computer vision examples

own_nn/YOLO_dataset/data.yaml

@@ -0,0 +1,6 @@
+names:
+- fivefingers
+nc: 1
+test: validation/images
+train: train/images
+val: validation/images

own_nn/YOLO_dataset/train.py

@@ -0,0 +1,7 @@
+from ultralytics import YOLO
+
+# Load an official or custom model
+#model = YOLO('yolov8s-seg.pt')  # Load an official Detect model
+
+model = YOLO('fivefingers-seg-100.pt')  # Load an official Detect model
+results = model.train(data='data.yaml', epochs=100, imgsz=640, batch=8)

own_nn/YOLO_dataset/train/labels/2024-05-02-171016.txt

@@ -0,0 +1 @@
+0 0.066102 0.506083 0.098984 0.461569 0.134328 0.427542 0.182430 0.362097 0.242312 0.306250 0.285992 0.279208 0.327227 0.254764 0.345875 0.209389 0.371398 0.160528 0.426867 0.078500 0.458766 0.041861 0.479383 0.033125 0.497055 0.045347 0.503930 0.065417 0.508344 0.086361 0.503437 0.111667 0.490672 0.134347 0.445516 0.198056 0.426375 0.230333 0.417539 0.255639 0.397414 0.284444 0.383180 0.321958 0.382688 0.334181 0.398891 0.341153 0.434227 0.301889 0.464172 0.273097 0.546633 0.224222 0.601109 0.183222 0.626148 0.179722 0.639891 0.200667 0.646266 0.230333 0.643813 0.261750 0.635961 0.284444 0.619766 0.297528 0.561844 0.326319 0.528469 0.352500 0.480367 0.387403 0.445516 0.412708 0.438648 0.428417 0.456313 0.426667 0.527484 0.386528 0.569211 0.364722 0.624180 0.330681 0.663445 0.305375 0.681117 0.304514 0.700750 0.346389 0.695844 0.397875 0.671789 0.419694 0.619766 0.453722 0.600625 0.465944 0.547609 0.499972 0.503930 0.517417 0.463187 0.534875 0.467602 0.566292 0.526508 0.561056 0.548102 0.555819 0.570680 0.561931 0.607000 0.546222 0.636945 0.537486 0.671305 0.533125 0.684555 0.551458 0.689953 0.592472 0.686023 0.612542 0.662961 0.637847 0.632523 0.652681 0.603078 0.662278 0.580984 0.668389 0.552516 0.676236 0.537797 0.679722 0.519141 0.689319 0.454844 0.687583 0.412633 0.708528 0.400359 0.715500 0.383180 0.732958 0.368945 0.757389 0.375328 0.773097 0.402813 0.779208 0.437664 0.781819 0.466625 0.789667 0.501469 0.814111 0.523070 0.855986 0.529937 0.893514 0.525031 0.919694 0.517180 0.939764 0.503930 0.952847 0.487727 0.953722 0.459258 0.938889 0.430789 0.927542 0.411648 0.917944 0.368453 0.901361 0.334094 0.893514 0.314461 0.893514 0.289430 0.879556 0.254578 0.868208 0.217273 0.849014 0.193227 0.830681 0.177516 0.805375 0.162797 0.764361 0.153961 0.744292 0.133836 0.729458 0.111750 0.730333

own_nn/YOLO_dataset/train/labels/2024-05-02-171040.txt

@@ -0,0 +1 @@
+0 0.186789 0.726861 0.210172 0.673903 0.247258 0.506208 0.277086 0.372917 0.287164 0.327764 0.281922 0.256111 0.288773 0.247500 0.300063 0.246069 0.308930 0.253236 0.320219 0.274667 0.326266 0.306194 0.331906 0.335653 0.327070 0.369264 0.323039 0.397208 0.315781 0.441708 0.316992 0.459625 0.358109 0.440931 0.387133 0.409389 0.418578 0.365681 0.444781 0.332069 0.458484 0.322750 0.469773 0.316306 0.490734 0.290431 0.517742 0.269722 0.530641 0.266069 0.541930 0.269722 0.549180 0.283333 0.552008 0.304833 0.549180 0.319889 0.541930 0.333431 0.493953 0.373556 0.473398 0.403014 0.456062 0.430958 0.439133 0.458194 0.445984 0.467514 0.466547 0.443861 0.492750 0.418778 0.517742 0.403736 0.563289 0.382236 0.583852 0.373556 0.594328 0.368611 0.608437 0.372917 0.615695 0.388681 0.617305 0.404444 0.616500 0.423792 0.612070 0.433833 0.592313 0.445222 0.552813 0.469667 0.535477 0.482569 0.482672 0.523417 0.462516 0.535528 0.462914 0.542042 0.466547 0.551361 0.476625 0.549208 0.508469 0.527000 0.531039 0.516958 0.565711 0.503986 0.590297 0.482569 0.601187 0.478986 0.610859 0.481847 0.619320 0.489722 0.624164 0.511875 0.620938 0.534875 0.616102 0.547056 0.592719 0.560681 0.563695 0.579236 0.542328 0.594361 0.518141 0.614417 0.482672 0.625889 0.470578 0.629472 0.470977 0.650903 0.486297 0.657417 0.510484 0.662361 0.550797 0.666667 0.579016 0.666667 0.591109 0.676056 0.595141 0.698194 0.589492 0.719764 0.581836 0.732667 0.564102 0.735542 0.448000 0.740556 0.424625 0.758389 0.393984 0.787139 0.379477 0.798597 0.362945 0.813583 0.333922 0.819375 0.317797 0.829347 0.303687 0.842319 0.298047 0.852347 0.280313 0.893917

own_nn/YOLO_dataset/train/labels/2024-05-02-171052.txt

@@ -0,0 +1 @@
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own_nn/YOLO_dataset/train/labels/2024-05-02-171111.txt

@@ -0,0 +1 @@
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own_nn/YOLO_dataset/train/labels/2024-05-02-171120.txt

@@ -0,0 +1,2 @@
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own_nn/YOLO_dataset/train/labels/2024-05-02-171126.txt

@@ -0,0 +1,2 @@
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own_nn/YOLO_dataset/train/labels/2024-05-02-171210.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171227.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171246.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171251.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171300.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171306.txt

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own_nn/YOLO_dataset/train/labels/2024-05-02-171312.txt

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