Object Detection
ultralytics
LiteRT
TensorBoard
Core ML
ONNX
English
YOLO11n
yolo
yolo11
yolo11n
fish
Eval Results (legacy)
Instructions to use akridge/yolo11-fish-detector-grayscale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use akridge/yolo11-fish-detector-grayscale with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("akridge/yolo11-fish-detector-grayscale") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 17,136 Bytes
739161d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
1, 1.2763, 2.0731, 1.1103, 0.39171, 0.66847, 0.4992, 0.34953, 1.1722, 2.1246, 0.99464, 0.0006595, 0.0006595, 0.0006595
2, 1.1943, 1.4568, 1.0833, 0.46562, 0.43106, 0.42633, 0.25528, 1.4379, 2.0806, 1.2331, 0.0012999, 0.0012999, 0.0012999
3, 1.2028, 1.3445, 1.0965, 0.70997, 0.5074, 0.55446, 0.35153, 1.2202, 1.96, 1.1122, 0.0019139, 0.0019139, 0.0019139
4, 1.1351, 1.1837, 1.0646, 0.70167, 0.70113, 0.76935, 0.53239, 1.0456, 1.2051, 1.0098, 0.0018812, 0.0018812, 0.0018812
5, 1.0996, 1.1097, 1.0563, 0.72763, 0.69471, 0.76234, 0.54874, 0.96527, 1.1152, 0.97886, 0.0018416, 0.0018416, 0.0018416
6, 1.0583, 1.0673, 1.0433, 0.77375, 0.69979, 0.81772, 0.55696, 1.1319, 1.0379, 1.0187, 0.001802, 0.001802, 0.001802
7, 1.0495, 1.0202, 1.0268, 0.6869, 0.56871, 0.5815, 0.37792, 1.3764, 1.6751, 1.1474, 0.0017624, 0.0017624, 0.0017624
8, 0.96519, 0.96867, 1.0014, 0.74016, 0.71666, 0.75587, 0.56732, 0.92046, 1.1781, 0.981, 0.0017228, 0.0017228, 0.0017228
9, 0.9665, 0.99234, 1.0135, 0.7452, 0.7759, 0.84065, 0.64321, 0.87944, 0.92422, 0.96226, 0.0016832, 0.0016832, 0.0016832
10, 0.92206, 0.92409, 0.98908, 0.75547, 0.73809, 0.8293, 0.62188, 0.927, 0.95513, 0.95996, 0.0016436, 0.0016436, 0.0016436
11, 0.92927, 0.93745, 0.9906, 0.73947, 0.67442, 0.72643, 0.53098, 0.96968, 1.0779, 0.98567, 0.001604, 0.001604, 0.001604
12, 0.9114, 0.88453, 0.98324, 0.76054, 0.76547, 0.84264, 0.67445, 0.73928, 0.84744, 0.91179, 0.0015644, 0.0015644, 0.0015644
13, 0.90561, 0.88458, 0.98387, 0.77756, 0.79075, 0.84277, 0.67926, 0.76233, 0.83764, 0.9093, 0.0015248, 0.0015248, 0.0015248
14, 0.86814, 0.87583, 0.96808, 0.75593, 0.64693, 0.71859, 0.56642, 0.82802, 1.0354, 0.94385, 0.0014852, 0.0014852, 0.0014852
15, 0.87301, 0.86266, 0.97365, 0.76401, 0.77344, 0.83151, 0.6736, 0.72999, 0.88011, 0.89402, 0.0014456, 0.0014456, 0.0014456
16, 0.86757, 0.84677, 0.97031, 0.83614, 0.76594, 0.8714, 0.70965, 0.73644, 0.76257, 0.90672, 0.001406, 0.001406, 0.001406
17, 0.85088, 0.8222, 0.96698, 0.77286, 0.72304, 0.82919, 0.60335, 0.93485, 1.0353, 0.98409, 0.0013664, 0.0013664, 0.0013664
18, 0.84592, 0.81221, 0.95839, 0.8518, 0.72909, 0.87409, 0.7138, 0.70348, 0.76687, 0.89074, 0.0013268, 0.0013268, 0.0013268
19, 0.83777, 0.80239, 0.96094, 0.75915, 0.85412, 0.87028, 0.71767, 0.68195, 0.73756, 0.88647, 0.0012872, 0.0012872, 0.0012872
20, 0.80897, 0.78522, 0.94692, 0.83022, 0.78436, 0.88147, 0.72052, 0.69901, 0.71581, 0.88779, 0.0012476, 0.0012476, 0.0012476
21, 0.7982, 0.77558, 0.9464, 0.76319, 0.82664, 0.86954, 0.70842, 0.74253, 0.79408, 0.8903, 0.001208, 0.001208, 0.001208
22, 0.80919, 0.78796, 0.94454, 0.79632, 0.7907, 0.86239, 0.69034, 0.76698, 0.77081, 0.89429, 0.0011684, 0.0011684, 0.0011684
23, 0.7753, 0.76175, 0.94198, 0.80268, 0.76533, 0.8588, 0.69326, 0.74555, 0.77058, 0.89773, 0.0011288, 0.0011288, 0.0011288
24, 0.78487, 0.75941, 0.93483, 0.83017, 0.79281, 0.87967, 0.71714, 0.73116, 0.73057, 0.885, 0.0010892, 0.0010892, 0.0010892
25, 0.74399, 0.72361, 0.92825, 0.79878, 0.84567, 0.90493, 0.76061, 0.66711, 0.69405, 0.87102, 0.0010496, 0.0010496, 0.0010496
26, 0.75373, 0.73823, 0.92637, 0.85032, 0.81668, 0.88987, 0.74141, 0.68613, 0.68211, 0.87216, 0.00101, 0.00101, 0.00101
27, 0.74609, 0.72624, 0.93325, 0.83744, 0.81685, 0.89875, 0.75743, 0.63484, 0.68894, 0.86403, 0.0009704, 0.0009704, 0.0009704
28, 0.75004, 0.73298, 0.92991, 0.8487, 0.83015, 0.91416, 0.75905, 0.66117, 0.66033, 0.86798, 0.0009308, 0.0009308, 0.0009308
29, 0.72169, 0.68738, 0.92472, 0.83707, 0.82241, 0.8992, 0.77063, 0.60134, 0.6557, 0.86308, 0.0008912, 0.0008912, 0.0008912
30, 0.7233, 0.70005, 0.92494, 0.83196, 0.82693, 0.9026, 0.75361, 0.66645, 0.6644, 0.87653, 0.0008516, 0.0008516, 0.0008516
31, 0.72331, 0.70268, 0.92379, 0.87548, 0.78785, 0.90653, 0.78293, 0.59064, 0.64436, 0.85785, 0.000812, 0.000812, 0.000812
32, 0.71755, 0.69223, 0.92182, 0.858, 0.83033, 0.91499, 0.76604, 0.6467, 0.67427, 0.86871, 0.0007724, 0.0007724, 0.0007724
33, 0.7112, 0.68316, 0.91777, 0.86119, 0.82633, 0.9086, 0.78004, 0.61656, 0.62613, 0.86254, 0.0007328, 0.0007328, 0.0007328
34, 0.70462, 0.68982, 0.9138, 0.84876, 0.82241, 0.9039, 0.76015, 0.64069, 0.64797, 0.86169, 0.0006932, 0.0006932, 0.0006932
35, 0.71388, 0.69105, 0.92278, 0.84884, 0.82452, 0.89739, 0.75162, 0.67021, 0.68167, 0.86842, 0.0006536, 0.0006536, 0.0006536
36, 0.69027, 0.66023, 0.91401, 0.8751, 0.82951, 0.92599, 0.80399, 0.57174, 0.59296, 0.84997, 0.000614, 0.000614, 0.000614
37, 0.67137, 0.64636, 0.90292, 0.84206, 0.86796, 0.92428, 0.78984, 0.62333, 0.61295, 0.85273, 0.0005744, 0.0005744, 0.0005744
38, 0.66001, 0.64, 0.90112, 0.87244, 0.79527, 0.91038, 0.77631, 0.6154, 0.63145, 0.85273, 0.0005348, 0.0005348, 0.0005348
39, 0.65865, 0.63608, 0.90891, 0.83852, 0.85628, 0.92219, 0.78984, 0.60736, 0.58547, 0.85321, 0.0004952, 0.0004952, 0.0004952
40, 0.64706, 0.61554, 0.90311, 0.86745, 0.8351, 0.91816, 0.8109, 0.53077, 0.5846, 0.84312, 0.0004556, 0.0004556, 0.0004556
41, 0.59104, 0.58174, 0.86552, 0.86876, 0.8537, 0.92769, 0.81938, 0.52493, 0.56464, 0.84173, 0.000416, 0.000416, 0.000416
42, 0.56211, 0.57194, 0.86568, 0.83428, 0.84778, 0.92814, 0.82878, 0.51491, 0.6014, 0.84183, 0.0003764, 0.0003764, 0.0003764
43, 0.54075, 0.55372, 0.85138, 0.83345, 0.84637, 0.92218, 0.82391, 0.51033, 0.5811, 0.84333, 0.0003368, 0.0003368, 0.0003368
44, 0.54138, 0.54247, 0.85256, 0.8776, 0.84884, 0.93206, 0.83682, 0.48998, 0.55087, 0.83787, 0.0002972, 0.0002972, 0.0002972
45, 0.53068, 0.53446, 0.84936, 0.82342, 0.87104, 0.93094, 0.83179, 0.49316, 0.56156, 0.83831, 0.0002576, 0.0002576, 0.0002576
46, 0.52698, 0.52525, 0.85387, 0.87812, 0.83778, 0.93411, 0.83454, 0.49506, 0.5506, 0.83781, 0.000218, 0.000218, 0.000218
47, 0.52316, 0.52866, 0.85468, 0.89652, 0.84989, 0.93913, 0.84632, 0.48306, 0.52852, 0.8363, 0.0001784, 0.0001784, 0.0001784
48, 0.50211, 0.51614, 0.84839, 0.88866, 0.85412, 0.93777, 0.84818, 0.46924, 0.52413, 0.83488, 0.0001388, 0.0001388, 0.0001388
49, 0.50147, 0.49958, 0.84159, 0.88482, 0.86079, 0.93745, 0.85261, 0.4589, 0.51889, 0.83284, 9.92e-05, 9.92e-05, 9.92e-05
50, 0.49021, 0.48838, 0.84385, 0.88992, 0.86892, 0.93716, 0.85232, 0.46175, 0.51644, 0.832, 5.96e-05, 5.96e-05, 5.96e-05
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