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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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pipeline_tag: video-classification |
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tags: |
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- i3d |
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- pytorch |
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- crime-detection |
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--- |
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# Smart Surveillance System |
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we leveraged a pre-trained I3D model and fine-tuned it using two strategies: |
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Block-level tuning Adjusting and retraining groups of layers (blocks) to adapt the model to the new dataset. |
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Layer-level tuning Fine-tuning specific layers for more granular control over feature learning. |
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The final classification layer of the I3D model was removed and replaced with a custom output layer tailored to our binary classification task: predicting whether an activity represents a crime (1) or non-crime (0). |
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## How Run |
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```python |
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import torch |
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import torch.nn as nn |
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class UCFModel(nn.Module): |
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def __init__(self, model_name="i3d_r50"): |
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super().__init__() |
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self.model_name = model_name |
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self.model = torch.hub.load("facebookresearch/pytorchvideo", model_name, pretrained=True) |
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in_features = self.model.blocks[-1].proj.in_features |
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self.model.blocks[-1].proj = nn.Linear(in_features, 2) |
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def forward(self, frames): |
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return self.model(frames) |
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``` |
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```python |
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import torch |
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from PIL import Image |
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from huggingface_hub import hf_hub_download |
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from torchvision import transforms |
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inference_transform = transforms.Compose( |
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[ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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] |
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) |
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class UCFInferenceByFrames: |
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def __init__(self, repo_id): |
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self.repo_id = repo_id |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self.model = self.load_model() |
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def load_model(self): |
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model_path = hf_hub_download(repo_id=self.repo_id, filename="ucf_model.pth") |
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state_dict = torch.load(model_path) |
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model = UCFModel().to(device=self.device) |
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model.load_state_dict(state_dict) |
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model.eval() |
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return model |
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def inference(self, frames): |
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video_tensor_list = [] |
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for frame in frames: |
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frame_pil = Image.fromarray(frame) |
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frame_tensor = inference_transform(frame_pil) |
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video_tensor_list.append(frame_tensor) |
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video_tensor = torch.stack(video_tensor_list) |
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video_tensor = video_tensor.permute(1, 0, 2, 3).unsqueeze(0).float() |
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video_tensor = video_tensor.to(self.device) |
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with torch.no_grad(): |
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output = self.model(video_tensor) |
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return output.argmax(1) |
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``` |
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```python |
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import cv2 as cv |
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import numpy as np |
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ucf = UCFInferenceByFrames("amjad-awad/ucf-i3d-model-by-block-lr-0.01") |
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def inference(ucf_model, video_path, max_frames=16): |
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cap = cv.VideoCapture(video_path) |
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if not cap.isOpened(): |
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print("No video") |
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return |
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frames = [] |
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while True: |
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ret, frame = cap.read() |
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if not ret: |
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break |
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frames.append(frame) |
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length = len(frames) |
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indices = np.linspace(0, length - 1, max_frames, dtype=int) |
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frames = [frames[i] for i in indices] |
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predict = ucf_model.inference(frames) |
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return "Crime" if int(predict) == 1 else "No-Crime" |
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``` |
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```python |
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predict = inference(ucf_model=ucf, video_path="YOUR_VIDEO_PATH.mp4") |
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print(predict) |
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``` |
