Rename `test.py` to `val.py` (#4000)

.github/ISSUE_TEMPLATE/bug-report.md

  - **Common dataset**: coco.yaml or coco128.yaml
  - **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments
  
-If this is a custom dataset/training question you **must include** your `train*.jpg`, `test*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
+If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
 
 
 ## 🐛 Bug

.github/workflows/ci-testing.yml

           # detect
           python detect.py --weights ${{ matrix.model }}.pt --device $di
           python detect.py --weights runs/train/exp/weights/last.pt --device $di
-          # test
-          python test.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di
-          python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
+          # val
+          python val.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di
+          python val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
 
           python hubconf.py  # hub
           python models/yolo.py --cfg ${{ matrix.model }}.yaml  # inspect

.github/workflows/greetings.yml

 
             ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
 
-            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
             

README.md

   
   * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. 
   * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
-  * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+  * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 </details>
 
 
   <summary>Table Notes (click to expand)</summary>
   
   * AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.  
-  * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
-  * Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
+  * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
+  * Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
   * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). 
-  * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+  * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 </details>
 
 

models/yolo.py

     # tb_writer = SummaryWriter('.')
     # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
     # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph
-    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard

train.py

 FILE = Path(__file__).absolute()
 sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
 
-import test  # for end-of-epoch mAP
+import val  # for end-of-epoch mAP
 from models.experimental import attempt_load
 from models.yolo import Model
 from utils.autoanchor import check_anchors
           opt,
           device,
           ):
-    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \
+    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, = \
         opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
-        opt.resume, opt.notest, opt.nosave, opt.workers
+        opt.resume, opt.noval, opt.nosave, opt.workers
 
     # Directories
     save_dir = Path(save_dir)
     with torch_distributed_zero_first(RANK):
         check_dataset(data_dict)  # check
     train_path = data_dict['train']
-    test_path = data_dict['val']
+    val_path = data_dict['val']
 
     # Freeze
     freeze = []  # parameter names to freeze (full or partial)
     # Image sizes
     gs = max(int(model.stride.max()), 32)  # grid size (max stride)
     nl = model.model[-1].nl  # number of detection layers (used for scaling hyp['obj'])
-    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples
+    imgsz, imgsz_val = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples
 
     # DP mode
     if cuda and RANK == -1 and torch.cuda.device_count() > 1:
 
     # Process 0
     if RANK in [-1, 0]:
-        testloader = create_dataloader(test_path, imgsz_test, batch_size // WORLD_SIZE * 2, gs, single_cls,
-                                       hyp=hyp, cache=opt.cache_images and not notest, rect=True, rank=-1,
+        valloader = create_dataloader(val_path, imgsz_val, batch_size // WORLD_SIZE * 2, gs, single_cls,
+                                       hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
                                        workers=workers,
                                        pad=0.5, prefix=colorstr('val: '))[0]
 
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
     compute_loss = ComputeLoss(model)  # init loss class
-    logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
+    logger.info(f'Image sizes {imgsz} train, {imgsz_val} val\n'
                 f'Using {dataloader.num_workers} dataloader workers\n'
                 f'Logging results to {save_dir}\n'
                 f'Starting training for {epochs} epochs...')
             # mAP
             ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
             final_epoch = epoch + 1 == epochs
-            if not notest or final_epoch:  # Calculate mAP
+            if not noval or final_epoch:  # Calculate mAP
                 wandb_logger.current_epoch = epoch + 1
-                results, maps, _ = test.run(data_dict,
-                                            batch_size=batch_size // WORLD_SIZE * 2,
-                                            imgsz=imgsz_test,
-                                            model=ema.ema,
-                                            single_cls=single_cls,
-                                            dataloader=testloader,
-                                            save_dir=save_dir,
-                                            save_json=is_coco and final_epoch,
-                                            verbose=nc < 50 and final_epoch,
-                                            plots=plots and final_epoch,
-                                            wandb_logger=wandb_logger,
-                                            compute_loss=compute_loss)
+                results, maps, _ = val.run(data_dict,
+                                           batch_size=batch_size // WORLD_SIZE * 2,
+                                           imgsz=imgsz_val,
+                                           model=ema.ema,
+                                           single_cls=single_cls,
+                                           dataloader=valloader,
+                                           save_dir=save_dir,
+                                           save_json=is_coco and final_epoch,
+                                           verbose=nc < 50 and final_epoch,
+                                           plots=plots and final_epoch,
+                                           wandb_logger=wandb_logger,
+                                           compute_loss=compute_loss)
 
             # Write
             with open(results_file, 'a') as f:
         if not evolve:
             if is_coco:  # COCO dataset
                 for m in [last, best] if best.exists() else [last]:  # speed, mAP tests
-                    results, _, _ = test.run(data_dict,
-                                             batch_size=batch_size // WORLD_SIZE * 2,
-                                             imgsz=imgsz_test,
-                                             model=attempt_load(m, device).half(),
-                                             single_cls=single_cls,
-                                             dataloader=testloader,
-                                             save_dir=save_dir,
-                                             save_json=True,
-                                             plots=False)
+                    results, _, _ = val.run(data_dict,
+                                            batch_size=batch_size // WORLD_SIZE * 2,
+                                            imgsz=imgsz_val,
+                                            model=attempt_load(m, device).half(),
+                                            single_cls=single_cls,
+                                            dataloader=valloader,
+                                            save_dir=save_dir,
+                                            save_json=True,
+                                            plots=False)
 
             # Strip optimizers
             for f in last, best:
     parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path')
     parser.add_argument('--epochs', type=int, default=300)
     parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, val] image sizes')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
     parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
-    parser.add_argument('--notest', action='store_true', help='only test final epoch')
+    parser.add_argument('--noval', action='store_true', help='only validate final epoch')
     parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
     parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
         # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
         opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check files
         assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-        opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
+        opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, val)
         opt.name = 'evolve' if opt.evolve else opt.name
         opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
 
             if 'anchors' not in hyp:  # anchors commented in hyp.yaml
                 hyp['anchors'] = 3
         assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve'
-        opt.notest, opt.nosave = True, True  # only test/save final epoch
+        opt.noval, opt.nosave = True, True  # only val/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
         yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml'  # save best result here
         if opt.bucket:

tutorial.ipynb

         "id": "0eq1SMWl6Sfn"
       },
       "source": [
-        "# 2. Test\n",
-        "Test a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
+        "# 2. Validate\n",
+        "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
       ]
     },
     {
       },
       "source": [
         "# Run YOLOv5x on COCO val2017\n",
-        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
+        "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
       ],
       "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, half=True, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
+            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, half=True, img_size=640, iou_thres=0.65, name='exp', project='runs/val', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
             "YOLOv5 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
             "                 all       5000      36335      0.746      0.626       0.68       0.49\n",
             "Speed: 5.3/1.5/6.8 ms inference/NMS/total per 640x640 image at batch-size 32\n",
             "\n",
-            "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
+            "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
             "loading annotations into memory...\n",
             "Done (t=0.44s)\n",
             "creating index...\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n",
-            "Results saved to runs/test/exp\n"
+            "Results saved to runs/val/exp\n"
           ],
           "name": "stdout"
         }
       },
       "source": [
         "# Run YOLOv5s on COCO test-dev2017 using --task test\n",
-        "!python test.py --weights yolov5s.pt --data coco.yaml --task test"
+        "!python val.py --weights yolov5s.pt --data coco.yaml --task test"
       ],
       "execution_count": null,
       "outputs": []
             "Plotting labels... \n",
             "\n",
             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
-            "Image sizes 640 train, 640 test\n",
+            "Image sizes 640 train, 640 val\n",
             "Using 2 dataloader workers\n",
             "Logging results to runs/train/exp\n",
             "Starting training for 3 epochs...\n",
       "source": [
         "## Local Logging\n",
         "\n",
-        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
+        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and val jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
       ]
     },
     {
       },
       "source": [
         "Image(filename='runs/train/exp/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
-        "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # test batch 0 labels\n",
-        "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # test batch 0 predictions"
+        "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # val batch 0 labels\n",
+        "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # val batch 0 predictions"
       ],
       "execution_count": null,
       "outputs": []
         "`train_batch0.jpg` shows train batch 0 mosaics and labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931217-4826f080-e002-11eb-87b9-ae0925a8c94b.jpg\" width=\"700\">  \n",
-        "`test_batch0_labels.jpg` shows test batch 0 labels\n",
+        "`test_batch0_labels.jpg` shows val batch 0 labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931209-46f5c380-e002-11eb-9bd5-7a3de2be9851.jpg\" width=\"700\">  \n",
-        "`test_batch0_pred.jpg` shows test batch 0 _predictions_"
+        "`test_batch0_pred.jpg` shows val batch 0 _predictions_"
       ]
     },
     {
         "\n",
         "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n",
         "\n",
-        "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+        "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
       ]
     },
     {
       "source": [
         "# Reproduce\n",
         "for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n",
-        "  !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45  # speed\n",
-        "  !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"
+        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45  # speed\n",
+        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"
       ],
       "execution_count": null,
       "outputs": []
         "  for d in 0 cpu; do  # devices\n",
         "    python detect.py --weights $m.pt --device $d  # detect official\n",
         "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
-        "    python test.py --weights $m.pt --device $d # test official\n",
-        "    python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n",
+        "    python val.py --weights $m.pt --device $d # val official\n",
+        "    python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",
         "  done\n",
         "  python hubconf.py  # hub\n",
         "  python models/yolo.py --cfg $m.yaml  # inspect\n",

utils/augmentations.py

 
     # Scale ratio (new / old)
     r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+    if not scaleup:  # only scale down, do not scale up (for better val mAP)
         r = min(r, 1.0)
 
     # Compute padding

utils/general.py

             for j, a in enumerate(d):  # per item
                 cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
                 im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('test%i.jpg' % j, cutout)
+                # cv2.imwrite('example%i.jpg' % j, cutout)
 
                 im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                 im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32

utils/plots.py

     plt.close()
 
 
-def plot_test_txt():  # from utils.plots import *; plot_test()
-    # Plot test.txt histograms
-    x = np.loadtxt('test.txt', dtype=np.float32)
+def plot_val_txt():  # from utils.plots import *; plot_val()
+    # Plot val.txt histograms
+    x = np.loadtxt('val.txt', dtype=np.float32)
     box = xyxy2xywh(x[:, :4])
     cx, cy = box[:, 0], box[:, 1]
 
 
 
 def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()
-    # Plot study.txt generated by test.py
+    # Plot study.txt generated by val.py
     plot2 = False  # plot additional results
     if plot2:
         ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()

test.py → val.py

-"""Test a trained YOLOv5 model accuracy on a custom dataset
+"""Validate a trained YOLOv5 model accuracy on a custom dataset
 
 Usage:
-    $ python path/to/test.py --data coco128.yaml --weights yolov5s.pt --img 640
+    $ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
 """
 
 import argparse
         save_hybrid=False,  # save label+prediction hybrid results to *.txt
         save_conf=False,  # save confidences in --save-txt labels
         save_json=False,  # save a cocoapi-compatible JSON results file
-        project='runs/test',  # save to project/name
+        project='runs/val',  # save to project/name
         name='exp',  # save to project/name
         exist_ok=False,  # existing project/name ok, do not increment
         half=True,  # use FP16 half-precision inference
 
         # Plot images
         if plots and batch_i < 3:
-            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
+            f = save_dir / f'val_batch{batch_i}_labels.jpg'  # labels
             Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
-            f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
+            f = save_dir / f'val_batch{batch_i}_pred.jpg'  # predictions
             Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
 
     # Compute statistics
     if plots:
         confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
         if wandb_logger and wandb_logger.wandb:
-            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
+            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('val*.jpg'))]
             wandb_logger.log({"Validation": val_batches})
     if wandb_images:
         wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
 
 
 def parse_opt():
-    parser = argparse.ArgumentParser(prog='test.py')
+    parser = argparse.ArgumentParser(prog='val.py')
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
     parser.add_argument('--batch-size', type=int, default=32, help='batch size')
     parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
     parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
-    parser.add_argument('--project', default='runs/test', help='save to project/name')
+    parser.add_argument('--project', default='runs/val', help='save to project/name')
     parser.add_argument('--name', default='exp', help='save to project/name')
     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
 
 def main(opt):
     set_logging()
-    print(colorstr('test: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+    print(colorstr('val: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
     check_requirements(exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
                 save_json=False, plots=False)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
-        # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
+        # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
         x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
         for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
             f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to