AI, we don't believe that you can fly bro pt 2
This commit is contained in:
257
training.py
257
training.py
@@ -28,6 +28,7 @@ if torch.cuda.is_available():
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print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
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device = torch.device('cuda:0')
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.enabled = True
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else:
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print("✗ CUDA not available, using CPU")
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device = torch.device('cpu')
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@@ -36,7 +37,7 @@ print("=" * 60)
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# ===============================
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# DATA LOADING WITH NaN HANDLING
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# DATA LOADING - HANDLES PARTIAL NaN
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# ===============================
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def load_kaggle_asl_data(base_path):
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train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
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@@ -46,7 +47,7 @@ def load_kaggle_asl_data(base_path):
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def extract_hand_landmarks_from_parquet(path):
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"""Extract hand landmarks, handling NaN values properly"""
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"""Extract hand landmarks - ONLY uses frames with valid (non-NaN) data"""
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try:
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df = pd.read_parquet(path)
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@@ -54,56 +55,70 @@ def extract_hand_landmarks_from_parquet(path):
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left = df[df["type"] == "left_hand"]
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right = df[df["type"] == "right_hand"]
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# Choose hand with more non-NaN data
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left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
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right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
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if left_valid == 0 and right_valid == 0:
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return None # No valid hand data
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hand = left if left_valid >= right_valid else right
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if len(hand) == 0:
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if len(left) == 0 and len(right) == 0:
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return None
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# Get frames with valid data
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# Count valid (non-NaN) rows for each hand
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left_valid = 0
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right_valid = 0
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if len(left) > 0:
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left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
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if len(right) > 0:
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right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
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# No valid data at all
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if left_valid == 0 and right_valid == 0:
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return None
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# Choose hand with more valid data
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hand = left if left_valid >= right_valid else right
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# Get unique frames
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frames = sorted(hand['frame'].unique())
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landmarks_seq = []
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for frame in frames:
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lm_frame = hand[hand['frame'] == frame]
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# Check if this frame has valid data
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valid_rows = lm_frame[['x', 'y', 'z']].notna().all(axis=1)
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if valid_rows.sum() < 10: # Need at least 10 valid landmarks
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# Count how many valid landmarks this frame has
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valid_count = lm_frame[['x', 'y', 'z']].notna().all(axis=1).sum()
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# Skip frames with too few valid landmarks
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if valid_count < 10:
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continue
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lm_list = []
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frame_has_data = False
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# Extract landmarks for this frame
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frame_landmarks = []
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valid_landmarks_in_frame = 0
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for i in range(21):
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lm = lm_frame[lm_frame['landmark_index'] == i]
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if len(lm) == 0:
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lm_list.append([0.0, 0.0, 0.0])
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frame_landmarks.append([0.0, 0.0, 0.0])
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else:
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x = lm['x'].iloc[0]
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y = lm['y'].iloc[0]
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z = lm['z'].iloc[0]
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x = float(lm['x'].iloc[0])
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y = float(lm['y'].iloc[0])
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z = float(lm['z'].iloc[0])
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# Check for NaN
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if pd.isna(x) or pd.isna(y) or pd.isna(z):
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lm_list.append([0.0, 0.0, 0.0])
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# Check if valid
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if pd.notna(x) and pd.notna(y) and pd.notna(z):
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frame_landmarks.append([x, y, z])
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valid_landmarks_in_frame += 1
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else:
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lm_list.append([float(x), float(y), float(z)])
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frame_has_data = True
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frame_landmarks.append([0.0, 0.0, 0.0])
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if frame_has_data:
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landmarks_seq.append(lm_list)
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# Only add frame if it has enough valid landmarks
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if valid_landmarks_in_frame >= 10:
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landmarks_seq.append(frame_landmarks)
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if len(landmarks_seq) == 0:
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# Need at least 3 valid frames
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if len(landmarks_seq) < 3:
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return None
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return np.array(landmarks_seq, dtype=np.float32)
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except Exception as e:
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return None
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@@ -113,43 +128,57 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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if landmarks_seq is None or len(landmarks_seq) == 0:
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return None, None
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# Center on wrist
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landmarks_seq = landmarks_seq - landmarks_seq[:, 0:1, :]
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# Center on wrist (landmark 0)
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wrist = landmarks_seq[:, 0:1, :].copy()
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landmarks_seq = landmarks_seq - wrist
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# Scale using wrist → middle finger MCP distance
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scale = np.linalg.norm(landmarks_seq[:, 0] - landmarks_seq[:, 9], axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6)
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# Scale normalization using wrist to middle finger MCP (landmark 9)
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scale = np.linalg.norm(landmarks_seq[:, 9, :] - np.zeros(3), axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6) # Avoid division by zero
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landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
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# Replace any remaining NaN/Inf with 0
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# Clean up any remaining NaN/Inf
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landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
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# Finger curl distances
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tips = [4, 8, 12, 16, 20]
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bases = [1, 5, 9, 13, 17]
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# Clip extreme values
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landmarks_seq = np.clip(landmarks_seq, -10, 10)
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# Calculate finger curl features
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tips = [4, 8, 12, 16, 20] # Thumb, index, middle, ring, pinky tips
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bases = [1, 5, 9, 13, 17] # Corresponding base joints
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curl_features = []
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for b, t in zip(bases, tips):
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curl = np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)
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curl_features.append(curl)
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curl_features = np.stack(curl_features, axis=1)
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curl_features = np.stack(curl_features, axis=1) # (T, 5)
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# Temporal deltas
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# Temporal deltas (motion)
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deltas = np.zeros_like(landmarks_seq)
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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if len(landmarks_seq) > 1:
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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# Flatten features
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seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2)
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# Combine all features
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seq = np.concatenate([
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landmarks_seq, # (T, 21, 3)
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deltas, # (T, 21, 3)
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curl_features[:, :, np.newaxis] # (T, 5, 1)
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], axis=2)
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# Flatten spatial dimensions: (T, 21*3 + 21*3 + 5) = (T, 131)
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seq = seq.reshape(seq.shape[0], -1)
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# Pad or truncate
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# Pad or truncate to max_frames
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T, F = seq.shape
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if T < max_frames:
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# Pad with zeros
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pad = np.zeros((max_frames - T, F), dtype=np.float32)
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seq = np.concatenate([seq, pad], axis=0)
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elif T > max_frames:
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# Truncate
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seq = seq[:max_frames, :]
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# Create mask
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# Create attention mask (True for valid positions)
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valid_mask = np.zeros(max_frames, dtype=bool)
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valid_mask[:min(T, max_frames)] = True
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@@ -157,26 +186,30 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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def process_row(row, base_path, max_frames=100):
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"""Process a single row"""
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"""Process a single row - worker function for multiprocessing"""
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path = os.path.join(base_path, row["path"])
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if not os.path.exists(path):
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return None, None, None
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try:
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# Extract landmarks
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lm = extract_hand_landmarks_from_parquet(path)
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if lm is None:
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return None, None, None
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# Get features
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feat, mask = get_features_sequence(lm, max_frames)
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if feat is None:
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return None, None, None
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# Final NaN check
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# Final safety check
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if np.isnan(feat).any() or np.isinf(feat).any():
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return None, None, None
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return feat, mask, row["sign"]
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except:
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except Exception as e:
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return None, None, None
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@@ -198,12 +231,15 @@ class PositionalEncoding(nn.Module):
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class TransformerASL(nn.Module):
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def __init__(self, input_dim=68, num_classes=250, d_model=256, nhead=8, num_layers=4):
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def __init__(self, input_dim, num_classes, d_model=256, nhead=8, num_layers=4):
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super().__init__()
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# Input projection
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self.proj = nn.Linear(input_dim, d_model)
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self.norm_in = nn.LayerNorm(d_model)
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self.pos = PositionalEncoding(d_model)
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self.pos = PositionalEncoding(d_model, max_len=128)
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# Transformer encoder
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enc_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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@@ -215,6 +251,7 @@ class TransformerASL(nn.Module):
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)
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self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
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# Classification head
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self.head = nn.Sequential(
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nn.LayerNorm(d_model),
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nn.Dropout(0.25),
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@@ -222,11 +259,17 @@ class TransformerASL(nn.Module):
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)
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def forward(self, x, key_padding_mask=None):
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# x: (batch, seq_len, input_dim)
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# key_padding_mask: (batch, seq_len) - True for padding positions
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x = self.proj(x)
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x = self.norm_in(x)
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x = self.pos(x)
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x = self.encoder(x, src_key_padding_mask=key_padding_mask)
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# Global average pooling over valid positions
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x = x.mean(dim=1)
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return self.head(x)
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@@ -236,7 +279,7 @@ class TransformerASL(nn.Module):
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def main():
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base_path = "asl_kaggle"
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max_frames = 100
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MIN_SAMPLES_PER_CLASS = 6
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MIN_SAMPLES_PER_CLASS = 5
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print("\nLoading metadata...")
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train_df, sign_to_idx = load_kaggle_asl_data(base_path)
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@@ -244,7 +287,10 @@ def main():
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rows = [row for _, row in train_df.iterrows()]
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print("\nProcessing sequences with NaN handling...")
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print("\nProcessing sequences (this will take a few minutes)...")
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print("Expected: ~36,000 valid sequences based on diagnostic")
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# Process with multiprocessing
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with Pool(cpu_count()) as pool:
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results = list(tqdm(
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pool.imap(
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@@ -259,27 +305,30 @@ def main():
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# Filter valid results
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X_list, masks_list, y_list = [], [], []
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for feat, mask, sign in results:
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if feat is not None and feat.shape[0] == max_frames:
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X_list.append(feat)
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masks_list.append(mask)
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y_list.append(sign)
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if feat is not None and mask is not None and sign is not None:
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if feat.shape[0] == max_frames:
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X_list.append(feat)
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masks_list.append(mask)
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y_list.append(sign)
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print(f"\n✓ Valid sequences: {len(X_list)} out of {len(train_df)}")
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print(f"\n✓ Successfully extracted: {len(X_list)} valid sequences")
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print(f" Success rate: {len(X_list) / len(train_df) * 100:.1f}%")
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if not X_list:
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print("❌ No valid sequences found!")
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print("\nPossible issues:")
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print(" 1. Most files contain only NaN hand landmarks")
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print(" 2. Hand detection failed in most videos")
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print(" 3. Dataset might be corrupted")
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if len(X_list) < 100:
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print("❌ Too few valid sequences found!")
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print(" This shouldn't happen - please share this output for debugging")
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return
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# Stack into arrays
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X = np.stack(X_list)
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masks = np.stack(masks_list)
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print(f"Data shape: {X.shape}")
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print(f"\nData shape: {X.shape}")
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print(f"Feature dimension: {X.shape[2]}")
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# Global normalization
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X = np.clip(X, -5.0, 5.0)
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print("Normalizing features...")
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X = np.clip(X, -10.0, 10.0)
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mean = X.mean(axis=(0, 1), keepdims=True)
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std = X.std(axis=(0, 1), keepdims=True) + 1e-8
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X = (X - mean) / std
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@@ -292,22 +341,29 @@ def main():
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counts = Counter(y)
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valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
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mask_valid = np.isin(y, valid_classes)
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X = X[mask_valid]
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masks = masks[mask_valid]
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y = y[mask_valid]
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# Re-encode
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# Re-encode after filtering
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le = LabelEncoder()
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y = le.fit_transform(y)
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print(f"Final dataset: {len(X)} samples | {len(le.classes_)} classes")
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print(f"\nFinal dataset after filtering:")
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print(f" Samples: {len(X):,}")
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print(f" Classes: {len(le.classes_)}")
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print(f" Sign examples: {list(le.classes_[:10])}")
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# Train-test split
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X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
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X, masks, y, test_size=0.15, stratify=y, random_state=42
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)
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# Dataset
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print(f"\nTrain set: {len(X_train):,} samples")
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print(f"Test set: {len(X_test):,} samples")
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# Dataset wrapper
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class ASLSequenceDataset(Dataset):
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def __init__(self, X, masks, y):
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self.X = torch.from_numpy(X).float()
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@@ -320,18 +376,27 @@ def main():
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def __getitem__(self, idx):
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return self.X[idx], self.masks[idx], self.y[idx]
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# DataLoaders
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batch_size = 128 if device.type == 'cuda' else 64
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train_loader = DataLoader(
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ASLSequenceDataset(X_train, masks_train, y_train),
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batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True
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)
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test_loader = DataLoader(
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ASLSequenceDataset(X_test, masks_test, y_test),
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batch_size=batch_size * 2, shuffle=False, num_workers=4, pin_memory=True
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batch_size=batch_size,
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shuffle=True,
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num_workers=4,
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pin_memory=True if device.type == 'cuda' else False
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)
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# Model
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test_loader = DataLoader(
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ASLSequenceDataset(X_test, masks_test, y_test),
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batch_size=batch_size * 2,
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shuffle=False,
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num_workers=4,
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pin_memory=True if device.type == 'cuda' else False
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)
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# Initialize model
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print("\nInitializing model...")
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model = TransformerASL(
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input_dim=X.shape[2],
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num_classes=len(le.classes_),
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@@ -341,35 +406,41 @@ def main():
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).to(device)
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total_params = sum(p.numel() for p in model.parameters())
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print(f"\nModel parameters: {total_params:,}")
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print(f"Model parameters: {total_params:,}")
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# Training setup
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criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
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optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
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# Training
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# Training loop
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best_acc = 0.0
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patience = 15
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wait = 0
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epochs = 70
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epochs = 60
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print("\nStarting training...")
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print("\n" + "=" * 60)
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print("STARTING TRAINING")
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print("=" * 60)
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for epoch in range(epochs):
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# Train
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model.train()
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total_loss = 0
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correct = total = 0
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for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"):
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for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}", leave=False):
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x, mask, yb = x.to(device), mask.to(device), yb.to(device)
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||||
|
||||
# Invert mask: True for padding positions
|
||||
key_mask = ~mask
|
||||
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
logits = model(x, key_padding_mask=key_mask)
|
||||
loss = criterion(logits, yb)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
|
||||
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
|
||||
optimizer.step()
|
||||
|
||||
total_loss += loss.item()
|
||||
@@ -378,7 +449,7 @@ def main():
|
||||
|
||||
train_acc = correct / total * 100
|
||||
|
||||
# Eval
|
||||
# Evaluate
|
||||
model.eval()
|
||||
correct = total = 0
|
||||
with torch.no_grad():
|
||||
@@ -391,11 +462,13 @@ def main():
|
||||
|
||||
test_acc = correct / total * 100
|
||||
|
||||
# Print progress
|
||||
print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
|
||||
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%")
|
||||
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")
|
||||
|
||||
scheduler.step()
|
||||
|
||||
# Save best model
|
||||
if test_acc > best_acc:
|
||||
best_acc = test_acc
|
||||
wait = 0
|
||||
@@ -406,19 +479,25 @@ def main():
|
||||
'acc': best_acc,
|
||||
'epoch': epoch,
|
||||
'input_dim': X.shape[2],
|
||||
'num_classes': len(le.classes_)
|
||||
'num_classes': len(le.classes_),
|
||||
'd_model': 256,
|
||||
'nhead': 8,
|
||||
'num_layers': 4
|
||||
}, "best_asl_transformer.pth")
|
||||
print(f" → New best: {best_acc:.2f}%")
|
||||
print(f" → New best: {best_acc:.2f}% ✓")
|
||||
else:
|
||||
wait += 1
|
||||
print()
|
||||
|
||||
if wait >= patience:
|
||||
print("Early stopping")
|
||||
print(f"\nEarly stopping triggered at epoch {epoch + 1}")
|
||||
break
|
||||
|
||||
print("=" * 60)
|
||||
print(f"\n✓ Training complete!")
|
||||
print("\n" + "=" * 60)
|
||||
print(f"✓ Training complete!")
|
||||
print(f"✓ Best test accuracy: {best_acc:.2f}%")
|
||||
print(f"✓ Model saved: best_asl_transformer.pth")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
626
valid_files.json
Normal file
626
valid_files.json
Normal file
@@ -0,0 +1,626 @@
|
||||
[
|
||||
{
|
||||
"path": "train_landmark_files/16069/100015657.parquet",
|
||||
"sign": "cloud",
|
||||
"hand": "left",
|
||||
"total_frames": 105,
|
||||
"valid_frames": 28,
|
||||
"nan_ratio": 0.7333333333333333
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1000278229.parquet",
|
||||
"sign": "lips",
|
||||
"hand": "left",
|
||||
"total_frames": 57,
|
||||
"valid_frames": 36,
|
||||
"nan_ratio": 0.3684210526315789
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1000536928.parquet",
|
||||
"sign": "apple",
|
||||
"hand": "left",
|
||||
"total_frames": 13,
|
||||
"valid_frames": 10,
|
||||
"nan_ratio": 0.23076923076923078
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1000638205.parquet",
|
||||
"sign": "puzzle",
|
||||
"hand": "left",
|
||||
"total_frames": 19,
|
||||
"valid_frames": 11,
|
||||
"nan_ratio": 0.42105263157894735
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/1000697904.parquet",
|
||||
"sign": "there",
|
||||
"hand": "left",
|
||||
"total_frames": 43,
|
||||
"valid_frames": 15,
|
||||
"nan_ratio": 0.6511627906976745
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1000909322.parquet",
|
||||
"sign": "shirt",
|
||||
"hand": "left",
|
||||
"total_frames": 22,
|
||||
"valid_frames": 22,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/1000956928.parquet",
|
||||
"sign": "owl",
|
||||
"hand": "left",
|
||||
"total_frames": 100,
|
||||
"valid_frames": 98,
|
||||
"nan_ratio": 0.02
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1001223069.parquet",
|
||||
"sign": "not",
|
||||
"hand": "left",
|
||||
"total_frames": 18,
|
||||
"valid_frames": 6,
|
||||
"nan_ratio": 0.6666666666666666
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1001258102.parquet",
|
||||
"sign": "zipper",
|
||||
"hand": "left",
|
||||
"total_frames": 13,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.38461538461538464
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1001471195.parquet",
|
||||
"sign": "cheek",
|
||||
"hand": "left",
|
||||
"total_frames": 17,
|
||||
"valid_frames": 17,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1001560021.parquet",
|
||||
"sign": "shoe",
|
||||
"hand": "left",
|
||||
"total_frames": 63,
|
||||
"valid_frames": 63,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/34503/1001685690.parquet",
|
||||
"sign": "empty",
|
||||
"hand": "left",
|
||||
"total_frames": 8,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1001819372.parquet",
|
||||
"sign": "balloon",
|
||||
"hand": "left",
|
||||
"total_frames": 27,
|
||||
"valid_frames": 16,
|
||||
"nan_ratio": 0.4074074074074074
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1001899025.parquet",
|
||||
"sign": "same",
|
||||
"hand": "left",
|
||||
"total_frames": 44,
|
||||
"valid_frames": 22,
|
||||
"nan_ratio": 0.5
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/28656/1001919956.parquet",
|
||||
"sign": "orange",
|
||||
"hand": "left",
|
||||
"total_frames": 53,
|
||||
"valid_frames": 48,
|
||||
"nan_ratio": 0.09433962264150944
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1001958254.parquet",
|
||||
"sign": "go",
|
||||
"hand": "left",
|
||||
"total_frames": 24,
|
||||
"valid_frames": 23,
|
||||
"nan_ratio": 0.041666666666666664
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1002052130.parquet",
|
||||
"sign": "TV",
|
||||
"hand": "left",
|
||||
"total_frames": 116,
|
||||
"valid_frames": 109,
|
||||
"nan_ratio": 0.0603448275862069
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1002113535.parquet",
|
||||
"sign": "another",
|
||||
"hand": "left",
|
||||
"total_frames": 6,
|
||||
"valid_frames": 3,
|
||||
"nan_ratio": 0.5
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1002129762.parquet",
|
||||
"sign": "giraffe",
|
||||
"hand": "left",
|
||||
"total_frames": 23,
|
||||
"valid_frames": 14,
|
||||
"nan_ratio": 0.391304347826087
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/29302/1002284514.parquet",
|
||||
"sign": "can",
|
||||
"hand": "left",
|
||||
"total_frames": 41,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.8048780487804879
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/100230619.parquet",
|
||||
"sign": "say",
|
||||
"hand": "left",
|
||||
"total_frames": 20,
|
||||
"valid_frames": 19,
|
||||
"nan_ratio": 0.05
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1002734054.parquet",
|
||||
"sign": "that",
|
||||
"hand": "left",
|
||||
"total_frames": 6,
|
||||
"valid_frames": 6,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1002776784.parquet",
|
||||
"sign": "black",
|
||||
"hand": "left",
|
||||
"total_frames": 15,
|
||||
"valid_frames": 9,
|
||||
"nan_ratio": 0.4
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/1003007869.parquet",
|
||||
"sign": "moon",
|
||||
"hand": "left",
|
||||
"total_frames": 62,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.9354838709677419
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1003093029.parquet",
|
||||
"sign": "pizza",
|
||||
"hand": "left",
|
||||
"total_frames": 23,
|
||||
"valid_frames": 18,
|
||||
"nan_ratio": 0.21739130434782608
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/1003109377.parquet",
|
||||
"sign": "shhh",
|
||||
"hand": "left",
|
||||
"total_frames": 23,
|
||||
"valid_frames": 20,
|
||||
"nan_ratio": 0.13043478260869565
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1003335907.parquet",
|
||||
"sign": "now",
|
||||
"hand": "left",
|
||||
"total_frames": 14,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.42857142857142855
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1003347075.parquet",
|
||||
"sign": "TV",
|
||||
"hand": "left",
|
||||
"total_frames": 89,
|
||||
"valid_frames": 69,
|
||||
"nan_ratio": 0.2247191011235955
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1003483016.parquet",
|
||||
"sign": "jump",
|
||||
"hand": "left",
|
||||
"total_frames": 6,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.3333333333333333
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/34503/1003483410.parquet",
|
||||
"sign": "sleep",
|
||||
"hand": "left",
|
||||
"total_frames": 8,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/100393298.parquet",
|
||||
"sign": "uncle",
|
||||
"hand": "left",
|
||||
"total_frames": 60,
|
||||
"valid_frames": 38,
|
||||
"nan_ratio": 0.36666666666666664
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1004200659.parquet",
|
||||
"sign": "dryer",
|
||||
"hand": "left",
|
||||
"total_frames": 59,
|
||||
"valid_frames": 33,
|
||||
"nan_ratio": 0.4406779661016949
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/10042041.parquet",
|
||||
"sign": "green",
|
||||
"hand": "left",
|
||||
"total_frames": 105,
|
||||
"valid_frames": 6,
|
||||
"nan_ratio": 0.9428571428571428
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1004211348.parquet",
|
||||
"sign": "bug",
|
||||
"hand": "left",
|
||||
"total_frames": 163,
|
||||
"valid_frames": 97,
|
||||
"nan_ratio": 0.4049079754601227
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/29302/1004285193.parquet",
|
||||
"sign": "brother",
|
||||
"hand": "left",
|
||||
"total_frames": 20,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.8
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1004302418.parquet",
|
||||
"sign": "sad",
|
||||
"hand": "left",
|
||||
"total_frames": 34,
|
||||
"valid_frames": 15,
|
||||
"nan_ratio": 0.5588235294117647
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/100438640.parquet",
|
||||
"sign": "penny",
|
||||
"hand": "left",
|
||||
"total_frames": 75,
|
||||
"valid_frames": 23,
|
||||
"nan_ratio": 0.6933333333333334
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/1004456612.parquet",
|
||||
"sign": "mitten",
|
||||
"hand": "left",
|
||||
"total_frames": 43,
|
||||
"valid_frames": 26,
|
||||
"nan_ratio": 0.3953488372093023
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1004789884.parquet",
|
||||
"sign": "brown",
|
||||
"hand": "left",
|
||||
"total_frames": 18,
|
||||
"valid_frames": 11,
|
||||
"nan_ratio": 0.3888888888888889
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/100498908.parquet",
|
||||
"sign": "drink",
|
||||
"hand": "left",
|
||||
"total_frames": 11,
|
||||
"valid_frames": 11,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1005009451.parquet",
|
||||
"sign": "stay",
|
||||
"hand": "left",
|
||||
"total_frames": 25,
|
||||
"valid_frames": 14,
|
||||
"nan_ratio": 0.44
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1005052722.parquet",
|
||||
"sign": "tooth",
|
||||
"hand": "left",
|
||||
"total_frames": 20,
|
||||
"valid_frames": 20,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/34503/1005088445.parquet",
|
||||
"sign": "awake",
|
||||
"hand": "left",
|
||||
"total_frames": 8,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/100515039.parquet",
|
||||
"sign": "hot",
|
||||
"hand": "left",
|
||||
"total_frames": 30,
|
||||
"valid_frames": 17,
|
||||
"nan_ratio": 0.43333333333333335
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1005189210.parquet",
|
||||
"sign": "like",
|
||||
"hand": "left",
|
||||
"total_frames": 7,
|
||||
"valid_frames": 7,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1005223850.parquet",
|
||||
"sign": "where",
|
||||
"hand": "left",
|
||||
"total_frames": 15,
|
||||
"valid_frames": 7,
|
||||
"nan_ratio": 0.5333333333333333
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1005422610.parquet",
|
||||
"sign": "potty",
|
||||
"hand": "left",
|
||||
"total_frames": 16,
|
||||
"valid_frames": 16,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1005476876.parquet",
|
||||
"sign": "down",
|
||||
"hand": "left",
|
||||
"total_frames": 9,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.1111111111111111
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1005492440.parquet",
|
||||
"sign": "old",
|
||||
"hand": "left",
|
||||
"total_frames": 76,
|
||||
"valid_frames": 15,
|
||||
"nan_ratio": 0.8026315789473685
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/1005734995.parquet",
|
||||
"sign": "no",
|
||||
"hand": "left",
|
||||
"total_frames": 6,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.3333333333333333
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1005826772.parquet",
|
||||
"sign": "head",
|
||||
"hand": "left",
|
||||
"total_frames": 26,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.8461538461538461
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/100585188.parquet",
|
||||
"sign": "down",
|
||||
"hand": "left",
|
||||
"total_frames": 71,
|
||||
"valid_frames": 23,
|
||||
"nan_ratio": 0.676056338028169
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1005865446.parquet",
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||||
"sign": "orange",
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||||
"hand": "left",
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||||
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||||
"valid_frames": 36,
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||||
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||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1005866711.parquet",
|
||||
"sign": "food",
|
||||
"hand": "left",
|
||||
"total_frames": 52,
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||||
"valid_frames": 47,
|
||||
"nan_ratio": 0.09615384615384616
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||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1006058791.parquet",
|
||||
"sign": "apple",
|
||||
"hand": "left",
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||||
"total_frames": 14,
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||||
"valid_frames": 9,
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||||
"nan_ratio": 0.35714285714285715
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||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/1006223282.parquet",
|
||||
"sign": "pretty",
|
||||
"hand": "left",
|
||||
"total_frames": 27,
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||||
"valid_frames": 21,
|
||||
"nan_ratio": 0.2222222222222222
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1006402771.parquet",
|
||||
"sign": "nuts",
|
||||
"hand": "left",
|
||||
"total_frames": 18,
|
||||
"valid_frames": 17,
|
||||
"nan_ratio": 0.05555555555555555
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1006443357.parquet",
|
||||
"sign": "animal",
|
||||
"hand": "left",
|
||||
"total_frames": 33,
|
||||
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|
||||
"nan_ratio": 0.30303030303030304
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/34503/1006554061.parquet",
|
||||
"sign": "say",
|
||||
"hand": "left",
|
||||
"total_frames": 47,
|
||||
"valid_frames": 6,
|
||||
"nan_ratio": 0.8723404255319149
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1006584947.parquet",
|
||||
"sign": "finish",
|
||||
"hand": "left",
|
||||
"total_frames": 26,
|
||||
"valid_frames": 19,
|
||||
"nan_ratio": 0.2692307692307692
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/37055/1006625592.parquet",
|
||||
"sign": "beside",
|
||||
"hand": "left",
|
||||
"total_frames": 42,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.9047619047619048
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/28656/1006698125.parquet",
|
||||
"sign": "noisy",
|
||||
"hand": "left",
|
||||
"total_frames": 311,
|
||||
"valid_frames": 273,
|
||||
"nan_ratio": 0.12218649517684887
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/22343/1006778422.parquet",
|
||||
"sign": "happy",
|
||||
"hand": "left",
|
||||
"total_frames": 36,
|
||||
"valid_frames": 3,
|
||||
"nan_ratio": 0.9166666666666666
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/34503/1006899334.parquet",
|
||||
"sign": "stuck",
|
||||
"hand": "left",
|
||||
"total_frames": 225,
|
||||
"valid_frames": 219,
|
||||
"nan_ratio": 0.02666666666666667
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/1006973536.parquet",
|
||||
"sign": "bug",
|
||||
"hand": "left",
|
||||
"total_frames": 42,
|
||||
"valid_frames": 41,
|
||||
"nan_ratio": 0.023809523809523808
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1007054441.parquet",
|
||||
"sign": "say",
|
||||
"hand": "left",
|
||||
"total_frames": 48,
|
||||
"valid_frames": 48,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1007073382.parquet",
|
||||
"sign": "sticky",
|
||||
"hand": "left",
|
||||
"total_frames": 76,
|
||||
"valid_frames": 51,
|
||||
"nan_ratio": 0.32894736842105265
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1007127288.parquet",
|
||||
"sign": "owie",
|
||||
"hand": "left",
|
||||
"total_frames": 105,
|
||||
"valid_frames": 47,
|
||||
"nan_ratio": 0.5523809523809524
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1007285542.parquet",
|
||||
"sign": "high",
|
||||
"hand": "left",
|
||||
"total_frames": 24,
|
||||
"valid_frames": 21,
|
||||
"nan_ratio": 0.125
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/55372/1007289801.parquet",
|
||||
"sign": "fine",
|
||||
"hand": "left",
|
||||
"total_frames": 116,
|
||||
"valid_frames": 43,
|
||||
"nan_ratio": 0.6293103448275862
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/61333/1007294943.parquet",
|
||||
"sign": "shirt",
|
||||
"hand": "left",
|
||||
"total_frames": 13,
|
||||
"valid_frames": 11,
|
||||
"nan_ratio": 0.15384615384615385
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1007313467.parquet",
|
||||
"sign": "finish",
|
||||
"hand": "left",
|
||||
"total_frames": 19,
|
||||
"valid_frames": 16,
|
||||
"nan_ratio": 0.15789473684210525
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1007343357.parquet",
|
||||
"sign": "boat",
|
||||
"hand": "left",
|
||||
"total_frames": 15,
|
||||
"valid_frames": 9,
|
||||
"nan_ratio": 0.4
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1007376023.parquet",
|
||||
"sign": "all",
|
||||
"hand": "left",
|
||||
"total_frames": 39,
|
||||
"valid_frames": 13,
|
||||
"nan_ratio": 0.6666666666666666
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/36257/1007470312.parquet",
|
||||
"sign": "pencil",
|
||||
"hand": "left",
|
||||
"total_frames": 8,
|
||||
"valid_frames": 8,
|
||||
"nan_ratio": 0.0
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/16069/1007589714.parquet",
|
||||
"sign": "sleepy",
|
||||
"hand": "left",
|
||||
"total_frames": 12,
|
||||
"valid_frames": 4,
|
||||
"nan_ratio": 0.6666666666666666
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/32319/1007595679.parquet",
|
||||
"sign": "moon",
|
||||
"hand": "left",
|
||||
"total_frames": 21,
|
||||
"valid_frames": 16,
|
||||
"nan_ratio": 0.23809523809523808
|
||||
},
|
||||
{
|
||||
"path": "train_landmark_files/27610/1007714989.parquet",
|
||||
"sign": "puzzle",
|
||||
"hand": "left",
|
||||
"total_frames": 145,
|
||||
"valid_frames": 59,
|
||||
"nan_ratio": 0.593103448275862
|
||||
}
|
||||
]
|
||||
Reference in New Issue
Block a user