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# Import the 'pipeline' function from the 'transformers' library.
from transformers import pipeline
pipe1 = pipeline('image-classification', model="dima806/crime_cctv_image_detection", device=0)
pipe2 = pipeline('image-classification', model="dima806/crime_type_cctv_image_detection", device=0)
# Apply the 'pipe1' function to process the 'image' variable to see if there is a high probability for crime.
res1 = pipe1(image)
crime_score = [res['score'] for res in res1 if res['label']=='Crime'][0]
print(f"Crime score: {round(crime_score, 3)}" )
threshold = 0.5
if crime_score > threshold:
# apply second pipeline to check the crime type
print("There is a potential crime")
res2 = pipe2(image)
print("Top scores for crime types:")
for res in res2:
print(f"For {res['label']}: {round(res['score'], 3)}")
Classification report:
precision recall f1-score support
Abuse 0.0831 0.4411 0.1399 297
Arrest 0.3701 0.2383 0.2899 3365
Arson 0.2103 0.2707 0.2367 2793
Assault 0.0135 0.0083 0.0103 2657
Burglary 0.4341 0.2628 0.3274 7657
Explosion 0.8575 0.1220 0.2136 6510
Fighting 0.1200 0.4119 0.1858 1231
RoadAccidents 0.2454 0.6568 0.3573 2663
Robbery 0.0170 0.2240 0.0316 835
Shooting 0.4182 0.0706 0.1209 7630
Shoplifting 0.9057 0.2457 0.3865 7623
Stealing 0.1380 0.2198 0.1696 1984
Vandalism 0.0980 0.2601 0.1423 1111
accuracy 0.2178 46356
macro avg 0.3008 0.2640 0.2009 46356
weighted avg 0.4766 0.2178 0.2410 46356