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b/base_models/yolo11n.pt new file mode 100644 index 0000000000000000000000000000000000000000..c7723db027d009343e9682f261370833fd6f0d84 --- /dev/null +++ b/base_models/yolo11n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ebbc80d4a7680d14987a577cd21342b65ecfd94632bd9a8da63ae6417644ee1 +size 5613764 diff --git a/base_models/yolov10n.pt b/base_models/yolov10n.pt new file mode 100644 index 0000000000000000000000000000000000000000..d9df4951d0c68d628b24dbfe13d834e9af96f4e7 --- /dev/null +++ b/base_models/yolov10n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11287ed0735678e7ba1ac2a9b3098c049155b3fde123992e724c1264bcc16b6f +size 5860383 diff --git a/base_models/yolov8m.pt b/base_models/yolov8m.pt new file mode 100644 index 0000000000000000000000000000000000000000..eea29123e0ae14239bf731ef2c912b8afa2cbd14 --- /dev/null +++ b/base_models/yolov8m.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d4a90cdc7a21786cc59cd19778e9eafff836df9e2da32524737c7ee6efe4fe5 +size 52136884 diff --git a/base_models/yolov8n.pt b/base_models/yolov8n.pt new file mode 100644 index 0000000000000000000000000000000000000000..719e6f1dbdfe7c560e5933fc8b0c5a7e857d0234 --- /dev/null +++ b/base_models/yolov8n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36 +size 6549796 diff --git a/logs/training_log_yolo11n.txt b/logs/training_log_yolo11n.txt new file mode 100644 index 0000000000000000000000000000000000000000..7872f58497ab93c471dfaaf287c6fb740f73bc11 --- /dev/null +++ b/logs/training_log_yolo11n.txt @@ -0,0 +1,7568 @@ +nohup: ignoring input +Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'base_models/yolo11n.pt'... + + 0%| | 0.00/5.35M [00:00 0 +Ultralytics 8.3.128 🚀 Python-3.12.8 torch-2.7.0+cu126 +/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/torch/cuda/__init__.py:789: UserWarning: Can't initialize NVML + warnings.warn("Can't initialize NVML") +Traceback (most recent call last): + File "/home/ahanaf/Hackathon/Activity_OD/train.py", line 42, in + train_model_and_store( + File "/home/ahanaf/Hackathon/Activity_OD/train.py", line 7, in train_model_and_store + model.train( + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/model.py", line 787, in train + self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 107, in __init__ + self.device = select_device(self.args.device, self.args.batch) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/utils/torch_utils.py", line 199, in select_device + raise ValueError( +ValueError: Invalid CUDA 'device=0' requested. Use 'device=cpu' or pass valid CUDA device(s) if available, i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU. + +torch.cuda.is_available(): False +torch.cuda.device_count(): 0 +os.environ['CUDA_VISIBLE_DEVICES']: None +See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no CUDA devices are seen by torch. + diff --git a/logs/training_log_yolov8n_New.txt b/logs/training_log_yolov8n_New.txt new file mode 100644 index 0000000000000000000000000000000000000000..1323c43783ac8b30f7eaf0bdfb8e45f0bff87866 --- /dev/null +++ b/logs/training_log_yolov8n_New.txt @@ -0,0 +1,96 @@ +nohup: ignoring input +New https://pypi.org/project/ultralytics/8.3.124 available 😃 Update with 'pip install -U ultralytics' +Ultralytics 8.3.123 🚀 Python-3.12.8 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce RTX 3090, 24257MiB) +engine/trainer: task=detect, mode=train, model=./base_models/yolov8n.pt, data=merged_data.yaml, epochs=50, time=None, patience=10, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=Activity_Detection_New_Ds, name=yolov8n.pt3, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=42, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.005, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=15, translate=0.2, scale=0.8, shear=10.0, perspective=0.001, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.2, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=Activity_Detection_New_Ds/yolov8n.pt3 +Overriding model.yaml nc=80 with nc=3 + + from n params module arguments + 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] + 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] + 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] + 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] + 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] + 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] + 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] + 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] + 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] + 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] + 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] + 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] + 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] + 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] + 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] + 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] + 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] + 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] + 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] + 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] + 22 [15, 18, 21] 1 751897 ultralytics.nn.modules.head.Detect [3, [64, 128, 256]] +Model summary: 129 layers, 3,011,433 parameters, 3,011,417 gradients, 8.2 GFLOPs + +Transferred 319/355 items from pretrained weights +Freezing layer 'model.22.dfl.conv.weight' +AMP: running Automatic Mixed Precision (AMP) checks... +AMP: checks passed ✅ +train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1772.5±1101.2 MB/s, size: 1439.9 KB) + train: Scanning /home/ahanaf/Hackathon/Activity_OD/db_1/data/labels/train...: 0%| | 0/25747 [00:00 + train_model_and_store( + File "/home/ahanaf/Hackathon/Activity_OD/train.py", line 8, in train_model_and_store + model.train( + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/model.py", line 790, in train + self.trainer.train() + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 210, in train + self._do_train(world_size) + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 326, in _do_train + self._setup_train(world_size) + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 290, in _setup_train + self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=LOCAL_RANK, mode="train") + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/models/yolo/detect/train.py", line 82, in get_dataloader + dataset = self.build_dataset(dataset_path, mode, batch_size) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/models/yolo/detect/train.py", line 65, in build_dataset + return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/data/build.py", line 109, in build_yolo_dataset + return dataset( + ^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/data/dataset.py", line 87, in __init__ + super().__init__(*args, channels=self.data["channels"], **kwargs) + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/data/base.py", line 114, in __init__ + self.labels = self.get_labels() + ^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/data/dataset.py", line 173, in get_labels + cache, exists = self.cache_labels(cache_path), False # run cache ops + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/data/dataset.py", line 124, in cache_labels + for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: + ^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + ^^^^^^^^ + File "/usr/lib/python3.12/multiprocessing/pool.py", line 861, in next + self._cond.wait(timeout) + File "/usr/lib/python3.12/threading.py", line 355, in wait + waiter.acquire() +KeyboardInterrupt diff --git a/logs/training_log_yolov8n_New_Ds.txt b/logs/training_log_yolov8n_New_Ds.txt new file mode 100644 index 0000000000000000000000000000000000000000..f43a70dad4257fb0d019a49b29af0618a314f7fb --- /dev/null +++ b/logs/training_log_yolov8n_New_Ds.txt @@ -0,0 +1,348 @@ +nohup: ignoring input +New https://pypi.org/project/ultralytics/8.3.124 available 😃 Update with 'pip install -U ultralytics' +Ultralytics 8.3.123 🚀 Python-3.12.8 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce RTX 3090, 24257MiB) +engine/trainer: task=detect, mode=train, model=./base_models/yolov8n.pt, data=merged_data.yaml, epochs=50, time=None, patience=10, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=Activity_Detection_New_Ds, name=yolov8n.pt4, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=42, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.005, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=15, translate=0.2, scale=0.8, shear=10.0, perspective=0.001, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.2, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=Activity_Detection_New_Ds/yolov8n.pt4 +Overriding model.yaml nc=80 with nc=3 + + from n params module arguments + 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] + 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] + 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] + 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] + 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] + 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] + 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] + 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] + 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] + 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] + 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] + 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] + 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] + 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] + 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] + 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] + 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] + 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] + 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] + 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] + 22 [15, 18, 21] 1 751897 ultralytics.nn.modules.head.Detect [3, [64, 128, 256]] +Model summary: 129 layers, 3,011,433 parameters, 3,011,417 gradients, 8.2 GFLOPs + +Transferred 319/355 items from pretrained weights +Freezing layer 'model.22.dfl.conv.weight' +AMP: running Automatic Mixed Precision (AMP) checks... +AMP: checks passed ✅ +train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 6660.6±3048.8 MB/s, size: 1439.9 KB) + train: Scanning /home/ahanaf/Hackathon/Activity_OD/db_1/data/labels/train...: 0%| | 0/25747 [00:00 + model_name="yolov8n.pt", +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/Activity_OD/train.py", line 37, in train_model_and_store + + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/models/yolo/model.py", line 53, in __init__ + super().__init__(model=model, task=task, verbose=verbose) + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/model.py", line 147, in __init__ + self._load(model, task=task) + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/engine/model.py", line 289, in _load + self.model, self.ckpt = attempt_load_one_weight(weights) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 1306, in attempt_load_one_weight + ckpt, weight = torch_safe_load(weight) # load ckpt + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 1211, in torch_safe_load + ckpt = torch.load(file, map_location="cpu") + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/ultralytics/utils/patches.py", line 115, in torch_load + return _torch_load(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/torch/serialization.py", line 1479, in load + with _open_file_like(f, "rb") as opened_file: + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/torch/serialization.py", line 759, in _open_file_like + return _open_file(name_or_buffer, mode) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/ahanaf/Hackathon/venv/lib/python3.12/site-packages/torch/serialization.py", line 740, in __init__ + super().__init__(open(name, mode)) + ^^^^^^^^^^^^^^^^ +FileNotFoundError: [Errno 2] No such file or directory: 'Activity_Detection_New_Ds/yolov8n.pt/weights/best.pt' diff --git a/merge_datasets.py b/merge_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..4a2c39aa78cbb921bf943be7ea0426b64f2d4036 --- /dev/null +++ b/merge_datasets.py @@ -0,0 +1,54 @@ +import os + +# Define the paths for both datasets +db_1_path = '/home/ahanaf/Hackathon/Activity_OD/db_1' +db_2_path = '/home/ahanaf/Hackathon/Activity_OD/db_2' + +## Paths to the train.txt and Validation.txt files in both datasets +db_1_train_file = os.path.join(db_1_path, 'data', 'images', 'train.txt') +db_2_train_file = os.path.join(db_2_path, 'data', 'images', 'train.txt') +db_1_val_file = os.path.join(db_1_path, 'data', 'images', 'Validation.txt') +db_2_val_file = os.path.join(db_2_path, 'data', 'images', 'Validation.txt') + +# Define the merged output file paths +merged_train_file = os.path.join(db_1_path, 'data', 'images', 'merged_train.txt') +merged_val_file = os.path.join(db_1_path, 'data', 'images', 'merged_Validation.txt') + +# Function to merge txt files (train/val) and use absolute paths +def merge_txt_files(file1, file2, output_file, base_dir): + with open(file1, 'r') as f1, open(file2, 'r') as f2, open(output_file, 'w') as out: + # Read the contents of both files + f1_lines = f1.readlines() + f2_lines = f2.readlines() + + # Convert relative paths to absolute paths based on the dataset paths + f1_lines = [f'/home/ahanaf/Hackathon/Activity_OD/db_1/{line}' for line in f1_lines] + f2_lines = [f'/home/ahanaf/Hackathon/Activity_OD/db_2/{line}' for line in f2_lines] + # print(f1_lines[0]) + # exit() + + # Combine the absolute paths and write to the output file + out.writelines(f1_lines) + out.writelines(f2_lines) + +# Merge the train and validation txt files +merge_txt_files(db_1_train_file, db_2_train_file, merged_train_file, 'images') +merge_txt_files(db_1_val_file, db_2_val_file, merged_val_file, 'images') + +print(f"✅ Merged train.txt into {merged_train_file}") +print(f"✅ Merged Validation.txt into {merged_val_file}") + +# Now merge the data.yaml files +merged_data_yaml = os.path.join(db_1_path, 'data', 'merged_data.yaml') + +with open(merged_data_yaml, 'w') as f: + # Write the merged data.yaml with appropriate paths + f.write(f'path: /home/ahanaf/db_1\n') + f.write(f'train: data/images/merged_train.txt\n') + f.write(f'val: data/images/merged_Validation.txt\n') + f.write(f'names:\n') + f.write(f' 0: AWAY_FROM_DESK\n') + f.write(f' 1: WORKING\n') + f.write(f' 2: IDLE\n') + +print(f"✅ Merged data.yaml saved at {merged_data_yaml}") diff --git a/merged_data.yaml b/merged_data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b6c5b18ee946c71c132af6350be72139f1a20aa1 --- /dev/null +++ b/merged_data.yaml @@ -0,0 +1,7 @@ +path: /home/ahanaf/Hackathon/Activity_OD/merged_data +train: merged_train.txt +val: merged_Validation.txt +names: + 0: AWAY_FROM_DESK + 1: WORKING + 2: IDLE diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..c53962def1072dceb5463b85a7d2ad2da87c808d --- /dev/null +++ b/train.py @@ -0,0 +1,63 @@ +from ultralytics import YOLO + +def train_model_and_store(model_name, epochs, img_size, batch_size, device, optimizer, learning_rate): + # Load the pretrained model + model = YOLO(f'./base_models/{model_name}') + + model.train( + data='./BracU_Ds/data.yaml', + epochs=epochs, + imgsz=img_size, + batch=batch_size, + device=device, + project='Activity_Detection_New_BracU', + name=model_name, + optimizer=optimizer, + lr0=learning_rate, + patience=10, + plots=True, + seed=42, + # pretrained=True, + # degrees=15, + # translate=0.2, + # scale=0.8, + # shear=10.0, + # perspective=0.001, + # fliplr=0.5, + # mosaic=1.0, + # mixup=0.2, + # hsv_h=0.015, + # hsv_s=0.7, + # hsv_v=0.4 + ) + + # Load the best model after training + # best_model_path = f'./Activity_Detection_New_BracU/{model_name}/weights/best.pt' + # model = YOLO(best_model_path) + + # Evaluate the model + # model.val(project='Activity_Detection_New_Eval', name=model_name) + +if __name__ == "__main__": + train_model_and_store( + model_name="yolov8n.pt", + epochs=50, + img_size=640, + batch_size=64, + device=0, + optimizer='Adam', + learning_rate=0.005 + ) + + +# We should fine tune the following model +# yolo11n.pt +# yolov10n.pt +# yolov8n.pt +# yolov8m.pt +# yolo11n-cls.pt +# yolov8n-cls.pt + +# nohup python train.py > logs/training_log_yolov8n_BracU_Ds.txt 2>&1 & +# 2980863 + diff --git a/yolo11n.pt b/yolo11n.pt new file mode 100644 index 0000000000000000000000000000000000000000..c7723db027d009343e9682f261370833fd6f0d84 --- /dev/null +++ b/yolo11n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ebbc80d4a7680d14987a577cd21342b65ecfd94632bd9a8da63ae6417644ee1 +size 5613764