Camshowrecordings/model/sam_samantha/5 !!hot!! (2027)

frame_idx = 0 while True: ret, frame = cap.read() if not ret: break

cd model/sam_samantha/5 ls -l Typical files you’ll see:

model: name: sam_samantha version: 5 backbone: vit_h image_size: 1024 num_classes: 1 # Usually segmentation → binary mask preprocess: normalize: true mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] device: cuda Below is a minimal, self‑contained script that loads the model and runs a single inference on a video frame. camshowrecordings/model/sam_samantha/5

# HWC → CHW and add batch dim tensor = torch.from_numpy(img_norm).permute(2, 0, 1).unsqueeze(0) return tensor.to(device)

# ------------------------------------------------------------------ # 4️⃣ Pre‑process a single frame (example uses OpenCV) # ------------------------------------------------------------------ def preprocess(img: np.ndarray, cfg) -> torch.Tensor: # Resize while keeping aspect ratio (optional) target_sz = cfg["model"]["image_size"] img_resized = cv2.resize(img, (target_sz, target_sz)) frame_idx = 0 while True: ret, frame = cap

# 3️⃣ Install dependencies pip install -r requirements.txt If the repo is private, make sure you have the right SSH key or token. 5️⃣ Inspecting the Model Files Navigate to the model folder:

Open config.yaml to verify things like:

device = torch.device(cfg.get("device", "cpu")) model.to(device)