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create metrics, report

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anetla 2023-11-28 16:21:45 +01:00
parent 8121a83606
commit 5b2770e624
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tomato_model/working_tomato.ipynb
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@ -883,6 +883,386 @@
"# Display all images with the specified size\n",
"display_images(image_paths)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.0.215 🚀 Python-3.9.18 torch-2.1.1+cpu CPU (11th Gen Intel Core(TM) i7-11800H 2.30GHz)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mScanning E:\\tomAIto\\model\\datasets\\val\\labels... 161 images, 0 backgrounds, 0 corrupt: 100%|██████████| 161/161 [00:00<00:00, 399.67it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: E:\\tomAIto\\model\\datasets\\val\\labels.cache\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [02:09<00:00, 11.81s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" all 161 1996 0.814 0.835 0.88 0.786\n",
" b_fully_ripened 161 72 0.799 0.778 0.854 0.788\n",
" b_half_ripened 161 116 0.78 0.819 0.844 0.765\n",
" b_green 161 387 0.861 0.928 0.956 0.847\n",
" l_fully_ripened 161 269 0.803 0.835 0.878 0.79\n",
" l_half_ripened 161 223 0.758 0.776 0.819 0.735\n",
" l_green 161 929 0.881 0.873 0.928 0.791\n",
"Speed: 2.2ms preprocess, 668.2ms inference, 0.0ms loss, 0.4ms postprocess per image\n",
"Results saved to \u001b[1mruns\\detect\\val2\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"ultralytics.utils.metrics.DetMetrics object with attributes:\n",
"\n",
"ap_class_index: array([0, 1, 2, 3, 4, 5])\n",
"box: ultralytics.utils.metrics.Metric object\n",
"confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x0000028F84334850>\n",
"curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
"curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n",
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" [ 0.99138, 0.99138, 0.99138, ..., 0, 0, 0],\n",
" [ 0.97933, 0.97933, 0.97416, ..., 0, 0, 0],\n",
" [ 0.96283, 0.96283, 0.96283, ..., 0, 0, 0],\n",
" [ 0.95516, 0.95516, 0.94619, ..., 0, 0, 0],\n",
" [ 0.95802, 0.95802, 0.95479, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n",
"fitness: 0.795417895242584\n",
"keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
"maps: array([ 0.78819, 0.7649, 0.84676, 0.79033, 0.73516, 0.79081])\n",
"names: {0: 'b_fully_ripened', 1: 'b_half_ripened', 2: 'b_green', 3: 'l_fully_ripened', 4: 'l_half_ripened', 5: 'l_green'}\n",
"plot: True\n",
"results_dict: {'metrics/precision(B)': 0.8135908045481376, 'metrics/recall(B)': 0.8347852733796678, 'metrics/mAP50(B)': 0.8799515738958958, 'metrics/mAP50-95(B)': 0.7860252642811049, 'fitness': 0.795417895242584}\n",
"save_dir: WindowsPath('runs/detect/val2')\n",
"speed: {'preprocess': 2.2406844618898, 'inference': 668.1798392941492, 'loss': 0.0, 'postprocess': 0.4184897641957917}\n",
"task: 'detect'"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.val(data='dataset.yaml')"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.0.215 🚀 Python-3.9.18 torch-2.1.1+cpu CPU (11th Gen Intel Core(TM) i7-11800H 2.30GHz)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mScanning E:\\tomAIto\\model\\datasets\\val\\labels.cache... 161 images, 0 backgrounds, 0 corrupt: 100%|██████████| 161/161 [00:00<?, ?it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [02:06<00:00, 11.49s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" all 161 1996 0.814 0.835 0.88 0.786\n",
" b_fully_ripened 161 72 0.799 0.778 0.854 0.788\n",
" b_half_ripened 161 116 0.78 0.819 0.844 0.765\n",
" b_green 161 387 0.861 0.928 0.956 0.847\n",
" l_fully_ripened 161 269 0.803 0.835 0.878 0.79\n",
" l_half_ripened 161 223 0.758 0.776 0.819 0.735\n",
" l_green 161 929 0.881 0.873 0.928 0.791\n",
"Speed: 2.1ms preprocess, 659.7ms inference, 0.0ms loss, 0.4ms postprocess per image\n",
"Results saved to \u001b[1mruns\\detect\\val3\u001b[0m\n"
]
}
],
"source": [
"metrics = model.val(data='dataset.yaml')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"precision = metrics.box.p\n",
"recall = metrics.box.r\n",
"f1 = metrics.box.f1"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.79877 0.77971 0.86094 0.80337 0.75786 0.8809]\n",
"[ 0.77778 0.81897 0.92784 0.83536 0.77578 0.87298]\n",
"[ 0.78813 0.79886 0.89314 0.81905 0.76672 0.87692]\n"
]
}
],
"source": [
"print(precision)\n",
"print(recall)\n",
"print(f1)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8809026853681999 0.9278379922911701 0.8931357127393529\n"
]
}
],
"source": [
"print(precision.max(), recall.max(), f1.max())"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.85426, 0.84396, 0.95612, 0.87801, 0.81905, 0.92831])"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics.box.ap50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some docs:\n",
"https://docs.ultralytics.com/guides/yolo-performance-metrics/#choosing-the-right-metrics\n"
]
}
],
"metadata": {