{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b8c66f43", "metadata": {}, "outputs": [], "source": [ "# Importy do całego projektu\n", "import os\n", "from IPython.display import display, clear_output\n", "from ultralytics import YOLO\n", "from pathlib import Path\n", "import cv2\n", "import numpy as np\n", "import random\n", "from tqdm import tqdm\n", "import shutil\n", "import gc" ] }, { "cell_type": "code", "execution_count": 2, "id": "c06cd52c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "torch.cuda.is_available()" ] }, { "cell_type": "code", "execution_count": 3, "id": "8997b216", "metadata": {}, "outputs": [], "source": [ "import kagglehub\n", "dataset_path1 = kagglehub.dataset_download(\"jessicali9530/lfw-dataset\")\n", "dataset_path2 = kagglehub.dataset_download(\"chiragsaipanuganti/morph\")\n", "dataset_path3 = kagglehub.dataset_download(\"tapakah68/facial-emotion-recognition\")\n", "dataset_path4 = kagglehub.dataset_download(\"atulanandjha/lfwpeople\")\n", "dataset_path5 = kagglehub.dataset_download(\"ashishjangra27/gender-recognition-200k-images-celeba\")\n", "dataset_path6 = kagglehub.dataset_download(\"trainingdatapro/age-detection-human-faces-18-60-years\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "29a5cbd7", "metadata": {}, "outputs": [], "source": [ "# Pobranie ścieżek do obrazów\n", "image_paths = []\n", "datasets = [dataset_path1, dataset_path2, dataset_path3, \n", "\t\t\tdataset_path4, dataset_path5, dataset_path6]\n", "for dataset_path in datasets:\n", " for root, dirs, files in os.walk(dataset_path):\n", " for file in files:\n", " if file.lower().endswith(('.jpg')):\n", " image_paths.append(os.path.join(root, file))" ] }, { "cell_type": "code", "execution_count": 5, "id": "deb582ce", "metadata": {}, "outputs": [], "source": [ "def populate_card_labels_v3(image_path, im_width=250):\n", "\timg = cv2.imread(image_path)\n", "\tif img is None:\n", "\t\treturn None, None\n", "\theight, width = img.shape[:2]\n", "\tim_height = int(height * im_width / width)\n", "\timg = cv2.resize(img, (im_width, im_height))\n", "\timg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", "\n", "\tdiagonal_size = int(np.sqrt(im_width**2 + im_height**2)) + 10\n", "\trot_canvas = np.ones((diagonal_size, diagonal_size, 3), dtype=np.uint8) * 255\n", "\tstart_x = (diagonal_size - im_width) // 2\n", "\tstart_y = (diagonal_size - im_height) // 2\n", "\trot_canvas[start_y:start_y + im_height, start_x:start_x + im_width] = img\n", "\n", "\trot_angle = random.uniform(-179, 180)\n", "\tcenter = (diagonal_size // 2, diagonal_size // 2)\n", "\trot_matrix = cv2.getRotationMatrix2D(center, rot_angle, 1.0)\n", "\trot_img = cv2.warpAffine(rot_canvas, rot_matrix, (diagonal_size, diagonal_size), borderValue=(255, 255, 255))\n", "\n", "\theight, width = rot_img.shape[:2]\n", "\tcard = np.ones((height+10, width+10, 3), dtype=np.uint8) * 255\n", "\tcard[5:5+height, 5:5+width] = rot_img\n", "\n", "\tcorners = np.array([\n", "\t\t[start_x, start_y],\n", "\t\t[start_x + im_width, start_y],\n", "\t\t[start_x + im_width, start_y + im_height],\n", "\t\t[start_x, start_y + im_height]\n", "\t], dtype=np.float32)\n", "\n", "\tones = np.ones((corners.shape[0], 1))\n", "\tcorners_homo = np.hstack([corners, ones])\n", "\trotated_corners = (rot_matrix @ corners_homo.T).T\n", "\trotated_corners += np.array([5, 5])\n", "\n", "\tx_min, y_min = rotated_corners.min(axis=0)\n", "\tx_max, y_max = rotated_corners.max(axis=0)\n", "\n", "\tbbox_cx = (x_min + x_max) / 2 / (width+10)\n", "\tbbox_cy = (y_min + y_max) / 2 / (height+10)\n", "\tbbox_w = (x_max - x_min) / (width+10)\n", "\tbbox_h = (y_max - y_min) / (height+10)\n", "\n", "\tnorm_corners = []\n", "\tfor x, y in rotated_corners:\n", "\t\tnorm_corners.extend([x / (width+10), y / (height+10), 2])\n", "\n", "\tlabel = [0, bbox_cx, bbox_cy, bbox_w, bbox_h] + norm_corners\n", "\n", "\treturn card, label\n", "\n", "def get_cards_labels(image_paths, num, im_width, output_path, starting_idx=0):\n", "\toutput_path = Path(output_path)\n", "\timages_path = \"images\" / output_path\n", "\tlabels_path = \"labels\" / output_path\n", "\timages_path.mkdir(parents=True, exist_ok=True)\n", "\tlabels_path.mkdir(parents=True, exist_ok=True)\n", "\twrong = 0\n", " \n", "\tfor i in range(starting_idx, starting_idx+num):\n", "\t\tcard, label = populate_card_labels_v3(\n", "\t\t\timage_paths[i],\n", "\t\t\tim_width=im_width\n", "\t\t)\n", "\t\tif card is None or label is None:\n", "\t\t\twrong += 1\n", "\t\t\tcontinue\n", "\n", "\t\tfilename = f\"card_{i+1}\"\n", "\t\timage_file = images_path / f\"{filename}.jpg\"\n", "\t\tlabel_file = labels_path / f\"{filename}.txt\"\n", " \n", "\t\tcard_bgr = cv2.cvtColor(card, cv2.COLOR_RGB2BGR)\n", "\t\tcv2.imwrite(str(image_file), card_bgr)\n", " \n", "\t\tlabel_line = \" \".join(f\"{v:.6f}\" if isinstance(v, float) else str(v) for v in label)\n", "\n", "\t\twith open(label_file, \"w\") as f:\n", "\t\t\tf.write(label_line)\n", "\n", "\t\tdel card_bgr\n", "\t\tdel card\n", "\t\tdel label\n", "\t\n", "\t\tprocessed_count_in_call = i - starting_idx + 1\n", "\t\tif processed_count_in_call % 50 == 0: # Adjust frequency as needed\n", "\t\t\tgc.collect()\n", "\t\n", " \n", "\tif wrong > 0:\n", "\t\tprint(f\"Skipped {wrong} images due to errors.\")\n", "\tgc.collect()\n", "\treturn None" ] }, { "cell_type": "code", "execution_count": 6, "id": "0bce0ae1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cards: 13%|█▎ | 2/15 [07:08<48:40, 224.67s/card]Invalid SOS parameters for sequential JPEG\n", "Generating cards: 20%|██ | 3/15 [15:25<1:09:52, 349.34s/card]Invalid SOS parameters for sequential JPEG\n", "Generating cards: 27%|██▋ | 4/15 [29:34<1:40:12, 546.61s/card]Invalid SOS parameters for sequential JPEG\n", "Generating cards: 100%|██████████| 15/15 [1:18:49<00:00, 315.29s/card] \n" ] } ], "source": [ "shutil.rmtree(\"images\", ignore_errors=True)\n", "shutil.rmtree(\"labels\", ignore_errors=True)\n", "\n", "random.shuffle(image_paths)\n", "\n", "i = 0\n", "max = len(image_paths)\n", "\n", "train_p = int(max * 0.9)\n", "valid_p = int(max * 0.05)\n", "test_p = max - train_p - valid_p\n", "\n", "res = [250, 500, 750, 1024, 1560]\n", "res_n = int(train_p / len(res))\n", "res_idx = 0-res_n\n", "\n", "iter = len(res)\n", "progress = None\n", "progress = tqdm(total=3*len(res), desc=\"Generating cards\", unit=\"card\")\n", "for j in res:\n", "\tres_idx += res_n\n", "\tget_cards_labels(image_paths, num=res_n, im_width=j, output_path='train', starting_idx=res_idx)\n", "\tprogress.update(1)\n", "res_n = int(valid_p / len(res))\n", "for j in res:\n", "\tres_idx += res_n\n", "\tget_cards_labels(image_paths, num=res_n, im_width=j, output_path='valid', starting_idx=res_idx)\n", "\tprogress.update(1)\n", "res_n = int(test_p / len(res))\n", "for j in res:\n", "\tres_idx += res_n\n", "\tget_cards_labels(image_paths, num=res_n, im_width=j, output_path='test', starting_idx=res_idx)\n", "\tprogress.update(1)\n", "\n", "if progress:\n", "\tprogress.close()" ] }, { "cell_type": "code", "execution_count": 46, "id": "2706ae83", "metadata": {}, "outputs": [], "source": [ "model = YOLO(\"runs/pose/image_rotation_model_train/weights/best.pt\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "fa113be4", "metadata": {}, "outputs": [], "source": [ "model = YOLO(\"yolo11x-pose.pt\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "15a84785", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New https://pypi.org/project/ultralytics/8.3.155 available 😃 Update with 'pip install -U ultralytics'\n", "Ultralytics 8.3.144 🚀 Python-3.11.9 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 4070 Ti SUPER, 16376MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=none, batch=32, bgr=0.0, box=7.5, cache=ram, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=image_rotation.yaml, degrees=5.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=3, erasing=0.0, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=416, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11x-pose.pt, momentum=0.937, mosaic=0.0, multi_scale=False, name=image_rotation_model_anew, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/pose/image_rotation_model_anew, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=pose, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[4, 3]\n", "Overriding model.yaml nc=80 with nc=1\n", "\n", " from n params module arguments \n", " 0 -1 1 2784 ultralytics.nn.modules.conv.Conv [3, 96, 3, 2] \n", " 1 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] \n", " 2 -1 2 389760 ultralytics.nn.modules.block.C3k2 [192, 384, 2, True, 0.25] \n", " 3 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n", " 4 -1 2 1553664 ultralytics.nn.modules.block.C3k2 [384, 768, 2, True, 0.25] \n", " 5 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] \n", " 6 -1 2 5022720 ultralytics.nn.modules.block.C3k2 [768, 768, 2, True] \n", " 7 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] \n", " 8 -1 2 5022720 ultralytics.nn.modules.block.C3k2 [768, 768, 2, True] \n", " 9 -1 1 1476864 ultralytics.nn.modules.block.SPPF [768, 768, 5] \n", " 10 -1 2 3264768 ultralytics.nn.modules.block.C2PSA [768, 768, 2] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 2 5612544 ultralytics.nn.modules.block.C3k2 [1536, 768, 2, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 2 1700352 ultralytics.nn.modules.block.C3k2 [1536, 384, 2, True] \n", " 17 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 2 5317632 ultralytics.nn.modules.block.C3k2 [1152, 768, 2, True] \n", " 20 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 2 5612544 ultralytics.nn.modules.block.C3k2 [1536, 768, 2, True] \n", " 23 [16, 19, 22] 1 5059063 ultralytics.nn.modules.head.Pose [1, [4, 3], [384, 768, 768]] \n", "YOLO11x-pose summary: 372 layers, 58,787,287 parameters, 58,787,271 gradients, 203.8 GFLOPs\n", "\n", "Transferred 1051/1057 items from pretrained weights\n", "Freezing layer 'model.23.dfl.conv.weight'\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 269.5±113.1 MB/s, size: 117.9 KB)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /repos/image-corner-rotation/labels/train... 251440 images, 0 backgrounds, 0 corrupt: 100%|██████████| 251440/251440 [03:58<00:00, 1055.79it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /repos/image-corner-rotation/labels/train.cache\n", "WARNING ⚠️ \u001b[34m\u001b[1mtrain: \u001b[0m182.4GB RAM required to cache images with 50% safety margin but only 44.0/49.0GB available, not caching images\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 144.2±142.5 MB/s, size: 111.4 KB)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mScanning /repos/image-corner-rotation/labels/valid... 13965 images, 0 backgrounds, 0 corrupt: 100%|██████████| 13965/13965 [00:13<00:00, 1027.42it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /repos/image-corner-rotation/labels/valid.cache\n", "WARNING ⚠️ cache='ram' may produce non-deterministic training results. Consider cache='disk' as a deterministic alternative if your disk space allows.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mCaching images (6.8GB RAM): 100%|██████████| 13965/13965 [00:12<00:00, 1074.73it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Plotting labels to runs/pose/image_rotation_model_anew/labels.jpg... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01, momentum=0.9) with parameter groups 173 weight(decay=0.0), 183 weight(decay=0.0005), 182 bias(decay=0.0)\n", "Image sizes 416 train, 416 val\n", "Using 8 dataloader workers\n", "Logging results to \u001b[1mruns/pose/image_rotation_model_anew\u001b[0m\n", "Starting training for 3 epochs...\n", "\n", " Epoch GPU_mem box_loss pose_loss kobj_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 1/3 12.9G 0.1887 0.23 0.1151 0.1374 0.954 16 416: 100%|██████████| 7858/7858 [47:57<00:00, 2.73it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95) Pose(P R mAP50 mAP50-95): 100%|██████████| 219/219 [01:48<00:00, 2.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 13965 13965 1 1 0.995 0.995 1 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Epoch GPU_mem box_loss pose_loss kobj_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 2/3 13.1G 0.119 0.05185 0.03086 0.09407 0.8949 16 416: 100%|██████████| 7858/7858 [46:42<00:00, 2.80it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95) Pose(P R mAP50 mAP50-95): 100%|██████████| 219/219 [02:01<00:00, 1.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 13965 13965 1 1 0.995 0.995 1 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Epoch GPU_mem box_loss pose_loss kobj_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 3/3 13.3G 0.09503 0.04164 0.02537 0.07738 0.8872 16 416: 100%|██████████| 7858/7858 [45:41<00:00, 2.87it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95) Pose(P R mAP50 mAP50-95): 100%|██████████| 219/219 [02:39<00:00, 1.37it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 13965 13965 1 1 0.995 0.995 1 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "3 epochs completed in 2.449 hours.\n", "Optimizer stripped from runs/pose/image_rotation_model_anew/weights/last.pt, 118.2MB\n", "Optimizer stripped from runs/pose/image_rotation_model_anew/weights/best.pt, 118.2MB\n", "\n", "Validating runs/pose/image_rotation_model_anew/weights/best.pt...\n", "Ultralytics 8.3.144 🚀 Python-3.11.9 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 4070 Ti SUPER, 16376MiB)\n", "YOLO11x-pose summary (fused): 199 layers, 58,739,959 parameters, 0 gradients, 202.7 GFLOPs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " Class Images Instances Box(P R mAP50 mAP50-95) Pose(P R mAP50 mAP50-95): 100%|██████████| 219/219 [01:36<00:00, 2.27it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 13965 13965 1 1 0.995 0.995 1 1 0.995 0.995\n", "Speed: 0.0ms preprocess, 3.0ms inference, 0.0ms loss, 0.9ms postprocess per image\n", "Results saved to \u001b[1mruns/pose/image_rotation_model_anew\u001b[0m\n" ] }, { "data": { "text/plain": [ "ultralytics.utils.metrics.PoseMetrics object with attributes:\n", "\n", "ap_class_index: array([0])\n", "box: ultralytics.utils.metrics.Metric object\n", "confusion_matrix: \n", "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)', 'Precision-Recall(P)', 'F1-Confidence(P)', 'Precision-Confidence(P)', 'Recall-Confidence(P)']\n", "curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n", " 0.072072, 0.073073, 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'metrics/recall(P)', 'metrics/mAP50(P)', 'metrics/mAP50-95(P)']\n", "maps: array([ 1.9899])\n", "names: {0: 'rotated_photo'}\n", "plot: True\n", "pose: ultralytics.utils.metrics.Metric object\n", "results_dict: {'metrics/precision(B)': np.float64(1.0), 'metrics/recall(B)': np.float64(1.0), 'metrics/mAP50(B)': np.float64(0.995), 'metrics/mAP50-95(B)': np.float64(0.9949697636350244), 'metrics/precision(P)': np.float64(0.9998567848191908), 'metrics/recall(P)': np.float64(0.9998567848191908), 'metrics/mAP50(P)': np.float64(0.9949492500076094), 'metrics/mAP50-95(P)': np.float64(0.9949489635772478), 'fitness': np.float64(1.989921779491806)}\n", "save_dir: PosixPath('runs/pose/image_rotation_model_anew')\n", "seg: ultralytics.utils.metrics.Metric object\n", "speed: {'preprocess': 0.041050202436414426, 'inference': 3.031985307482032, 'loss': 9.123995764002789e-05, 'postprocess': 0.8749647458659169}\n", "task: 'pose'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.train(\n", " data='image_rotation.yaml',\n", " epochs=3,\n", " imgsz=416,\n", " batch=32,\n", " degrees=5.0,\n", " mosaic=0.0,\n", " auto_augment='none',\n", " erasing=0.0,\n", " cache='ram',\n", " name='image_rotation_model_anew',\n", " exist_ok=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 35, "id": "b1d4c2b7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "WARNING ⚠️ \n", "inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n", "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n", "\n", "Example:\n", " results = model(source=..., stream=True) # generator of Results objects\n", " for r in results:\n", " boxes = r.boxes # Boxes object for bbox outputs\n", " masks = r.masks # Masks object for segment masks outputs\n", " probs = r.probs # Class probabilities for classification outputs\n", "\n", "image 1/1320 /repos/image-corner-rotation/images/test/card_11906.jpg: 416x416 1 rotated_photo, 14.8ms\n", "image 2/1320 /repos/image-corner-rotation/images/test/card_11907.jpg: 416x416 1 rotated_photo, 31.7ms\n", "image 3/1320 /repos/image-corner-rotation/images/test/card_11908.jpg: 416x416 1 rotated_photo, 21.0ms\n", "image 4/1320 /repos/image-corner-rotation/images/test/card_11909.jpg: 416x416 1 rotated_photo, 15.2ms\n", "image 5/1320 /repos/image-corner-rotation/images/test/card_11910.jpg: 416x416 1 rotated_photo, 17.8ms\n", "image 6/1320 /repos/image-corner-rotation/images/test/card_11911.jpg: 416x416 1 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/repos/image-corner-rotation/images/test/card_13199.jpg: 416x416 1 rotated_photo, 15.9ms\n", "image 1295/1320 /repos/image-corner-rotation/images/test/card_13200.jpg: 416x416 1 rotated_photo, 16.8ms\n", "image 1296/1320 /repos/image-corner-rotation/images/test/card_13201.jpg: 416x416 1 rotated_photo, 18.2ms\n", "image 1297/1320 /repos/image-corner-rotation/images/test/card_13202.jpg: 416x416 1 rotated_photo, 14.9ms\n", "image 1298/1320 /repos/image-corner-rotation/images/test/card_13203.jpg: 416x416 1 rotated_photo, 17.0ms\n", "image 1299/1320 /repos/image-corner-rotation/images/test/card_13204.jpg: 416x416 1 rotated_photo, 14.9ms\n", "image 1300/1320 /repos/image-corner-rotation/images/test/card_13205.jpg: 416x416 1 rotated_photo, 18.0ms\n", "image 1301/1320 /repos/image-corner-rotation/images/test/card_13206.jpg: 416x416 1 rotated_photo, 13.9ms\n", "image 1302/1320 /repos/image-corner-rotation/images/test/card_13207.jpg: 416x416 1 rotated_photo, 16.0ms\n", "image 1303/1320 /repos/image-corner-rotation/images/test/card_13208.jpg: 416x416 1 rotated_photo, 19.2ms\n", "image 1304/1320 /repos/image-corner-rotation/images/test/card_13209.jpg: 416x416 1 rotated_photo, 19.8ms\n", "image 1305/1320 /repos/image-corner-rotation/images/test/card_13210.jpg: 416x416 1 rotated_photo, 17.1ms\n", "image 1306/1320 /repos/image-corner-rotation/images/test/card_13211.jpg: 416x416 1 rotated_photo, 20.7ms\n", "image 1307/1320 /repos/image-corner-rotation/images/test/card_13212.jpg: 416x416 1 rotated_photo, 39.0ms\n", "image 1308/1320 /repos/image-corner-rotation/images/test/card_13213.jpg: 416x416 1 rotated_photo, 16.8ms\n", "image 1309/1320 /repos/image-corner-rotation/images/test/card_13214.jpg: 416x416 1 rotated_photo, 22.5ms\n", "image 1310/1320 /repos/image-corner-rotation/images/test/card_13215.jpg: 416x416 1 rotated_photo, 19.8ms\n", "image 1311/1320 /repos/image-corner-rotation/images/test/card_13216.jpg: 416x416 1 rotated_photo, 20.7ms\n", "image 1312/1320 /repos/image-corner-rotation/images/test/card_13217.jpg: 416x416 1 rotated_photo, 20.7ms\n", "image 1313/1320 /repos/image-corner-rotation/images/test/card_13218.jpg: 416x416 1 rotated_photo, 19.5ms\n", "image 1314/1320 /repos/image-corner-rotation/images/test/card_13219.jpg: 416x416 1 rotated_photo, 14.8ms\n", "image 1315/1320 /repos/image-corner-rotation/images/test/card_13220.jpg: 416x416 1 rotated_photo, 18.1ms\n", "image 1316/1320 /repos/image-corner-rotation/images/test/card_13221.jpg: 416x416 1 rotated_photo, 23.6ms\n", "image 1317/1320 /repos/image-corner-rotation/images/test/card_13222.jpg: 416x416 1 rotated_photo, 19.3ms\n", "image 1318/1320 /repos/image-corner-rotation/images/test/card_13223.jpg: 416x416 1 rotated_photo, 17.4ms\n", "image 1319/1320 /repos/image-corner-rotation/images/test/card_13224.jpg: 416x416 1 rotated_photo, 27.5ms\n", "image 1320/1320 /repos/image-corner-rotation/images/test/card_13225.jpg: 416x416 1 rotated_photo, 17.4ms\n", "Speed: 0.7ms preprocess, 18.0ms inference, 1.8ms postprocess per image at shape (1, 3, 416, 416)\n", "Results saved to \u001b[1mruns/pose/predict\u001b[0m\n" ] } ], "source": [ "model = YOLO('runs/pose/image_rotation_model_train/weights/best.pt')\n", "\n", "results = model.predict(\n", " source='images/test',\n", " save=True,\n", " conf=0.25,\n", " imgsz=416\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }