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MosquitoAlert Challenge 2023

MosquitoAlert - YoloV5 Baseline Submission

MosquitoAlert - YoloV5 Baseline Submission

dipam

MosquitoAlert - YoloV5 Baseline Submission

This notebook will contains instructions and example submission with yolov5 trained on the dataset.

Installations 🤖

  1. aicrowd-cli for downloading challenge data and making submissions
  2. pyarrow for saving to parquet for submissions
In [18]:
!pip install -U ultralytics tqdm opencv-python torchvision pandas Pillow gdown aicrowd-cli
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In [1]:
import torch
import os
from pathlib import Path
import cv2
from timeit import default_timer as timer
import pandas as pd
from tqdm.auto import tqdm
from PIL import Image

Download Dataset and Models

In [3]:
!aicrowd login
Please login here: https://api.aicrowd.com/auth/ks-kKYlf2rj3hWkSkWwYQxho8Heu6oJs_cn-Qs-wDFI
/usr/bin/xdg-open: 869: www-browser: not found
/usr/bin/xdg-open: 869: links2: not found
/usr/bin/xdg-open: 869: elinks: not found
/usr/bin/xdg-open: 869: links: not found
/usr/bin/xdg-open: 869: lynx: not found
/usr/bin/xdg-open: 869: w3m: not found
xdg-open: no method available for opening 'https://api.aicrowd.com/auth/ks-kKYlf2rj3hWkSkWwYQxho8Heu6oJs_cn-Qs-wDFI'
API Key valid
Gitlab access token valid
Saved details successfully!
In [10]:
!aicrowd dataset download --challenge mosquitoalert-challenge-2023
sample_submission_phase1_v2.csv: 100% 359k/359k [00:00<00:00, 3.75MB/s]
test_images_phase1.zip: 100% 2.74G/2.74G [05:00<00:00, 9.14MB/s]
test_phase1_v2.csv: 100% 138k/138k [00:00<00:00, 2.11MB/s]
train.csv: 100% 629k/629k [00:00<00:00, 4.86MB/s]
train_images.zip: 100% 8.20G/8.20G [09:50<00:00, 13.9MB/s]
In [11]:
!unzip -qq test_images_phase1.zip -d test_images_phase1/
In [12]:
# Downloading the weight file at https://drive.google.com/file/d/1s7EoK3V9YTwQOxhuO1ERABRZ95atz8PS/view
!gdown "1s7EoK3V9YTwQOxhuO1ERABRZ95atz8PS"
Downloading...
From: https://drive.google.com/uc?id=1s7EoK3V9YTwQOxhuO1ERABRZ95atz8PS
To: /content/mosquitoalert-yolov5-baseline.pt
100% 14.4M/14.4M [00:00<00:00, 75.9MB/s]
In [4]:
TEST_DATA_DIR = 'test_images_phase1/'
In [5]:
def classify_image(image):
    image_information = {}
    result = yolov5_model(image)
    result_df = result.pandas().xyxy[0]
    if result_df.empty:
        print('No results from yolov5 model!')
    else:
        image_information = result_df.to_dict()
    return image_information

# getting mosquito_class name from predicted result
def extract_predicted_mosquito_class_name(extractedInformation):
    mosquito_class = ""
    if extractedInformation is not None:
        mosquito_class = str(extractedInformation.get("name").get(0))
    return mosquito_class

# getting mosquito_class number from predicted result
def extract_predicted_mosquito_class_number(extractedInformation):
    mosquito_class = ""
    if extractedInformation is not None:
        mosquito_class = str(extractedInformation.get("class").get(0))
    return mosquito_class

# getting mosquito_class confidence score from predicted result
def extract_predicted_mosquito_class_confidence(extractedInformation):
    mosquito_class = ""
    if extractedInformation is not None:
        mosquito_class = str(extractedInformation.get("confidence").get(0))
    return mosquito_class

# getting mosquito bounding box from predicted result
# it looks like the results are in different notation
# than the uoutput file
# Pascal VOC? (top, left, bottom, right)?
def extract_predicted_mosquito_bbox(extractedInformation):
    bbox = []
    if extractedInformation is not None:
        xmin = str(extractedInformation.get("xmin").get(0))
        ymin = str(extractedInformation.get("ymin").get(0))
        xmax = str(extractedInformation.get("xmax").get(0))
        ymax = str(extractedInformation.get("ymax").get(0))
        bbox = [xmin, ymin, xmax, ymax]
    return bbox

# find image id
def find_image_id(original_image):
    image_name = os.path.splitext(original_image)[0]
    return image_name
In [6]:
# path to dataset
root_images = os.path.join(TEST_DATA_DIR)

all_images = os.listdir(root_images)
print(f"Total images: {len(all_images)}")

class_labels = {
    "aegypti":      0,
    "albopictus":   1,
    "anopheles":    2,
    "culex":        3,
    "culiseta":     4,
    "japonicus/koreicus": 5
}

# counter for correctly recognized mosquito classes
counter = 0
labels_counter = 0
no_mosquito_detected = 0
rows = []

# yolov5 challenge trained baseline
trained_model_pretrained = "mosquitoalert-yolov5-baseline.pt"

# yolov5_model
# Colab gets an error on this -> Use Runtime -> Restart Session (The data will not be deleted)
yolov5_model = torch.hub.load('ultralytics/yolov5', 'custom', path = trained_model_pretrained, force_reload = True)

for original_image in tqdm(all_images):
    try:
        original_image_file = os.path.join(root_images, original_image)
        # checking if it is a file
        if os.path.isfile(original_image_file):
            # opening testing image
            # print(f'You are watching: {original_image}')
            # classifying image by yolov5 model
            predictedInformation = classify_image(original_image_file)
            mosquito_class_name_predicted = ""
            mosquito_class_number_predicted = ""
            mosquito_class_confidence = 0
            mosquito_class_bbox = [0, 0, 0, 0]

            if predictedInformation:
                mosquito_class_name_predicted = extract_predicted_mosquito_class_name(predictedInformation)
                mosquito_class_number_predicted = extract_predicted_mosquito_class_number(predictedInformation)
                mosquito_class_confidence = extract_predicted_mosquito_class_confidence(predictedInformation)
                mosquito_class_bbox = extract_predicted_mosquito_bbox(predictedInformation)
                counter += 1
            else:
                no_mosquito_detected += 1
            # print(f"Predicted mosquito class: {mosquito_class_name_predicted} with {float(mosquito_class_confidence):.2f} confidence score.")
            #  bbox = [xmin, ymin, xmax, ymax]
            # create row for csv file
            row = [original_image, mosquito_class_name_predicted, mosquito_class_number_predicted, mosquito_class_confidence,
                   mosquito_class_bbox[0], mosquito_class_bbox[1], mosquito_class_bbox[2], mosquito_class_bbox[3]]
            rows.append(row)
            labels_counter += 1
            # print(f"Finished file reading, file {original_image} read correctly!")
    except Exception as e:
        print(f'Unable to process file: {original_image}!')
        print(f'Exception: {e}!')
Total images: 2676
Downloading: "https://github.com/ultralytics/yolov5/zipball/master" to /root/.cache/torch/hub/master.zip
requirements: Ultralytics requirement "gitpython>=3.1.30" not found, attempting AutoUpdate...
Collecting gitpython>=3.1.30
  Downloading GitPython-3.1.31-py3-none-any.whl (184 kB)
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  Attempting uninstall: gitpython
    Found existing installation: GitPython 3.1.18
    Uninstalling GitPython-3.1.18:
      Successfully uninstalled GitPython-3.1.18
Successfully installed gitpython-3.1.31

requirements: 1 package updated per /root/.cache/torch/hub/ultralytics_yolov5_master/requirements.txt
requirements: ⚠️ Restart runtime or rerun command for updates to take effect

YOLOv5 🚀 2023-6-28 Python-3.10.12 torch-2.0.1+cu118 CPU

Fusing layers... 
Model summary: 157 layers, 7026307 parameters, 0 gradients
Adding AutoShape... 
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
No results from yolov5 model!
In [7]:
df = pd.DataFrame(rows, columns=["img_fName", "class_label", "predicted_class_number", "confidence_score", "bbx_xtl", "bbx_ytl", "bbx_xbr", "bbx_ybr"])
sub_df = df.drop(['predicted_class_number', 'confidence_score'], axis=1, inplace=False)
sub_df.head()
Out[7]:
img_fName class_label bbx_xtl bbx_ytl bbx_xbr bbx_ybr
0 751b3f5c-3031-4312-aede-b164889610fc.jpeg albopictus 246.53277587890625 189.07186889648438 476.0340270996094 413.14471435546875
1 b21d1a3c-f417-41f2-abff-8565fb48f008.jpeg culex 352.280029296875 375.5829162597656 463.6744689941406 489.3329162597656
2 4d2b9f10-614c-47ed-8126-ed3f0c58cd76.jpeg albopictus 940.1656494140625 1120.868896484375 1883.7869873046875 1953.6861572265625
3 013aefb4-829a-445c-8b52-59a52badb050.jpeg japonicus-koreicus 1356.7244873046875 1233.9964599609375 2903.752197265625 2074.76025390625
4 6c3a2882-a266-4d59-b014-904bfbc9c3fd.jpeg albopictus 392.6656494140625 355.2624816894531 699.4199829101562 625.9489135742188
In [8]:
sub_df.to_csv('submission_phase1.csv', index=False)
In [9]:
!aicrowd submission create -c mosquitoalert-challenge-2023 -f "submission_phase1.csv"
submission_phase1.csv ━━━━━━━━━━━━ 35.4%114.7/323.6  713.3 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 38.0%122.9/323.6  754.2 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 40.5%131.1/323.6  797.3 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 43.0%139.3/323.6  839.2 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 45.6%147.5/323.6  880.7 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 48.1%155.6/323.6  921.3 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 50.6%163.8/323.6  961.1 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 53.2%172.0/323.6  999.8 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 53.2%172.0/323.6  999.8 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 55.7%180.2/323.6  597.4 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 58.2%188.4/323.6  619.6 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 60.8%196.6/323.6  641.5 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 63.3%204.8/323.6  664.4 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 65.8%213.0/323.6  686.6 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 68.4%221.2/323.6  709.4 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 70.9%229.4/323.6  730.0 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 73.4%237.6/323.6  751.5 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 76.0%245.8/323.6  773.2 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 78.5%254.0/323.6  794.7 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 81.0%262.1/323.6  816.1 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 81.0%262.1/323.6  816.1 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 83.5%270.3/323.6  609.4 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━ 86.1%278.5/323.6  623.5 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━ 86.1%278.5/323.6  623.5 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━ 88.6%286.7/323.6  635.8 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━ 91.1%294.9/323.6  651.3 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 93.7%303.1/323.6  666.9 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 96.2%311.3/323.6  682.2 kB/s0:00:01
submission_phase1.csv ━━━━━━━━━━━━ 98.7%319.5/323.6  697.5 kB/s0:00:01
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submission_phase1.csv ━━━━━━━━━━━━ 100.0%325.2/323.6  707.3 kB/s0:00:00
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                                            KB                                  
                                             ╭─────────────────────────╮                                              
                                             │ Successfully submitted! │                                              
                                             ╰─────────────────────────╯                                              
                                                   Important links                                                    
┌──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/mosquitoalert-challenge-2023/submissions/235641              │
│                  │                                                                                                 │
│  All submissions │ https://www.aicrowd.com/challenges/mosquitoalert-challenge-2023/submissions?my_submissions=true │
│                  │                                                                                                 │
│      Leaderboard │ https://www.aicrowd.com/challenges/mosquitoalert-challenge-2023/leaderboards                    │
│                  │                                                                                                 │
│ Discussion forum │ https://discourse.aicrowd.com/c/mosquitoalert-challenge-2023                                    │
│                  │                                                                                                 │
│   Challenge page │ https://www.aicrowd.com/challenges/mosquitoalert-challenge-2023                                 │
└──────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────┘
{'submission_id': 235641, 'created_at': '2023-06-28T12:18:42.976Z'}

Comments

apoorv_agnihotri8
11 months ago

Thanks for the trained model! :)

adegboyega_samuel
11 months ago

pls , are we required to training the model from scratch or fine tune a pretrained object detection model such as yolo v5

m_varun_reddy
11 months ago

Where is the downloaded data located? when I try to unzip the file, it says : unzip: cannot find or open test_images_phase1.zip, test_images_phase1.zip.zip or test_images_phase1.zip.ZIP.

mfalk
11 months ago

@adegboyega_samuel you can tried both approaches. You can train model from scratch or fine tune already pretrained model. This notebook is just an example.

saidinesh_pola
9 months ago

@mfalk Could you provide us with the training configuration and whether the entire training dataset was used to train the Yolov5 baseline submission model.

mfalk
9 months ago

Comment deleted by mfalk.

mfalk
9 months ago

@saidinesh_pola I will be talking about baseline in tomorrow’s town hall event, if are not able to attend it is going to be available later as a recording. Here is an announcement about townhall: https://discourse.aicrowd.com/t/join-mosquito-alert-challenge-townhall/9080

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