library_version())"
+```

model_farm_ppe_detection_qcs8550_qnn2.31_w8a16_aidlite/models/ppe_detection_w8a16.qnn231.ctx.bin

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