diff --git a/run_performance_modelos.ipynb b/run_performance_modelos.ipynb
index b80ae40af84058de579cfd52cfa65bf6049149bd..186a35edd2c78100d733af83a8d7942a2ccd5bbb 100755
--- a/run_performance_modelos.ipynb
+++ b/run_performance_modelos.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -32,7 +32,7 @@
"YOLOv9c summary: 618 layers, 25590912 parameters, 0 gradients, 104.0 GFLOPs\n",
"New https://pypi.org/project/ultralytics/8.2.35 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics YOLOv8.2.2 🚀 Python-3.10.12 torch-2.3.0+rocm6.0 CUDA:0 (AMD Radeon RX 6700 XT, 12272MiB)\n",
- "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov9c.pt, data=datasets/v6 - data augmentation/data.yaml, epochs=300, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train2, exist_ok=False, pretrained=True, optimizer=AdamW, verbose=True, seed=0, 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, save_hybrid=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=False, opset=None, workspace=4, nms=False, lr0=0.00868, lrf=0.01661, momentum=0.85978, weight_decay=0.00074, warmup_epochs=4.85808, warmup_momentum=0.9448, warmup_bias_lr=0.1, box=5.82113, cls=0.49328, dfl=1.1396, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.01256, hsv_s=0.51594, hsv_v=0.17726, degrees=0.0, translate=0.12564, scale=0.46438, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.76665, bgr=0.0, mosaic=0.61458, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=datasets/v6 - data augmentation/best_hyperparameters.yaml, tracker=botsort.yaml, save_dir=runs/detect/train2\n",
+ "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov9c.pt, data=datasets/v6 - data augmentation/data.yaml, epochs=130, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=AdamW, verbose=True, seed=0, 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, save_hybrid=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=False, opset=None, workspace=4, nms=False, lr0=0.00868, lrf=0.01661, momentum=0.85978, weight_decay=0.00074, warmup_epochs=4.85808, warmup_momentum=0.9448, warmup_bias_lr=0.1, box=5.82113, cls=0.49328, dfl=1.1396, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.01256, hsv_s=0.51594, hsv_v=0.17726, degrees=0.0, translate=0.12564, scale=0.46438, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.76665, bgr=0.0, mosaic=0.61458, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=datasets/v6 - data augmentation/best_hyperparameters.yaml, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
"Overriding model.yaml nc=80 with nc=3\n",
"\n",
" from n params module arguments \n",
@@ -79,12 +79,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Plotting labels to runs/detect/train2/labels.jpg... \n",
+ "Plotting labels to runs/detect/train3/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.00868, momentum=0.85978) with parameter groups 154 weight(decay=0.0), 161 weight(decay=0.00074), 160 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
- "Logging results to \u001b[1mruns/detect/train2\u001b[0m\n",
- "Starting training for 300 epochs...\n",
+ "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
+ "Starting training for 130 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
@@ -93,26 +93,4124 @@
"name": "stderr",
"output_type": "stream",
"text": [
- " 1/300 10.8G 1.616 3.6 1.727 38 640: 11%|â–ˆ | 26/240 [00:20<02:50, 1.26it/s]\n"
+ " 1/130 11.5G 1.996 3.592 2.201 30 640: 100%|██████████| 240/240 [02:54<00:00, 1.38it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:06<00:00, 1.29it/s]"
]
},
{
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[3], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39minfo()\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Falta lo que usan para ir salvando y el resume\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdatasets/v6 - data augmentation/data.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAdamW\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m300\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m640\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdatasets/v6 - data augmentation/best_hyperparameters.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/ultralytics/engine/model.py:673\u001b[0m, in \u001b[0;36mModel.train\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 672\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mhub_session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msession \u001b[38;5;66;03m# attach optional HUB session\u001b[39;00m\n\u001b[0;32m--> 673\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;66;03m# Update model and cfg after training\u001b[39;00m\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m}:\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/ultralytics/engine/trainer.py:199\u001b[0m, in \u001b[0;36mBaseTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 196\u001b[0m ddp_cleanup(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mstr\u001b[39m(file))\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 199\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mworld_size\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/ultralytics/engine/trainer.py:383\u001b[0m, in \u001b[0;36mBaseTrainer._do_train\u001b[0;34m(self, world_size)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;66;03m# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html\u001b[39;00m\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ni \u001b[38;5;241m-\u001b[39m last_opt_step \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccumulate:\n\u001b[0;32m--> 383\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 384\u001b[0m last_opt_step \u001b[38;5;241m=\u001b[39m ni\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Timed stopping\u001b[39;00m\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/ultralytics/engine/trainer.py:544\u001b[0m, in \u001b[0;36mBaseTrainer.optimizer_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler\u001b[38;5;241m.\u001b[39munscale_(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer) \u001b[38;5;66;03m# unscale gradients\u001b[39;00m\n\u001b[1;32m 543\u001b[0m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mclip_grad_norm_(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mparameters(), max_norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m) \u001b[38;5;66;03m# clip gradients\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py:453\u001b[0m, in \u001b[0;36mGradScaler.step\u001b[0;34m(self, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munscale_(optimizer)\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28mlen\u001b[39m(optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound_inf_per_device\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 451\u001b[0m ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo inf checks were recorded for this optimizer.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 453\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_opt_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 455\u001b[0m optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstage\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m OptState\u001b[38;5;241m.\u001b[39mSTEPPED\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py:350\u001b[0m, in \u001b[0;36mGradScaler._maybe_opt_step\u001b[0;34m(self, optimizer, optimizer_state, *args, **kwargs)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_maybe_opt_step\u001b[39m(\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 344\u001b[0m optimizer: torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mOptimizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 348\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 349\u001b[0m retval: Optional[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43msum\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moptimizer_state\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf_per_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 351\u001b[0m retval \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mstep(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
- "File \u001b[0;32m~/Documentos/Taller_TSCF_Weed/venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py:350\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_maybe_opt_step\u001b[39m(\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 344\u001b[0m optimizer: torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mOptimizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 348\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 349\u001b[0m retval: Optional[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28msum\u001b[39m(\u001b[43mv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound_inf_per_device\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 351\u001b[0m retval \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mstep(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
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+ "name": "stderr",
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+ " 2/130 11.6G 1.964 3.438 2.177 39 640: 100%|██████████| 240/240 [02:51<00:00, 1.40it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.48it/s]"
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+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
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+ " 3/130 11.6G 1.876 3.238 2.071 87 640: 100%|██████████| 240/240 [02:51<00:00, 1.40it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.52it/s]"
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+ " 4/130 11.6G 1.81 3.136 1.989 41 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.58it/s]"
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+ " 5/130 11.6G 1.745 3.025 1.933 35 640: 100%|██████████| 240/240 [02:49<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.47it/s]"
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+ " 6/130 11.6G 1.725 2.939 1.904 54 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.50it/s]"
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+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.63it/s]"
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+ " 8/130 11.6G 1.661 2.738 1.837 32 640: 100%|██████████| 240/240 [02:49<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.56it/s]"
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+ " 9/130 11.6G 1.637 2.704 1.795 34 640: 100%|██████████| 240/240 [02:49<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.53it/s]"
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+ " 10/130 11.6G 1.614 2.666 1.793 41 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.63it/s]"
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+ "output_type": "stream",
+ "text": [
+ " 11/130 11.6G 1.589 2.574 1.749 39 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.50it/s]"
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+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.64it/s]"
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+ " 13/130 11.6G 1.549 2.461 1.707 77 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.56it/s]"
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+ " 14/130 11.6G 1.552 2.442 1.69 24 640: 100%|██████████| 240/240 [02:49<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.61it/s]"
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+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
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+ "output_type": "stream",
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+ " 15/130 11.6G 1.525 2.419 1.667 19 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.60it/s]"
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+ "output_type": "stream",
+ "text": [
+ " 16/130 11.6G 1.506 2.363 1.652 32 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.67it/s]"
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+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
+ " 129/130 11.6G 0.7252 0.6664 0.9978 6 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
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+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 130/130 11.6G 0.7187 0.6602 0.9853 52 640: 100%|██████████| 240/240 [02:50<00:00, 1.41it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.55it/s]"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "130 epochs completed in 6.334 hours.\n",
+ "Optimizer stripped from runs/detect/train3/weights/last.pt, 51.6MB\n",
+ "Optimizer stripped from runs/detect/train3/weights/best.pt, 51.6MB\n",
+ "\n",
+ "Validating runs/detect/train3/weights/best.pt...\n",
+ "Ultralytics YOLOv8.2.2 🚀 Python-3.10.12 torch-2.3.0+rocm6.0 CUDA:0 (AMD Radeon RX 6700 XT, 12272MiB)\n",
+ "YOLOv9c summary (fused): 384 layers, 25321561 parameters, 0 gradients, 102.3 GFLOPs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 9/9 [00:03<00:00, 2.41it/s]\n"
]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
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+ " Lolium 273 380 0.493 0.284 0.3 0.151\n",
+ " cyperus 273 252 0.544 0.46 0.485 0.252\n",
+ " ipomoea 273 315 0.726 0.849 0.886 0.742\n",
+ "Speed: 0.2ms preprocess, 9.7ms inference, 1.0ms loss, 0.5ms postprocess per image\n",
+ "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
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+ " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n",
+ " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n",
+ " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n",
+ " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n",
+ " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n",
+ " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n",
+ " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n",
+ " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n",
+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0.00015416, 7.7078e-05, 0],\n",
+ " [ 1, 1, 1, ..., 0.0006256, 0.0003128, 0],\n",
+ " [ 1, 1, 1, ..., 0.012042, 0.006021, 0]]), 'Recall', 'Precision'], [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",
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+ " 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",
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+ " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n",
+ " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n",
+ " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n",
+ " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n",
+ " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n",
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+ " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n",
+ " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n",
+ " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n",
+ " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n",
+ " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n",
+ " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n",
+ " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n",
+ " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n",
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+ " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n",
+ " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n",
+ " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n",
+ " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n",
+ " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n",
+ " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n",
+ " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n",
+ " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n",
+ " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n",
+ " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n",
+ " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n",
+ " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n",
+ " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n",
+ " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n",
+ " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n",
+ " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n",
+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.054299, 0.054299, 0.065532, ..., 0, 0, 0],\n",
+ " [ 0.13356, 0.13356, 0.1635, ..., 0, 0, 0],\n",
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+ " [ 0.073161, 0.073161, 0.091695, ..., 1, 1, 1],\n",
+ " [ 0.32462, 0.32462, 0.37625, ..., 1, 1, 1]]), 'Confidence', 'Precision'], [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",
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+ " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n",
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+ " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n",
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+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.63158, 0.63158, 0.62368, ..., 0, 0, 0],\n",
+ " [ 0.76587, 0.76587, 0.75397, ..., 0, 0, 0],\n",
+ " [ 0.94603, 0.94603, 0.94286, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n",
+ "fitness: 0.3990209864395032\n",
+ "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
+ "maps: array([ 0.15051, 0.25184, 0.74211])\n",
+ "names: {0: 'Lolium', 1: 'cyperus', 2: 'ipomoea'}\n",
+ "plot: True\n",
+ "results_dict: {'metrics/precision(B)': 0.5876138256567858, 'metrics/recall(B)': 0.5312263449495419, 'metrics/mAP50(B)': 0.5568237263316268, 'metrics/mAP50-95(B)': 0.38148734867371165, 'fitness': 0.3990209864395032}\n",
+ "save_dir: PosixPath('runs/detect/train3')\n",
+ "speed: {'preprocess': 0.19636695638244406, 'inference': 9.675540330209138, 'loss': 0.9990410927014475, 'postprocess': 0.4803304707174336}\n",
+ "task: 'detect'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
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diff --git a/runs/detect/train3/args.yaml b/runs/detect/train3/args.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5510ccaab37f220c828f315db6466fb51301cb7e
--- /dev/null
+++ b/runs/detect/train3/args.yaml
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+task: detect
+mode: train
+model: yolov9c.pt
+data: datasets/v6 - data augmentation/data.yaml
+epochs: 130
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: train3
+exist_ok: false
+pretrained: true
+optimizer: AdamW
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+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: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.00868
+lrf: 0.01661
+momentum: 0.85978
+weight_decay: 0.00074
+warmup_epochs: 4.85808
+warmup_momentum: 0.9448
+warmup_bias_lr: 0.1
+box: 5.82113
+cls: 0.49328
+dfl: 1.1396
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.01256
+hsv_s: 0.51594
+hsv_v: 0.17726
+degrees: 0.0
+translate: 0.12564
+scale: 0.46438
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.76665
+bgr: 0.0
+mosaic: 0.61458
+mixup: 0.0
+copy_paste: 0.0
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: datasets/v6 - data augmentation/best_hyperparameters.yaml
+tracker: botsort.yaml
+save_dir: runs/detect/train3
diff --git a/runs/detect/train3/confusion_matrix.png b/runs/detect/train3/confusion_matrix.png
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diff --git a/runs/detect/train3/results.csv b/runs/detect/train3/results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d519a6c99228528ee71c64d17a56dd4d9d68de7e
--- /dev/null
+++ b/runs/detect/train3/results.csv
@@ -0,0 +1,131 @@
+ epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
+ 1, 1.9961, 3.5922, 2.2006, 0.35142, 0.0418, 0.00543, 0.00138, 2.5825, 68.765, 4.3949, 0.081282, 0.0017792, 0.0017792
+ 2, 1.9641, 3.4383, 2.1771, 0.12665, 0.12302, 0.01351, 0.00431, 2.2842, 4.8025, 2.586, 0.062458, 0.0035388, 0.0035388
+ 3, 1.8758, 3.2383, 2.071, 0.77328, 0.11746, 0.1078, 0.04351, 1.9586, 4.2863, 2.1598, 0.043608, 0.0052714, 0.0052714
+ 4, 1.8099, 3.136, 1.9894, 0.09836, 0.10265, 0.06528, 0.028, 2.2642, 3.9116, 2.5603, 0.02473, 0.006977, 0.006977
+ 5, 1.7449, 3.0249, 1.933, 0.4886, 0.12857, 0.10621, 0.049, 1.8714, 3.5695, 1.9917, 0.0084174, 0.0084174, 0.0084174
+ 6, 1.7251, 2.939, 1.904, 0.67897, 0.19055, 0.21397, 0.11177, 1.6501, 2.716, 1.7959, 0.0083517, 0.0083517, 0.0083517
+ 7, 1.6862, 2.8471, 1.8533, 0.46621, 0.2029, 0.18093, 0.09443, 1.8375, 2.6598, 1.9437, 0.008286, 0.008286, 0.008286
+ 8, 1.6611, 2.7381, 1.8369, 0.22716, 0.23942, 0.20105, 0.09057, 1.8279, 2.9935, 1.974, 0.0082204, 0.0082204, 0.0082204
+ 9, 1.6366, 2.7043, 1.7947, 0.22837, 0.20869, 0.17905, 0.08438, 1.7171, 2.832, 1.8659, 0.0081547, 0.0081547, 0.0081547
+ 10, 1.6139, 2.666, 1.7931, 0.37961, 0.30939, 0.27042, 0.1295, 1.6377, 2.3729, 1.7824, 0.0080891, 0.0080891, 0.0080891
+ 11, 1.5889, 2.5736, 1.7485, 0.43807, 0.26986, 0.25712, 0.147, 1.5998, 2.4841, 1.7171, 0.0080234, 0.0080234, 0.0080234
+ 12, 1.5858, 2.5293, 1.7301, 0.8788, 0.2204, 0.26977, 0.14804, 1.5557, 2.4994, 1.7415, 0.0079577, 0.0079577, 0.0079577
+ 13, 1.5488, 2.4611, 1.7069, 0.49038, 0.3157, 0.32547, 0.17072, 1.5255, 2.5723, 1.6515, 0.0078921, 0.0078921, 0.0078921
+ 14, 1.5522, 2.4422, 1.6899, 0.42109, 0.26271, 0.26271, 0.13822, 1.5979, 2.6132, 1.7502, 0.0078264, 0.0078264, 0.0078264
+ 15, 1.5247, 2.4187, 1.6666, 0.38277, 0.35776, 0.34358, 0.18785, 1.4565, 2.2498, 1.5857, 0.0077608, 0.0077608, 0.0077608
+ 16, 1.5057, 2.3626, 1.6516, 0.38834, 0.3191, 0.28581, 0.16177, 1.5305, 2.5754, 1.6823, 0.0076951, 0.0076951, 0.0076951
+ 17, 1.4975, 2.3114, 1.6522, 0.52718, 0.36229, 0.3711, 0.19614, 1.4137, 1.9459, 1.5399, 0.0076294, 0.0076294, 0.0076294
+ 18, 1.4728, 2.2849, 1.6228, 0.43104, 0.36299, 0.33445, 0.18109, 1.5082, 2.163, 1.6161, 0.0075638, 0.0075638, 0.0075638
+ 19, 1.4816, 2.2774, 1.6285, 0.44551, 0.36019, 0.34314, 0.17705, 1.506, 2.1185, 1.6426, 0.0074981, 0.0074981, 0.0074981
+ 20, 1.4486, 2.2456, 1.6067, 0.93946, 0.23867, 0.34344, 0.20199, 1.493, 2.2175, 1.612, 0.0074325, 0.0074325, 0.0074325
+ 21, 1.4149, 2.1602, 1.5759, 0.50147, 0.38567, 0.39101, 0.22951, 1.3441, 1.8783, 1.5076, 0.0073668, 0.0073668, 0.0073668
+ 22, 1.4303, 2.1848, 1.5836, 0.45649, 0.35227, 0.35639, 0.19783, 1.4758, 2.0535, 1.5995, 0.0073011, 0.0073011, 0.0073011
+ 23, 1.4308, 2.1502, 1.5753, 0.51548, 0.35979, 0.38726, 0.2135, 1.3972, 1.9967, 1.5272, 0.0072355, 0.0072355, 0.0072355
+ 24, 1.3909, 2.0968, 1.5548, 0.57763, 0.40447, 0.42658, 0.23828, 1.3234, 1.8423, 1.4784, 0.0071698, 0.0071698, 0.0071698
+ 25, 1.3996, 2.094, 1.5527, 0.55566, 0.3742, 0.40699, 0.24213, 1.3212, 1.8166, 1.4535, 0.0071042, 0.0071042, 0.0071042
+ 26, 1.3798, 2.0679, 1.5298, 0.51944, 0.43102, 0.43472, 0.23729, 1.3869, 1.8067, 1.5082, 0.0070385, 0.0070385, 0.0070385
+ 27, 1.3782, 2.0429, 1.5309, 0.59973, 0.39487, 0.41974, 0.23311, 1.3993, 1.7771, 1.4974, 0.0069728, 0.0069728, 0.0069728
+ 28, 1.3564, 1.9958, 1.5077, 0.56653, 0.41094, 0.42465, 0.24725, 1.3267, 1.7738, 1.4687, 0.0069072, 0.0069072, 0.0069072
+ 29, 1.3583, 1.9994, 1.5135, 0.48175, 0.38709, 0.38819, 0.22088, 1.367, 2.022, 1.4704, 0.0068415, 0.0068415, 0.0068415
+ 30, 1.3617, 1.9831, 1.507, 0.59775, 0.36594, 0.41646, 0.25753, 1.2518, 1.8487, 1.4124, 0.0067759, 0.0067759, 0.0067759
+ 31, 1.3414, 1.9386, 1.492, 0.54868, 0.43236, 0.45805, 0.27824, 1.2611, 1.6667, 1.4083, 0.0067102, 0.0067102, 0.0067102
+ 32, 1.3391, 1.9177, 1.485, 0.56849, 0.42568, 0.45446, 0.27271, 1.3012, 1.651, 1.4466, 0.0066445, 0.0066445, 0.0066445
+ 33, 1.3364, 1.9105, 1.4856, 0.46832, 0.40209, 0.40069, 0.2317, 1.3102, 1.8778, 1.4687, 0.0065789, 0.0065789, 0.0065789
+ 34, 1.3389, 1.902, 1.4816, 0.51671, 0.45955, 0.45271, 0.26498, 1.2644, 1.6674, 1.4099, 0.0065132, 0.0065132, 0.0065132
+ 35, 1.3405, 1.8856, 1.4775, 0.52902, 0.44353, 0.46015, 0.26108, 1.3062, 1.6669, 1.4118, 0.0064476, 0.0064476, 0.0064476
+ 36, 1.3132, 1.8574, 1.4662, 0.54695, 0.44915, 0.47421, 0.2672, 1.2969, 1.5679, 1.4249, 0.0063819, 0.0063819, 0.0063819
+ 37, 1.3241, 1.8433, 1.4572, 0.61635, 0.44192, 0.47858, 0.28546, 1.2653, 1.5739, 1.3955, 0.0063162, 0.0063162, 0.0063162
+ 38, 1.2883, 1.8005, 1.4461, 0.53871, 0.45255, 0.46515, 0.26593, 1.3229, 1.6239, 1.4296, 0.0062506, 0.0062506, 0.0062506
+ 39, 1.3006, 1.7883, 1.4395, 0.64148, 0.44009, 0.4839, 0.29694, 1.2216, 1.5274, 1.3632, 0.0061849, 0.0061849, 0.0061849
+ 40, 1.2748, 1.7529, 1.4217, 0.57236, 0.46099, 0.47958, 0.27475, 1.3036, 1.5916, 1.4055, 0.0061193, 0.0061193, 0.0061193
+ 41, 1.2828, 1.7889, 1.4394, 0.52965, 0.47005, 0.48352, 0.29393, 1.2286, 1.547, 1.357, 0.0060536, 0.0060536, 0.0060536
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