\n",
+ " ```\n",
+ " In the event that the repository does not exist, it will be created automatically on your behalf. "
+ ],
+ "metadata": {
+ "id": "1PW42cdcweqa"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Additinal parameters\n",
+ "\n",
+ "```\n",
+ ":param experiment_name: Name used for logging and loading purposes\n",
+ ":param storage_location: If set to 's3' (i.e. s3://my-bucket) saves the Checkpoints in AWS S3 otherwise saves the Checkpoints Locally\n",
+ ":param resumed: If true, then old tensorboard files will **NOT** be deleted when tb_files_user_prompt=True\n",
+ ":param training_params: training_params for the experiment.\n",
+ ":param checkpoints_dir_path: Local root directory path where all experiment logging directories will reside.\n",
+ ":param tb_files_user_prompt: Asks user for Tensorboard deletion prompt.\n",
+ ":param launch_tensorboard: Whether to launch a TensorBoard process.\n",
+ ":param tensorboard_port: Specific port number for the tensorboard to use when launched (when set to None, some free port number will be used\n",
+ ":param save_checkpoints_remote: Saves checkpoints in s3 and DagsHub.\n",
+ ":param save_tensorboard_remote: Saves tensorboard in s3.\n",
+ ":param save_logs_remote: Saves log files in s3 and DagsHub.\n",
+ ":param monitor_system: Save the system statistics (GPU utilization, CPU, ...) in the tensorboard\n",
+ ":param dagshub_auth: Provide dagshub authentication token for automatic pipeline\n",
+ ":param log_mlflow_only: Skip logging to DVC, use MLflow for all artifacts being logged\n",
+ "```"
+ ],
+ "metadata": {
+ "id": "bpAfmCyOwgmK"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_params = {\n",
+ " \"sg_logger\": \"dagshub_sg_logger\",\n",
+ " \"sg_logger_params\": # Params that will be passes to __init__ of the logger super_gradients.common.sg_loggers.dagshub_sg_logger.DagsHubSGLogger\n",
+ " {\n",
+ " \"dagshub_repository\": \"nirbarazida/yolonax_cifar\", # Optional: Your DagsHub project name, consisting of the owner name, followed by '/', and the repo name. If this is left empty, you'll be prompted in your run to fill it in manually.\n",
+ " \"log_mlflow_only\": False, # Optional: Change to true to bypass logging to DVC, and log all artifacts only to MLflow \n",
+ " \"save_checkpoints_remote\": True,\n",
+ " \"save_tensorboard_remote\": True,\n",
+ " \"save_logs_remote\": True,\n",
+ " },\n",
+ " # ENABLING SILENT MODE\n",
+ " \"average_best_models\":True,\n",
+ " \"warmup_mode\": \"linear_epoch_step\",\n",
+ " # \"warmup_initial_lr\": 1e-2,#1e-6,\n",
+ " \"lr_warmup_epochs\": 3,\n",
+ " \"initial_lr\": 1e-2,\n",
+ " \"lr_mode\": \"cosine\",\n",
+ " \"cosine_final_lr_ratio\": 0.1,\n",
+ " \"optimizer\": \"SGD\",\n",
+ " # \"optimizer_params\": {\"weight_decay\": 0.0001},\n",
+ " \"zero_weight_decay_on_bias_and_bn\": True,\n",
+ " \"ema\": True,\n",
+ " \"ema_params\": {\"decay\": 0.9, \"decay_type\": \"threshold\"},\n",
+ " # ONLY TRAINING FOR 10 EPOCHS FOR THIS EXAMPLE NOTEBOOK\n",
+ " \"max_epochs\": 3,\n",
+ " \"mixed_precision\": True,\n",
+ " \"loss\": PPYoloELoss(\n",
+ " use_static_assigner=False,\n",
+ " # NOTE: num_classes needs to be defined here\n",
+ " num_classes=len(train_dataloader.dataset.dataset_params[\"class_inclusion_list\"]),\n",
+ " reg_max=16\n",
+ " ),\n",
+ " \"valid_metrics_list\": [\n",
+ " DetectionMetrics_050(\n",
+ " score_thres=0.1,\n",
+ " top_k_predictions=300,\n",
+ " # NOTE: num_classes needs to be defined here\n",
+ " num_cls=len(train_dataloader.dataset.dataset_params[\"class_inclusion_list\"]),\n",
+ " normalize_targets=True,\n",
+ " post_prediction_callback=PPYoloEPostPredictionCallback(\n",
+ " score_threshold=0.01,\n",
+ " nms_top_k=1000,\n",
+ " max_predictions=300,\n",
+ " nms_threshold=0.7\n",
+ " )\n",
+ " )\n",
+ " ],\n",
+ " \"metric_to_watch\": 'mAP@0.50'\n",
+ "}"
+ ],
+ "metadata": {
+ "id": "436oI8rN8pJY"
+ },
+ "execution_count": 9,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.train(model=model, \n",
+ " training_params=train_params, \n",
+ " train_loader=train_dataloader, \n",
+ " valid_loader=val_dataloader)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "aae338390efa4c07ac4a49f45e2f684b",
+ "90426ad247124f448d08c02e6ec41c18"
+ ]
+ },
+ "id": "_U0N0yLX_Lux",
+ "outputId": "fcc4e44a-e23d-47fe-e02e-df136e5d77ad"
+ },
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " \u001b[1m❗❗❗ AUTHORIZATION REQUIRED ❗❗❗\u001b[0m \n"
+ ],
+ "text/html": [
+ " ❗❗❗ AUTHORIZATION REQUIRED ❗❗❗ \n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:36:51] INFO - sg_trainer.py - Using EMA with params {'decay': 0.9, 'decay_type': 'threshold'}\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Output()"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "aae338390efa4c07ac4a49f45e2f684b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The console stream is now moved to ./train_models/pascal_voc_detectio/console_May07_17_36_51.txt\n",
+ "\n",
+ "\n",
+ "Open the following link in your browser to authorize the client:\n",
+ "https://dagshub.com/login/oauth/authorize?state=859fe37e-60bb-4644-9dec-373a75b24e02&client_id=32b60ba385aa7cecf24046d8195a71c07dd345d9657977863b52e7748e0f0f28&middleman_request_id=ce7251da94b3822e23fa9da2301650b11316783bc6a0113e2593a2d34b11f362\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [],
+ "text/html": [
+ "\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Repository initialized!\n"
+ ],
+ "text/html": [
+ "Repository initialized!\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:36:59] INFO - helpers.py - Repository initialized!\n",
+ "2023/05/07 17:36:59 INFO mlflow.tracking.fluent: Experiment with name 'pascal_voc_detectio' does not exist. Creating a new experiment.\n",
+ "[2023-05-07 17:37:22] INFO - sg_trainer_utils.py - TRAINING PARAMETERS:\n",
+ " - Mode: Single GPU\n",
+ " - Number of GPUs: 1 (1 available on the machine)\n",
+ " - Dataset size: 7309 (len(train_set))\n",
+ " - Batch size per GPU: 16 (batch_size)\n",
+ " - Batch Accumulate: 1 (batch_accumulate)\n",
+ " - Total batch size: 16 (num_gpus * batch_size)\n",
+ " - Effective Batch size: 16 (num_gpus * batch_size * batch_accumulate)\n",
+ " - Iterations per epoch: 456 (len(train_loader))\n",
+ " - Gradient updates per epoch: 456 (len(train_loader) / batch_accumulate)\n",
+ "\n",
+ "[2023-05-07 17:37:22] INFO - sg_trainer.py - Started training for 3 epochs (0/2)\n",
+ "\n",
+ "Train epoch 0: 100%|██████████| 456/456 [02:52<00:00, 2.64it/s, PPYoloELoss/loss=2.26, PPYoloELoss/loss_cls=1.14, PPYoloELoss/loss_dfl=1.16, PPYoloELoss/loss_iou=0.217, gpu_mem=2.99]\n",
+ "Validation epoch 0: 100%|██████████| 40/40 [00:36<00:00, 1.11it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "===========================================================\n",
+ "SUMMARY OF EPOCH 0\n",
+ "├── Training\n",
+ "│ ├── Ppyoloeloss/loss = 2.2612\n",
+ "│ ├── Ppyoloeloss/loss_cls = 1.1375\n",
+ "│ ├── Ppyoloeloss/loss_dfl = 1.1602\n",
+ "│ └── Ppyoloeloss/loss_iou = 0.2175\n",
+ "└── Validation\n",
+ " ├── F1@0.50 = 0.0993\n",
+ " ├── Map@0.50 = 0.5846\n",
+ " ├── Ppyoloeloss/loss = 1.9335\n",
+ " ├── Ppyoloeloss/loss_cls = 0.9522\n",
+ " ├── Ppyoloeloss/loss_dfl = 1.0076\n",
+ " ├── Ppyoloeloss/loss_iou = 0.191\n",
+ " ├── Precision@0.50 = 0.0527\n",
+ " └── Recall@0.50 = 0.8553\n",
+ "\n",
+ "===========================================================\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:43:36] INFO - sg_trainer.py - Best checkpoint overriden: validation mAP@0.50: 0.584586501121521\n",
+ "Train epoch 1: 100%|██████████| 456/456 [02:43<00:00, 2.79it/s, PPYoloELoss/loss=2.01, PPYoloELoss/loss_cls=0.999, PPYoloELoss/loss_dfl=1.05, PPYoloELoss/loss_iou=0.195, gpu_mem=4.72]\n",
+ "Validation epoch 1: 100%|██████████| 40/40 [00:36<00:00, 1.11it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "===========================================================\n",
+ "SUMMARY OF EPOCH 1\n",
+ "├── Training\n",
+ "│ ├── Ppyoloeloss/loss = 2.0122\n",
+ "│ │ ├── Best until now = 2.2612 (\u001b[32m↘ -0.2491\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 2.2612 (\u001b[32m↘ -0.2491\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_cls = 0.9985\n",
+ "│ │ ├── Best until now = 1.1375 (\u001b[32m↘ -0.1389\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 1.1375 (\u001b[32m↘ -0.1389\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_dfl = 1.0522\n",
+ "│ │ ├── Best until now = 1.1602 (\u001b[32m↘ -0.108\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 1.1602 (\u001b[32m↘ -0.108\u001b[0m)\n",
+ "│ └── Ppyoloeloss/loss_iou = 0.195\n",
+ "│ ├── Best until now = 0.2175 (\u001b[32m↘ -0.0225\u001b[0m)\n",
+ "│ └── Epoch N-1 = 0.2175 (\u001b[32m↘ -0.0225\u001b[0m)\n",
+ "└── Validation\n",
+ " ├── F1@0.50 = 0.1147\n",
+ " │ ├── Best until now = 0.0993 (\u001b[32m↗ 0.0155\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.0993 (\u001b[32m↗ 0.0155\u001b[0m)\n",
+ " ├── Map@0.50 = 0.607\n",
+ " │ ├── Best until now = 0.5846 (\u001b[32m↗ 0.0224\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.5846 (\u001b[32m↗ 0.0224\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss = 1.8934\n",
+ " │ ├── Best until now = 1.9335 (\u001b[32m↘ -0.0402\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.9335 (\u001b[32m↘ -0.0402\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_cls = 0.9099\n",
+ " │ ├── Best until now = 0.9522 (\u001b[32m↘ -0.0423\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.9522 (\u001b[32m↘ -0.0423\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_dfl = 1.0222\n",
+ " │ ├── Best until now = 1.0076 (\u001b[31m↗ 0.0147\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.0076 (\u001b[31m↗ 0.0147\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_iou = 0.189\n",
+ " │ ├── Best until now = 0.191 (\u001b[32m↘ -0.0021\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.191 (\u001b[32m↘ -0.0021\u001b[0m)\n",
+ " ├── Precision@0.50 = 0.0615\n",
+ " │ ├── Best until now = 0.0527 (\u001b[32m↗ 0.0087\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.0527 (\u001b[32m↗ 0.0087\u001b[0m)\n",
+ " └── Recall@0.50 = 0.8657\n",
+ " ├── Best until now = 0.8553 (\u001b[32m↗ 0.0105\u001b[0m)\n",
+ " └── Epoch N-1 = 0.8553 (\u001b[32m↗ 0.0105\u001b[0m)\n",
+ "\n",
+ "===========================================================\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:48:52] INFO - sg_trainer.py - Best checkpoint overriden: validation mAP@0.50: 0.6069886088371277\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "File already staged for upload on path \n",
+ "\u001b[32m\"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\"\u001b[0m. Overwriting\n"
+ ],
+ "text/html": [
+ "File already staged for upload on path \n",
+ "\"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\". Overwriting\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:50:04] INFO - helpers.py - File already staged for upload on path \"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\". Overwriting\n",
+ "Train epoch 2: 100%|██████████| 456/456 [02:46<00:00, 2.73it/s, PPYoloELoss/loss=1.94, PPYoloELoss/loss_cls=0.95, PPYoloELoss/loss_dfl=1.03, PPYoloELoss/loss_iou=0.19, gpu_mem=4.72]\n",
+ "Validation epoch 2: 100%|██████████| 40/40 [00:36<00:00, 1.10it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "===========================================================\n",
+ "SUMMARY OF EPOCH 2\n",
+ "├── Training\n",
+ "│ ├── Ppyoloeloss/loss = 1.9417\n",
+ "│ │ ├── Best until now = 2.0122 (\u001b[32m↘ -0.0704\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 2.0122 (\u001b[32m↘ -0.0704\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_cls = 0.9499\n",
+ "│ │ ├── Best until now = 0.9985 (\u001b[32m↘ -0.0486\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 0.9985 (\u001b[32m↘ -0.0486\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_dfl = 1.0322\n",
+ "│ │ ├── Best until now = 1.0522 (\u001b[32m↘ -0.02\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 1.0522 (\u001b[32m↘ -0.02\u001b[0m)\n",
+ "│ └── Ppyoloeloss/loss_iou = 0.1903\n",
+ "│ ├── Best until now = 0.195 (\u001b[32m↘ -0.0047\u001b[0m)\n",
+ "│ └── Epoch N-1 = 0.195 (\u001b[32m↘ -0.0047\u001b[0m)\n",
+ "└── Validation\n",
+ " ├── F1@0.50 = 0.1337\n",
+ " │ ├── Best until now = 0.1147 (\u001b[32m↗ 0.0189\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.1147 (\u001b[32m↗ 0.0189\u001b[0m)\n",
+ " ├── Map@0.50 = 0.6335\n",
+ " │ ├── Best until now = 0.607 (\u001b[32m↗ 0.0265\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.607 (\u001b[32m↗ 0.0265\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss = 1.8808\n",
+ " │ ├── Best until now = 1.8934 (\u001b[32m↘ -0.0125\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.8934 (\u001b[32m↘ -0.0125\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_cls = 0.9078\n",
+ " │ ├── Best until now = 0.9099 (\u001b[32m↘ -0.0021\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.9099 (\u001b[32m↘ -0.0021\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_dfl = 1.0149\n",
+ " │ ├── Best until now = 1.0076 (\u001b[31m↗ 0.0073\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.0222 (\u001b[32m↘ -0.0074\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_iou = 0.1862\n",
+ " │ ├── Best until now = 0.189 (\u001b[32m↘ -0.0027\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.189 (\u001b[32m↘ -0.0027\u001b[0m)\n",
+ " ├── Precision@0.50 = 0.0727\n",
+ " │ ├── Best until now = 0.0615 (\u001b[32m↗ 0.0112\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.0615 (\u001b[32m↗ 0.0112\u001b[0m)\n",
+ " └── Recall@0.50 = 0.8578\n",
+ " ├── Best until now = 0.8657 (\u001b[31m↘ -0.0079\u001b[0m)\n",
+ " └── Epoch N-1 = 0.8657 (\u001b[31m↘ -0.0079\u001b[0m)\n",
+ "\n",
+ "===========================================================\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "File already staged for upload on path \u001b[32m\"models/ckpt_latest.pth\"\u001b[0m. Overwriting\n"
+ ],
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+ "File already staged for upload on path \"models/ckpt_latest.pth\". Overwriting\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:54:13] INFO - helpers.py - File already staged for upload on path \"models/ckpt_latest.pth\". Overwriting\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:56:22] INFO - helpers.py - File already staged for upload on path \"models/ckpt_best.pth\". Overwriting\n",
+ "[2023-05-07 17:56:22] INFO - sg_trainer.py - Best checkpoint overriden: validation mAP@0.50: 0.6334812641143799\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "File already staged for upload on path \u001b[32m\"models/average_model.pth\"\u001b[0m. Overwriting\n"
+ ],
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+ "File already staged for upload on path \"models/average_model.pth\". Overwriting\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "File already staged for upload on path \n",
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+ ],
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+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:57:03] INFO - helpers.py - File already staged for upload on path \"models/average_model.pth\". Overwriting\n",
+ "[2023-05-07 17:57:03] INFO - helpers.py - File already staged for upload on path \"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\". Overwriting\n",
+ "[2023-05-07 17:57:03] INFO - sg_trainer.py - RUNNING ADDITIONAL TEST ON THE AVERAGED MODEL...\n",
+ "Validation epoch 3: 100%|██████████| 40/40 [00:37<00:00, 1.07it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "===========================================================\n",
+ "SUMMARY OF EPOCH 3\n",
+ "├── Training\n",
+ "│ ├── Ppyoloeloss/loss = 1.9417\n",
+ "│ │ ├── Best until now = 2.0122 (\u001b[32m↘ -0.0704\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 2.0122 (\u001b[32m↘ -0.0704\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_cls = 0.9499\n",
+ "│ │ ├── Best until now = 0.9985 (\u001b[32m↘ -0.0486\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 0.9985 (\u001b[32m↘ -0.0486\u001b[0m)\n",
+ "│ ├── Ppyoloeloss/loss_dfl = 1.0322\n",
+ "│ │ ├── Best until now = 1.0522 (\u001b[32m↘ -0.02\u001b[0m)\n",
+ "│ │ └── Epoch N-1 = 1.0522 (\u001b[32m↘ -0.02\u001b[0m)\n",
+ "│ └── Ppyoloeloss/loss_iou = 0.1903\n",
+ "│ ├── Best until now = 0.195 (\u001b[32m↘ -0.0047\u001b[0m)\n",
+ "│ └── Epoch N-1 = 0.195 (\u001b[32m↘ -0.0047\u001b[0m)\n",
+ "└── Validation\n",
+ " ├── F1@0.50 = 0.2458\n",
+ " │ ├── Best until now = 0.1337 (\u001b[32m↗ 0.1121\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.1337 (\u001b[32m↗ 0.1121\u001b[0m)\n",
+ " ├── Map@0.50 = 0.7248\n",
+ " │ ├── Best until now = 0.6335 (\u001b[32m↗ 0.0913\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.6335 (\u001b[32m↗ 0.0913\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss = 1.8097\n",
+ " │ ├── Best until now = 1.8808 (\u001b[32m↘ -0.0711\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.8808 (\u001b[32m↘ -0.0711\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_cls = 0.9522\n",
+ " │ ├── Best until now = 0.9078 (\u001b[31m↗ 0.0444\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.9078 (\u001b[31m↗ 0.0444\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_dfl = 0.9125\n",
+ " │ ├── Best until now = 1.0076 (\u001b[32m↘ -0.0951\u001b[0m)\n",
+ " │ └── Epoch N-1 = 1.0149 (\u001b[32m↘ -0.1024\u001b[0m)\n",
+ " ├── Ppyoloeloss/loss_iou = 0.1605\n",
+ " │ ├── Best until now = 0.1862 (\u001b[32m↘ -0.0257\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.1862 (\u001b[32m↘ -0.0257\u001b[0m)\n",
+ " ├── Precision@0.50 = 0.1432\n",
+ " │ ├── Best until now = 0.0727 (\u001b[32m↗ 0.0705\u001b[0m)\n",
+ " │ └── Epoch N-1 = 0.0727 (\u001b[32m↗ 0.0705\u001b[0m)\n",
+ " └── Recall@0.50 = 0.8724\n",
+ " ├── Best until now = 0.8657 (\u001b[32m↗ 0.0066\u001b[0m)\n",
+ " └── Epoch N-1 = 0.8578 (\u001b[32m↗ 0.0145\u001b[0m)\n",
+ "\n",
+ "===========================================================\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "File already staged for upload on path \n",
+ "\u001b[32m\"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\"\u001b[0m. Overwriting\n"
+ ],
+ "text/html": [
+ "File already staged for upload on path \n",
+ "\"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\". Overwriting\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:57:42] INFO - helpers.py - File already staged for upload on path \"artifacts/./train_models/pascal_voc_detectio/experiment_logs_May07_17_36_51.txt\". Overwriting\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Uploading files \u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m to \u001b[32m\"nirbarazida/yolonax_cifar\"\u001b[0m\u001b[33m...\u001b[0m\n"
+ ],
+ "text/html": [
+ "Uploading files (4) to \"nirbarazida/yolonax_cifar\"...\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:57:43] INFO - helpers.py - Uploading files (4) to \"nirbarazida/yolonax_cifar\"...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Upload finished successfully!\n"
+ ],
+ "text/html": [
+ "Upload finished successfully!\n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[2023-05-07 17:58:59] INFO - helpers.py - Upload finished successfully!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 🏁 Finish line\n",
+ "\n",
+ "Congratulations! You've reached the finish line! \n",
+ "\n",
+ "You successfully logged your experiment with DagsHub. You can see the metrics and parameters under the experiment tab, the artifacts logged by MLflow unser `http://dagshub.com//.mlflow` and the DVC versioned files in the file view of the repo.\n",
+ "\n",
+ "If you have any questions, feel free to reach out on our community Discord server 👉\n",
+ "![](\"https://img.shields.io/discord/698874030052212737?logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAB4AAAAXCAYAAAAcP%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%2BIDxyZGY6U2VxPiA8cmRmOmxpIHN0RXZ0OmFjdGlvbj0ic2F2ZWQiIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6YjEwYTE0YzgtYjc4OS00Njk4LWJlYTItZGUyODQ4N2ZhMjIyIiBzdEV2dDp3aGVuPSIyMDIxLTEwLTIzVDE2OjM0OjEyKzAzOjAwIiBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgMjIuMSAoTWFjaW50b3NoKSIgc3RFdnQ6Y2hhbmdlZD0iLyIvPiA8L3JkZjpTZXE%2BIDwveG1wTU06SGlzdG9yeT4gPC9yZGY6RGVzY3JpcHRpb24%2BIDwvcmRmOlJERj4gPC94OnhtcG1ldGE%2BIDw%2FeHBhY2tldCBlbmQ9InIiPz4jeahYAAACnElEQVRIx62WPWxOURjH7%2Fu%2BrTdN1RsGIkgw2BBLQwxCmvgamzAxCAs2xCYGSZWBWYihSUcDFhJpJJ1M0kUqJS1BlRCk%2Bno%2F7s%2FyPPV38lwtcZKTc%2B89z33%2B%2F%2BfznAzIkllO3lcD%2FcAloBbI%2B9wMDAB7ArlSKl8EWgWOAA%2BAWX6Ni8AaoA84AOwDdhm5%2ByL3ARgG9haBK2jF1rXAJL%2BPps2ikdvaANrJ3sMIXIE7bL1jP3w3sDxRjilv2WwX7DeAur2fM92dKbCD7hbmRZbli%2Fjmo2XrF2ClhtPN99g%2BWgD4X8YPWwfUao3tDnFT%2Fh%2BBPRQfJdtL6vebCcM0uRoLEGrbv61gzz14wq32GPcA7xOGHr92Qdz%2B9K0VEMdCmQFlB96fCKRZOgIMAd8Cxf48CdwCxgID%2FHnWynU%2Bq68GSeUKz0rJbQTeiTInOqqlYiTVkFz09SvwSCLo65go67b1tJB0r2yXkGXAKusDaq0bddmBu4BXiZUuNCx1Xklq3cl9NSCtkArwrEDnPQdeF7BzpU%2Bk6N3i44HFWxOLVxihSOdTB95SkFDO9Ki4uwZMyL4ru5scNteDZHV9U0A1s%2FhErU%2Bfh%2BxYnAqyui3euWAnGkHNu9wMUNOOFfXcZtA%2BF1PH9aD%2BtYMtz4BeAckDQeS0qssR2RJ3N4E5yRUKTiyAaaAnAzbI5lzSrW6biz%2F%2FRW9%2BCZwBHgtg23QDPNc67pOSQs7RF8ApYBtwGLhhzWICeAu8AcatFV6zW0mvkf1kRtRF7yiwyYH9SOwCrhTcNA4mWdtppbNUatfnYBD3GeCkXrGy9GYArLdymE5KpWqNpBRc9ErAElu7gdf27zhw3gi6XEd69SknBJYBx4yIesYvDj5LQsat3wkcCrw0T%2FonycbE%2FgQEhDUAAAAASUVORK5CYII%3D\")
"
+ ],
+ "metadata": {
+ "id": "31Ttjjgsvaii"
+ }
+ }
+ ]
+}
\ No newline at end of file
![\"DagsHubXSuperGradients\"](\"https://i.ibb.co/QnyBq3p/Screen-Shot-2023-04-30-at-18-23-41.png\")
![](\"https://img.shields.io/youtube/channel/subscribers/UCeuZrCdpIY69XNWqn9OeSYQ?style=social\")
| ![](\"https://img.shields.io/twitter/follow/TheRealDAGsHub.svg?style=social\")
"
+ ],
+ "metadata": {
+ "id": "Rqzr5m7PFa7t"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 🧘♀️ Before we start\n",
+ " \n",
+ "![](\"https://img.shields.io/badge/DagsHub-Sign%20Up-%231F4C55?logo=data:image/png;base64,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\"){kind=icon}
\n",
+ "\n",
+ "