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zenon
2023-12-28 16:12:31 +01:00
parent a6ed18303e
commit 2a29b414b1
12 changed files with 8674 additions and 3668 deletions
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classification/evaluation/detection.py
-11
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@@ -63,7 +63,6 @@ def detect_yolo_only(img_path: str, yolo_path: str):
:param str yolo_path: path to yolo weights
:returns: tuple of recent image and dict of bounding boxes and
their predictions
"""
img = cv2.imread(img_path)
@@ -112,13 +111,6 @@ def classify(resnet_path, img):
session.run_with_iobinding(io_binding)
out = torch.tensor(io_binding.copy_outputs_to_cpu()[0])
# Do inference
# session = onnxruntime.InferenceSession(resnet_path)
# outname = [i.name for i in session.get_outputs()]
# inname = [i.name for i in session.get_inputs()]
# inp = {inname[0]: batch.numpy()}
# out = torch.tensor(np.array(session.run(outname, inp)))[0]
# Apply softmax to get percentage confidence of classes
out = torch.nn.functional.softmax(out, dim=1)[0] * 100
return out
@@ -201,12 +193,9 @@ def get_boxes(yolo_path, image):
session.run_with_iobinding(io_binding)
outs = torch.tensor(io_binding.copy_outputs_to_cpu()[0])
# out = torch.tensor(np.array(session.run(outname, inp)))[0]
# print(out.shape)
# Apply NMS to results
preds_nms = apply_nms([outs])[0]
#preds_nms = outs
# Convert boxes from resized img to original img
xyxy_boxes = preds_nms[:, [1, 2, 3, 4]] # xmin, ymin, xmax, ymax
classification/evaluation/eval-test-model.ipynb
+3 -276
View File
@@ -1,64 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "945c9b80",
"metadata": {},
"source": [
"# Table of contents\n",
"1. [Introduction](#introduction)\n",
"2. [Aggregate Model Evaluation](#modelevaluation)\n",
" 1. [Loading the dataset](#modeload)\n",
" 2. [Perform detections](#modeldetect)\n",
" 3. [Evaluate detections](#modeldetectionseval)\n",
" 4. [Calculate results and plot them](#modelshowresults)\n",
" 5. [View dataset in fiftyone](#modelfiftyonesession)"
]
},
{
"cell_type": "markdown",
"id": "01339680",
"metadata": {},
"source": [
"## Introduction <a name=\"introduction\"></a>\n",
"\n",
"This notebook loads the test dataset in YOLOv5 format from disk and evaluates the model's performance."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ff25695e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.13) or chardet (5.1.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n",
" RequestsDependencyWarning)\n"
]
}
],
"source": [
"import fiftyone as fo\n",
"from PIL import Image\n",
"from detection import detect\n",
"from detection import detect_yolo_only"
]
},
{
"cell_type": "markdown",
"id": "86a5e832",
"metadata": {},
"source": [
"## Aggregate Model Evaluation <a name=\"modelevaluation\"></a>\n",
"\n",
"First, load the dataset from the directory containing the images and the labels in YOLOv5 format.\n",
"\n",
"### Loading the dataset <a name=\"modeload\"></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -237,76 +178,6 @@
"The code for the LaTeX table of the classification report can be printed by first converting the results to a pandas DataFrame and then calling the `to_latex()` method of the DataFrame. This code can then be inserted into the LaTeX document."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f7ad63b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\begin{tabular}{lrrrr}\n",
"\\toprule\n",
"{} & precision & recall & f1-score & support \\\\\n",
"\\midrule\n",
"Healthy & 0.679 & 0.525 & 0.592 & 766.0 \\\\\n",
"Stressed & 0.646 & 0.447 & 0.529 & 494.0 \\\\\n",
"micro avg & 0.667 & 0.494 & 0.568 & 1260.0 \\\\\n",
"macro avg & 0.663 & 0.486 & 0.560 & 1260.0 \\\\\n",
"weighted avg & 0.666 & 0.494 & 0.567 & 1260.0 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n",
"0.3374377395168513\n"
]
}
],
"source": [
"results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"# YOLO second hyp with Resnet optimized and relabeled dataset\n",
"print(results.mAP())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d73cca50",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\begin{tabular}{lrrrr}\n",
"\\toprule\n",
"{} & precision & recall & f1-score & support \\\\\n",
"\\midrule\n",
"Healthy & 0.653 & 0.604 & 0.628 & 766.0 \\\\\n",
"Stressed & 0.566 & 0.492 & 0.527 & 494.0 \\\\\n",
"micro avg & 0.620 & 0.560 & 0.589 & 1260.0 \\\\\n",
"macro avg & 0.610 & 0.548 & 0.577 & 1260.0 \\\\\n",
"weighted avg & 0.619 & 0.560 & 0.588 & 1260.0 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n",
"0.36171308664990176\n"
]
}
],
"source": [
"results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"# YOLO original with Resnet optimized and relabeled dataset\n",
"print(results.mAP())"
]
},
{
"cell_type": "code",
"execution_count": 14,
@@ -338,7 +209,7 @@
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"# YOLO optimized with Resnet optimized and relabeled dataset\n",
"# YOLO original with Resnet original and relabeled dataset\n",
"print(results.mAP())"
]
},
@@ -373,154 +244,10 @@
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"# YOLO original with Resnet original and relabeled dataset\n",
"# YOLO optimized with Resnet optimized and relabeled dataset\n",
"print(results.mAP())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b14d2b25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\begin{tabular}{lrrrr}\n",
"\\toprule\n",
"{} & precision & recall & f1-score & support \\\\\n",
"\\midrule\n",
"Healthy & 0.841 & 0.759 & 0.798 & 663.0 \\\\\n",
"Stressed & 0.726 & 0.810 & 0.766 & 484.0 \\\\\n",
"micro avg & 0.786 & 0.780 & 0.783 & 1147.0 \\\\\n",
"macro avg & 0.784 & 0.784 & 0.782 & 1147.0 \\\\\n",
"weighted avg & 0.793 & 0.780 & 0.784 & 1147.0 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
]
}
],
"source": [
"results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"# YOLO original with Resnet original and new dataset"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "900e9014",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\begin{tabular}{lrrrr}\n",
"\\toprule\n",
"{} & precision & recall & f1-score & support \\\\\n",
"\\midrule\n",
"Healthy & 0.674 & 0.721 & 0.696 & 662.0 \\\\\n",
"Stressed & 0.616 & 0.543 & 0.577 & 488.0 \\\\\n",
"micro avg & 0.652 & 0.645 & 0.649 & 1150.0 \\\\\n",
"macro avg & 0.645 & 0.632 & 0.637 & 1150.0 \\\\\n",
"weighted avg & 0.649 & 0.645 & 0.646 & 1150.0 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n",
"0.49320073714096757\n"
]
}
],
"source": [
"results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
"\n",
"# Export DataFrame to LaTeX tabular environment\n",
"print(results_df.to_latex())\n",
"print(results.mAP())\n",
"# YOLO original and Resnet final with old dataset"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "24df35b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" Healthy 0.82 0.74 0.78 662\n",
" Stressed 0.71 0.78 0.74 488\n",
"\n",
" micro avg 0.77 0.76 0.76 1150\n",
" macro avg 0.77 0.76 0.76 1150\n",
"weighted avg 0.77 0.76 0.77 1150\n",
"\n",
"0.6225848121901868\n"
]
}
],
"source": [
"# Print a classification report for all classes\n",
"results.print_report()\n",
"\n",
"print(results.mAP())\n",
"# YOLO original and Resnet original with old dataset"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a6bb272a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" Healthy 0.66 0.64 0.65 662\n",
" Stressed 0.68 0.54 0.60 488\n",
"\n",
" micro avg 0.67 0.60 0.63 1150\n",
" macro avg 0.67 0.59 0.63 1150\n",
"weighted avg 0.67 0.60 0.63 1150\n",
"\n",
"0.44258882390400406\n",
"\\begin{tabular}{lrrrr}\n",
"\\toprule\n",
"{} & precision & recall & f1-score & support \\\\\n",
"\\midrule\n",
"Healthy & 0.664 & 0.640 & 0.652 & 662.0 \\\\\n",
"Stressed & 0.680 & 0.539 & 0.601 & 488.0 \\\\\n",
"micro avg & 0.670 & 0.597 & 0.631 & 1150.0 \\\\\n",
"macro avg & 0.672 & 0.590 & 0.626 & 1150.0 \\\\\n",
"weighted avg & 0.670 & 0.597 & 0.630 & 1150.0 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
]
}
],
"source": [
"# Print a classification report for all classes\n",
"results.print_report()\n",
"results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
"\n",
"print(results.mAP())\n",
"print(results_df.to_latex())\n",
"# YOLO final and Resnet final with old dataset"
]
},
{
"cell_type": "code",
"execution_count": 35,
@@ -676,7 +403,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.15"
"version": "3.10.13"
}
},
"nbformat": 4,
classification/evaluation/eval-test-yolo.ipynb
+1 -1
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@@ -569,7 +569,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.15"
"version": "3.10.13"
}
},
"nbformat": 4,