initial commit

.gitattributes

@@ -33,3 +33,10 @@ 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
+*.om filter=lfs diff=lfs merge=lfs -text
+*.mnn filter=lfs diff=lfs merge=lfs -text
+*.rknn filter=lfs diff=lfs merge=lfs -text
+*.torchscript filter=lfs diff=lfs merge=lfs -text
+*.param filter=lfs diff=lfs merge=lfs -text
+*.pdparams filter=lfs diff=lfs merge=lfs -text
+*.pdiparams filter=lfs diff=lfs merge=lfs -text

safetensors/convert.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+
+import torch
+from ultralytics import YOLO
+from safetensors.torch import save_file
+
+# Load model
+model = YOLO("yolov5su.pt")
+
+# Extract state dict
+state_dict = model.model.state_dict()
+
+# Convert all tensors to float32 (safetensors requires contiguous tensors)
+state_dict = {k: v.contiguous().float() for k, v in state_dict.items()}
+
+# Save
+save_file(state_dict, "yolov5su.safetensors")
+print("Done!")

safetensors/yolov5su.safetensors

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yolov5-om/fusion_result.json

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+    }
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yolov5-om/onnx-version.py

@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+import onnx
+import sys
+
+# Load the ONNX model
+model = onnx.load(sys.argv[1])
+
+# Print the opset version
+print("Opset version:", model.opset_import[0].version)
+
+# Optional: print the IR version
+print("IR version:", model.ir_version)
+

yolov5-om/yolov5su-opset14.onnx

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yolov5-om/yolov5su-upstream.pt

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yolov5-om/yolov5su_saved_model/fingerprint.pb

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yolov5-om/yolov5su_saved_model/metadata.yaml

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+description: Ultralytics YOLOv5s model trained on coco.yaml
+author: Ultralytics
+date: '2026-02-06T07:41:51.180089'
+version: 8.4.8
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  nms: false
+channels: 3
+end2end: false

yolov5-om/yolov5su_saved_model/read-tflite.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+
+import flatbuffers
+import tflite.Model as tflite_model
+
+with open("yolov5su_float32.tflite", "rb") as f:
+    buf = f.read()
+
+model = tflite_model.GetRootAsModel(buf, 0)
+print("Number of subgraphs:", model.SubgraphsLength())
+for i in range(model.SubgraphsLength()):
+    sg = model.Subgraphs(i)
+    print(f"Subgraph {i} has {sg.TensorsLength()} tensors")

yolov5-om/yolov5su_saved_model/saved_model.pb

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yolov5-om/yolov5su_saved_model/yolov5su_float16.tflite

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yolov5-om/yolov5su_saved_model/yolov5su_float32.tflite

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yolov5-om/yolov5supt_to_onnx14.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+
+import sys
+import torch
+from pathlib import Path
+
+# Load your PyTorch model
+checkpoint = torch.load(sys.argv[1], map_location="cpu", weights_only=False)  # adjust path
+model = checkpoint['model'] if 'model' in checkpoint else checkpoint
+model.eval()
+
+# Dummy input matching the model input
+dummy_input = torch.randn(1, 3, 640, 640)  # Adjust shape to your model
+
+# Export to ONNX
+onnx_path = Path("model.onnx")
+torch.onnx.export(
+    model,
+    dummy_input,
+    str(onnx_path),
+    export_params=True,
+    opset_version=14,          # recommended for ATC
+    do_constant_folding=True,
+    input_names=['images'],
+    output_names=['output'],
+    dynamic_axes={'images': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
+)
+print(f"Exported ONNX model to {onnx_path}")
+

yolov5-rknn/convert.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+
+# apt-get install libgl1
+# pip install rknn-toolkit2
+# pip install onnxsim
+# pip install "setuptools<70"
+# pip install "onnx==1.14.1"
+
+from rknn.api import RKNN
+
+rknn = RKNN()
+
+# 1. Configure
+rknn.config(
+    mean_values=[[0, 0, 0]],
+    std_values=[[255, 255, 255]],
+    target_platform='rk3588'   # or rk3566, rk3568
+)
+
+# 2. Load ONNX
+ret = rknn.load_onnx(model='yolov5su-opset14.onnx')
+if ret != 0:
+    exit(ret)
+
+# 3. Build (quantization optional but recommended)
+ret = rknn.build(do_quantization=False)
+if ret != 0:
+    exit(ret)
+
+# 4. Export RKNN
+ret = rknn.export_rknn('yolov5su.rknn')
+if ret != 0:
+    exit(ret)
+
+rknn.release()

yolov5-rknn/yolov5su-opset14.onnx

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yolov5su.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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yolov5su.mlpackage/Manifest.json

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+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
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+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "531f3a11-f094-499c-9378-34c19d85935f": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "531f3a11-f094-499c-9378-34c19d85935f"
+}

yolov5su.mnn

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yolov5su.onnx

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yolov5su.pt

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yolov5su.torchscript

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yolov5su_ncnn_model/metadata.yaml

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+description: Ultralytics YOLOv5s model trained on coco.yaml
+author: Ultralytics
+date: '2026-03-13T12:55:51.616112'
+version: 8.4.8
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+args:
+  batch: 1
+  half: false
+channels: 3
+end2end: false

yolov5su_ncnn_model/model.ncnn.bin

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yolov5su_ncnn_model/model.ncnn.param

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yolov5su_ncnn_model/model_ncnn.py

@@ -0,0 +1,26 @@
+import numpy as np
+import ncnn
+import torch
+
+def test_inference():
+    torch.manual_seed(0)
+    in0 = torch.rand(1, 3, 640, 640, dtype=torch.float)
+    out = []
+
+    with ncnn.Net() as net:
+        net.load_param("yolov5su_ncnn_model/model.ncnn.param")
+        net.load_model("yolov5su_ncnn_model/model.ncnn.bin")
+
+        with net.create_extractor() as ex:
+            ex.input("in0", ncnn.Mat(in0.squeeze(0).numpy()).clone())
+
+            _, out0 = ex.extract("out0")
+            out.append(torch.from_numpy(np.array(out0)).unsqueeze(0))
+
+    if len(out) == 1:
+        return out[0]
+    else:
+        return tuple(out)
+
+if __name__ == "__main__":
+    print(test_inference())

yolov5su_openvino_model/metadata.yaml

@@ -0,0 +1,102 @@
+description: Ultralytics YOLOv5s model trained on coco.yaml
+author: Ultralytics
+date: '2026-03-13T12:46:35.094055'
+version: 8.4.8
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  dynamic: false
+  nms: false
+channels: 3
+end2end: false

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yolov5su_paddle_model/inference_model/model.pdiparams

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29692450e1a8aa9b012127187e69c8ca8e0e3739f09eafa3de5c933a7b2eaad7
+size 36675067

yolov5su_paddle_model/metadata.yaml

@@ -0,0 +1,97 @@
+description: Ultralytics YOLOv5s model trained on coco.yaml
+author: Ultralytics
+date: '2026-03-13T12:53:28.548281'
+version: 8.4.8
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+args:
+  batch: 1
+channels: 3
+end2end: false

yolov5su_paddle_model/model.pdparams

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+version https://git-lfs.github.com/spec/v1
+oid sha256:515746866e7b54d31426703cb9b1e4d23c239573e8936654d4b29c4cff8feeb5
+size 36679647

yolov5su_paddle_model/x2paddle_code.py

@@ -0,0 +1,405 @@
+import paddle
+import math
+from x2paddle.op_mapper.pytorch2paddle import pytorch_custom_layer as x2paddle_nn
+
+class DetectionModel(paddle.nn.Layer):
+    def __init__(self):
+        super(DetectionModel, self).__init__()
+        self.conv2d0 = paddle.nn.Conv2D(stride=2, padding=2, out_channels=32, kernel_size=(6, 6), in_channels=3)
+        self.silu0 = paddle.nn.Silu()
+        self.conv2d1 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=64, kernel_size=(3, 3), in_channels=32)
+        self.silu1 = paddle.nn.Silu()
+        self.conv2d2 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=64)
+        self.silu2 = paddle.nn.Silu()
+        self.conv2d3 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=32)
+        self.silu3 = paddle.nn.Silu()
+        self.conv2d4 = paddle.nn.Conv2D(padding=1, out_channels=32, kernel_size=(3, 3), in_channels=32)
+        self.silu4 = paddle.nn.Silu()
+        self.conv2d5 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=64)
+        self.silu5 = paddle.nn.Silu()
+        self.conv2d6 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.silu6 = paddle.nn.Silu()
+        self.conv2d7 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=128, kernel_size=(3, 3), in_channels=64)
+        self.silu7 = paddle.nn.Silu()
+        self.conv2d8 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=128)
+        self.silu8 = paddle.nn.Silu()
+        self.conv2d9 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.silu9 = paddle.nn.Silu()
+        self.conv2d10 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu10 = paddle.nn.Silu()
+        self.conv2d11 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.silu11 = paddle.nn.Silu()
+        self.conv2d12 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu12 = paddle.nn.Silu()
+        self.conv2d13 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=128)
+        self.silu13 = paddle.nn.Silu()
+        self.conv2d14 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu14 = paddle.nn.Silu()
+        self.conv2d15 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=256, kernel_size=(3, 3), in_channels=128)
+        self.silu15 = paddle.nn.Silu()
+        self.conv2d16 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256)
+        self.silu16 = paddle.nn.Silu()
+        self.conv2d17 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu17 = paddle.nn.Silu()
+        self.conv2d18 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu18 = paddle.nn.Silu()
+        self.conv2d19 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu19 = paddle.nn.Silu()
+        self.conv2d20 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu20 = paddle.nn.Silu()
+        self.conv2d21 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu21 = paddle.nn.Silu()
+        self.conv2d22 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu22 = paddle.nn.Silu()
+        self.conv2d23 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256)
+        self.silu23 = paddle.nn.Silu()
+        self.conv2d24 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256)
+        self.silu24 = paddle.nn.Silu()
+        self.conv2d25 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=512, kernel_size=(3, 3), in_channels=256)
+        self.silu25 = paddle.nn.Silu()
+        self.conv2d26 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu26 = paddle.nn.Silu()
+        self.conv2d27 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256)
+        self.silu27 = paddle.nn.Silu()
+        self.conv2d28 = paddle.nn.Conv2D(padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256)
+        self.silu28 = paddle.nn.Silu()
+        self.conv2d29 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu29 = paddle.nn.Silu()
+        self.conv2d30 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=512)
+        self.silu30 = paddle.nn.Silu()
+        self.conv2d31 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu31 = paddle.nn.Silu()
+        self.pool2d0 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2)
+        self.pool2d1 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2)
+        self.pool2d2 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2)
+        self.conv2d32 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=1024)
+        self.silu32 = paddle.nn.Silu()
+        self.conv2d33 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu33 = paddle.nn.Silu()
+        self.conv2d34 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=512)
+        self.silu34 = paddle.nn.Silu()
+        self.conv2d35 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu35 = paddle.nn.Silu()
+        self.conv2d36 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu36 = paddle.nn.Silu()
+        self.conv2d37 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=512)
+        self.silu37 = paddle.nn.Silu()
+        self.conv2d38 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256)
+        self.silu38 = paddle.nn.Silu()
+        self.conv2d39 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256)
+        self.silu39 = paddle.nn.Silu()
+        self.conv2d40 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=256)
+        self.silu40 = paddle.nn.Silu()
+        self.conv2d41 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.silu41 = paddle.nn.Silu()
+        self.conv2d42 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu42 = paddle.nn.Silu()
+        self.conv2d43 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=256)
+        self.silu43 = paddle.nn.Silu()
+        self.conv2d44 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu44 = paddle.nn.Silu()
+        self.conv2d45 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu45 = paddle.nn.Silu()
+        self.conv2d46 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256)
+        self.silu46 = paddle.nn.Silu()
+        self.conv2d47 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128)
+        self.silu47 = paddle.nn.Silu()
+        self.conv2d48 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu48 = paddle.nn.Silu()
+        self.conv2d49 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256)
+        self.silu49 = paddle.nn.Silu()
+        self.conv2d50 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256)
+        self.silu50 = paddle.nn.Silu()
+        self.conv2d51 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256)
+        self.silu51 = paddle.nn.Silu()
+        self.conv2d52 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu52 = paddle.nn.Silu()
+        self.conv2d53 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256)
+        self.silu53 = paddle.nn.Silu()
+        self.conv2d54 = paddle.nn.Conv2D(padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256)
+        self.silu54 = paddle.nn.Silu()
+        self.conv2d55 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512)
+        self.silu55 = paddle.nn.Silu()
+        self.conv2d56 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=512)
+        self.silu56 = paddle.nn.Silu()
+        self.x731 = self.create_parameter(dtype='float32', shape=(1, 8400), default_initializer=paddle.nn.initializer.Constant(value=0.0))
+        self.conv2d57 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=128)
+        self.silu57 = paddle.nn.Silu()
+        self.conv2d58 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu58 = paddle.nn.Silu()
+        self.conv2d59 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.conv2d60 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=256)
+        self.silu59 = paddle.nn.Silu()
+        self.conv2d61 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu60 = paddle.nn.Silu()
+        self.conv2d62 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.conv2d63 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=512)
+        self.silu61 = paddle.nn.Silu()
+        self.conv2d64 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64)
+        self.silu62 = paddle.nn.Silu()
+        self.conv2d65 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64)
+        self.conv2d66 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu63 = paddle.nn.Silu()
+        self.conv2d67 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu64 = paddle.nn.Silu()
+        self.conv2d68 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128)
+        self.conv2d69 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=256)
+        self.silu65 = paddle.nn.Silu()
+        self.conv2d70 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu66 = paddle.nn.Silu()
+        self.conv2d71 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128)
+        self.conv2d72 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=512)
+        self.silu67 = paddle.nn.Silu()
+        self.conv2d73 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128)
+        self.silu68 = paddle.nn.Silu()
+        self.conv2d74 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128)
+        self.softmax0 = paddle.nn.Softmax(axis=1)
+        self.conv2d75 = paddle.nn.Conv2D(out_channels=1, kernel_size=(1, 1), bias_attr=False, in_channels=16)
+        self.x949 = self.create_parameter(dtype='float32', shape=(1, 2, 8400), default_initializer=paddle.nn.initializer.Constant(value=0.0))
+        self.sigmoid0 = paddle.nn.Sigmoid()
+
+    def forward(self, x0):
+        x49 = self.conv2d0(x0)
+        x50 = self.silu0(x49)
+        x61 = self.conv2d1(x50)
+        x62 = self.silu1(x61)
+        x77 = self.conv2d2(x62)
+        x78 = self.silu2(x77)
+        x89 = self.conv2d3(x78)
+        x90 = self.silu3(x89)
+        x98 = self.conv2d4(x90)
+        x99 = self.silu4(x98)
+        x100 = x78 + x99
+        x108 = self.conv2d5(x62)
+        x109 = self.silu5(x108)
+        x110 = [x100, x109]
+        x111 = paddle.concat(x=x110, axis=1)
+        x119 = self.conv2d6(x111)
+        x120 = self.silu6(x119)
+        x131 = self.conv2d7(x120)
+        x132 = self.silu7(x131)
+        x147 = self.conv2d8(x132)
+        x148 = self.silu8(x147)
+        x160 = self.conv2d9(x148)
+        x161 = self.silu9(x160)
+        x169 = self.conv2d10(x161)
+        x170 = self.silu10(x169)
+        x171 = x148 + x170
+        x181 = self.conv2d11(x171)
+        x182 = self.silu11(x181)
+        x190 = self.conv2d12(x182)
+        x191 = self.silu12(x190)
+        x192 = x171 + x191
+        x200 = self.conv2d13(x132)
+        x201 = self.silu13(x200)
+        x202 = [x192, x201]
+        x203 = paddle.concat(x=x202, axis=1)
+        x211 = self.conv2d14(x203)
+        x212 = self.silu14(x211)
+        x223 = self.conv2d15(x212)
+        x224 = self.silu15(x223)
+        x239 = self.conv2d16(x224)
+        x240 = self.silu16(x239)
+        x253 = self.conv2d17(x240)
+        x254 = self.silu17(x253)
+        x262 = self.conv2d18(x254)
+        x263 = self.silu18(x262)
+        x264 = x240 + x263
+        x274 = self.conv2d19(x264)
+        x275 = self.silu19(x274)
+        x283 = self.conv2d20(x275)
+        x284 = self.silu20(x283)
+        x285 = x264 + x284
+        x295 = self.conv2d21(x285)
+        x296 = self.silu21(x295)
+        x304 = self.conv2d22(x296)
+        x305 = self.silu22(x304)
+        x306 = x285 + x305
+        x314 = self.conv2d23(x224)
+        x315 = self.silu23(x314)
+        x316 = [x306, x315]
+        x317 = paddle.concat(x=x316, axis=1)
+        x325 = self.conv2d24(x317)
+        x326 = self.silu24(x325)
+        x337 = self.conv2d25(x326)
+        x338 = self.silu25(x337)
+        x353 = self.conv2d26(x338)
+        x354 = self.silu26(x353)
+        x365 = self.conv2d27(x354)
+        x366 = self.silu27(x365)
+        x374 = self.conv2d28(x366)
+        x375 = self.silu28(x374)
+        x376 = x354 + x375
+        x384 = self.conv2d29(x338)
+        x385 = self.silu29(x384)
+        x386 = [x376, x385]
+        x387 = paddle.concat(x=x386, axis=1)
+        x395 = self.conv2d30(x387)
+        x396 = self.silu30(x395)
+        x409 = self.conv2d31(x396)
+        x410 = self.silu31(x409)
+        x415 = self.pool2d0(x410)
+        x420 = self.pool2d1(x415)
+        x425 = self.pool2d2(x420)
+        x426 = [x410, x415, x420, x425]
+        x427 = paddle.concat(x=x426, axis=1)
+        x435 = self.conv2d32(x427)
+        x436 = self.silu32(x435)
+        x447 = self.conv2d33(x436)
+        x448 = self.silu33(x447)
+        x450 = [2.0, 2.0]
+        x451 = paddle.nn.functional.interpolate(x=x448, scale_factor=x450, mode='nearest')
+        x453 = [x451, x326]
+        x454 = paddle.concat(x=x453, axis=1)
+        x469 = self.conv2d34(x454)
+        x470 = self.silu34(x469)
+        x481 = self.conv2d35(x470)
+        x482 = self.silu35(x481)
+        x490 = self.conv2d36(x482)
+        x491 = self.silu36(x490)
+        x499 = self.conv2d37(x454)
+        x500 = self.silu37(x499)
+        x501 = [x491, x500]
+        x502 = paddle.concat(x=x501, axis=1)
+        x510 = self.conv2d38(x502)
+        x511 = self.silu38(x510)
+        x522 = self.conv2d39(x511)
+        x523 = self.silu39(x522)
+        x525 = [2.0, 2.0]
+        x526 = paddle.nn.functional.interpolate(x=x523, scale_factor=x525, mode='nearest')
+        x528 = [x526, x212]
+        x529 = paddle.concat(x=x528, axis=1)
+        x544 = self.conv2d40(x529)
+        x545 = self.silu40(x544)
+        x556 = self.conv2d41(x545)
+        x557 = self.silu41(x556)
+        x565 = self.conv2d42(x557)
+        x566 = self.silu42(x565)
+        x574 = self.conv2d43(x529)
+        x575 = self.silu43(x574)
+        x576 = [x566, x575]
+        x577 = paddle.concat(x=x576, axis=1)
+        x585 = self.conv2d44(x577)
+        x586 = self.silu44(x585)
+        x597 = self.conv2d45(x586)
+        x598 = self.silu45(x597)
+        x600 = [x598, x523]
+        x601 = paddle.concat(x=x600, axis=1)
+        x616 = self.conv2d46(x601)
+        x617 = self.silu46(x616)
+        x628 = self.conv2d47(x617)
+        x629 = self.silu47(x628)
+        x637 = self.conv2d48(x629)
+        x638 = self.silu48(x637)
+        x646 = self.conv2d49(x601)
+        x647 = self.silu49(x646)
+        x648 = [x638, x647]
+        x649 = paddle.concat(x=x648, axis=1)
+        x657 = self.conv2d50(x649)
+        x658 = self.silu50(x657)
+        x669 = self.conv2d51(x658)
+        x670 = self.silu51(x669)
+        x672 = [x670, x448]
+        x673 = paddle.concat(x=x672, axis=1)
+        x688 = self.conv2d52(x673)
+        x689 = self.silu52(x688)
+        x700 = self.conv2d53(x689)
+        x701 = self.silu53(x700)
+        x709 = self.conv2d54(x701)
+        x710 = self.silu54(x709)
+        x718 = self.conv2d55(x673)
+        x719 = self.silu55(x718)
+        x720 = [x710, x719]
+        x721 = paddle.concat(x=x720, axis=1)
+        x729 = self.conv2d56(x721)
+        x730 = self.silu56(x729)
+        x731 = self.x731
+        x732 = 2
+        x733 = 2
+        x736 = 1
+        x762 = self.conv2d57(x586)
+        x763 = self.silu57(x762)
+        x771 = self.conv2d58(x763)
+        x772 = self.silu58(x771)
+        x779 = self.conv2d59(x772)
+        x781 = paddle.reshape(x=x779, shape=[1, 64, -1])
+        x792 = self.conv2d60(x658)
+        x793 = self.silu59(x792)
+        x801 = self.conv2d61(x793)
+        x802 = self.silu60(x801)
+        x809 = self.conv2d62(x802)
+        x811 = paddle.reshape(x=x809, shape=[1, 64, -1])
+        x822 = self.conv2d63(x730)
+        x823 = self.silu61(x822)
+        x831 = self.conv2d64(x823)
+        x832 = self.silu62(x831)
+        x839 = self.conv2d65(x832)
+        x841 = paddle.reshape(x=x839, shape=[1, 64, -1])
+        x842 = [x781, x811, x841]
+        x843 = paddle.concat(x=x842, axis=-1)
+        x854 = self.conv2d66(x586)
+        x855 = self.silu63(x854)
+        x863 = self.conv2d67(x855)
+        x864 = self.silu64(x863)
+        x871 = self.conv2d68(x864)
+        x873 = paddle.reshape(x=x871, shape=[1, 80, -1])
+        x884 = self.conv2d69(x658)
+        x885 = self.silu65(x884)
+        x893 = self.conv2d70(x885)
+        x894 = self.silu66(x893)
+        x901 = self.conv2d71(x894)
+        x903 = paddle.reshape(x=x901, shape=[1, 80, -1])
+        x914 = self.conv2d72(x730)
+        x915 = self.silu67(x914)
+        x923 = self.conv2d73(x915)
+        x924 = self.silu68(x923)
+        x931 = self.conv2d74(x924)
+        x933 = paddle.reshape(x=x931, shape=[1, 80, -1])
+        x934 = [x873, x903, x933]
+        x935 = paddle.concat(x=x934, axis=-1)
+        x938 = paddle.reshape(x=x843, shape=[1, 4, 16, 8400])
+        x939_shape = x938.shape
+        x939_len = len(x939_shape)
+        x939_list = []
+        for i in range(x939_len):
+            x939_list.append(i)
+        if x733 < 0:
+            x733_new = x733 + x939_len
+        else:
+            x733_new = x733
+        if x736 < 0:
+            x736_new = x736 + x939_len
+        else:
+            x736_new = x736
+        x939_list[x733_new] = x736_new
+        x939_list[x736_new] = x733_new
+        x939 = paddle.transpose(x=x938, perm=x939_list)
+        x940 = self.softmax0(x939)
+        x946 = self.conv2d75(x940)
+        x948 = paddle.reshape(x=x946, shape=[1, 4, 8400])
+        x949 = self.x949
+        x950 = paddle.split(x=x948, num_or_sections=2, axis=1)
+        x951, x952 = x950
+        x953 = x949 - x951
+        x954 = x949 + x952
+        x955 = x953 + x954
+        x956 = x955 / x732
+        x957 = x954 - x953
+        x958 = [x956, x957]
+        x959 = paddle.concat(x=x958, axis=1)
+        x960 = x959 * x731
+        x961 = self.sigmoid0(x935)
+        x962 = [x960, x961]
+        x963 = paddle.concat(x=x962, axis=1)
+        return x963
+
+def main(x0):
+    # There are 1 inputs.
+    # x0: shape-[1, 3, 640, 640], type-float32.
+    paddle.disable_static()
+    params = paddle.load(r'/work/models/yolo/yolov5su_paddle_model/model.pdparams')
+    model = DetectionModel()
+    model.set_dict(params, use_structured_name=True)
+    model.eval()
+    out = model(x0)
+    return out

yolov5su_saved_model/fingerprint.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26128e4181784b192e82b912e947dbdb5162398a48c00b4201f2a2a274b2ce0f
+size 76

yolov5su_saved_model/metadata.yaml

@@ -0,0 +1,101 @@
+description: Ultralytics YOLOv5s model trained on coco.yaml
+author: Ultralytics
+date: '2026-03-13T12:52:08.604912'
+version: 8.4.8
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  nms: false
+channels: 3
+end2end: false

yolov5su_saved_model/saved_model.pb

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+oid sha256:729eaa2d4eeda365d5e02569f4bd5a70abf9b23cc498a08d993300a36ea2b131
+size 36998135

yolov5su_saved_model/yolov5su_float16.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5ed66e27fc50a5ee2924b8386d0ef7c3e139a863401903b9b6719157d3f9d6c
+size 18464625

yolov5su_saved_model/yolov5su_float32.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a93e77ed4f0e080267ed68534cda94206389f51eda2debdcfe88bc96498d552
+size 36832425