`ultralytics 8.0.73` minor fixes (#1929)

Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: joseliraGB <122470533+joseliraGB@users.noreply.github.com>

.github/workflows/ci.yaml

@@ -76,9 +76,9 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel
           if [ "${{ matrix.os }}" == "macos-latest" ]; then
-              pip install -e . coremltools openvino-dev tensorflow-macos --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . coremltools openvino-dev tensorflow-macos tensorflowjs --extra-index-url https://download.pytorch.org/whl/cpu
             else
-              pip install -e . coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . coremltools openvino-dev tensorflow-cpu tensorflowjs --extra-index-url https://download.pytorch.org/whl/cpu
           fi
           yolo export format=tflite
       - name: Check environment

examples/hub.ipynb

@@ -0,0 +1,103 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Ultralytics HUB",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FIzICjaph_Wy"
+      },
+      "source": [
+        "<a align=\"center\" href=\"https://hub.ultralytics.com\" target=\"_blank\">\n",
+        "<img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"></a>\n",
+        "\n",
+        "<div align=\"center\">\n",
+        "  <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n",
+        "    <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
+        "  <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb\">\n",
+        "    <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "\n",
+        "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook! \n",
+        "\n",
+        "This notebook allows you to train [YOLOv5](https://github.com/ultralytics/yolov5) and [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/hub/issues/new/choose\">GitHub</a> for support, and join our <a href=\"https://discord.gg/n6cFeSPZdD\">Discord</a> community for questions and discussions!\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eRQ2ow94MiOv"
+      },
+      "source": [
+        "# Setup\n",
+        "\n",
+        "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) and check software and hardware."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FyDnXd-n4c7Y",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "22dcbc27-9c6f-44fb-9745-620431f93793"
+      },
+      "source": [
+        "%pip install ultralytics  # install\n",
+        "from ultralytics import YOLO, checks, hub\n",
+        "checks()  # checks"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Ultralytics YOLOv8.0.64 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
+            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.3/166.8 GB disk)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cQ9BwaAqxAm4"
+      },
+      "source": [
+        "# Start\n",
+        "\n",
+        "Login with your [API key](https://hub.ultralytics.com/settings?tab=api+keys), select your YOLO 🚀 model and start training!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XSlZaJ9Iw_iZ"
+      },
+      "source": [
+        "hub.login('API_KEY')  # use your API key\n",
+        "\n",
+        "model = YOLO('https://hub.ultralytics.com/MODEL_ID')  # use your model URL\n",
+        "model.train()  # train model"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.72'
+__version__ = '8.0.73'
 
 from ultralytics.hub import start
 from ultralytics.yolo.engine.model import YOLO

ultralytics/hub/session.py

@@ -112,10 +112,8 @@ class HUBTrainingSession:
                 raise ValueError('Dataset may still be processing. Please wait a minute and try again.')  # RF fix
             self.model_id = data['id']
 
-            # TODO: restore when server keys when dataset URL and GPU train is working
-
             self.train_args = {
-                'batch': data['batch_size'],
+                'batch': data['batch' if ('batch' in data) else 'batch_size'],  # TODO: deprecate 'batch_size' in 3Q23
                 'epochs': data['epochs'],
                 'imgsz': data['imgsz'],
                 'patience': data['patience'],

ultralytics/nn/tasks.py

@@ -539,7 +539,7 @@ def guess_model_task(model):
         model (nn.Module) or (dict): PyTorch model or model configuration in YAML format.
 
     Returns:
-        str: Task of the model ('detect', 'segment', 'classify').
+        str: Task of the model ('detect', 'segment', 'classify', 'pose').
 
     Raises:
         SyntaxError: If the task of the model could not be determined.

ultralytics/yolo/data/base.py

@@ -180,10 +180,8 @@ class BaseDataset(Dataset):
         label = self.labels[index].copy()
         label.pop('shape', None)  # shape is for rect, remove it
         label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index)
-        label['ratio_pad'] = (
+        label['ratio_pad'] = (label['resized_shape'][0] / label['ori_shape'][0],
-            label['resized_shape'][0] / label['ori_shape'][0],
+                              label['resized_shape'][1] / label['ori_shape'][1])  # for evaluation
-            label['resized_shape'][1] / label['ori_shape'][1],
-        )  # for evaluation
         if self.rect:
             label['rect_shape'] = self.batch_shapes[self.batch[index]]
         label = self.update_labels_info(label)

ultralytics/yolo/data/utils.py

@@ -17,7 +17,8 @@ from PIL import ExifTags, Image, ImageOps
 from tqdm import tqdm
 
 from ultralytics.nn.autobackend import check_class_names
-from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, clean_url, colorstr, emojis, yaml_load
+from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis,
+                                    yaml_load)
 from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
 from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file
 from ultralytics.yolo.utils.ops import segments2boxes
@@ -246,6 +247,7 @@ def check_det_dataset(dataset, autodownload=True):
             if s and autodownload:
                 LOGGER.warning(m)
             else:
+                m += f"\nNote dataset download directory is '{DATASETS_DIR}'. You can update this in '{SETTINGS_YAML}'"
                 raise FileNotFoundError(m)
             t = time.time()
             if s.startswith('http') and s.endswith('.zip'):  # URL

ultralytics/yolo/engine/model.py

@@ -356,7 +356,6 @@ class YOLO:
             raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
         if overrides.get('resume'):
             overrides['resume'] = self.ckpt_path
-
         self.task = overrides.get('task') or self.task
         self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
         if not overrides.get('resume'):  # manually set model only if not resuming

ultralytics/yolo/engine/predictor.py

@@ -109,8 +109,35 @@ class BasePredictor:
     def preprocess(self, img):
         pass
 
-    def write_results(self, results, batch, print_string):
+    def write_results(self, idx, results, batch):
-        raise NotImplementedError('print_results function needs to be implemented')
+        p, im, _ = batch
+        log_string = ''
+        if len(im.shape) == 3:
+            im = im[None]  # expand for batch dim
+        self.seen += 1
+        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
+            log_string += f'{idx}: '
+            frame = self.dataset.count
+        else:
+            frame = getattr(self.dataset, 'frame', 0)
+        self.data_path = p
+        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
+        log_string += '%gx%g ' % im.shape[2:]  # print string
+        result = results[idx]
+        log_string += result.verbose()
+
+        if self.args.save or self.args.show:  # Add bbox to image
+            plot_args = dict(line_width=self.args.line_thickness, boxes=self.args.boxes)
+            if not self.args.retina_masks:
+                plot_args['im_gpu'] = im[idx]
+            self.plotted_img = result.plot(**plot_args)
+        # write
+        if self.args.save_txt:
+            result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
+        if self.args.save_crop:
+            result.save_crop(save_dir=self.save_dir / 'crops', file_name=self.data_path.stem)
+
+        return log_string
 
     def postprocess(self, preds, img, orig_img):
         return preds

ultralytics/yolo/engine/results.py

@@ -7,13 +7,14 @@ Usage: See https://docs.ultralytics.com/modes/predict/
 
 from copy import deepcopy
 from functools import lru_cache
+from pathlib import Path
 
 import numpy as np
 import torch
 
 from ultralytics.yolo.data.augment import LetterBox
 from ultralytics.yolo.utils import LOGGER, SimpleClass, deprecation_warn, ops
-from ultralytics.yolo.utils.plotting import Annotator, colors
+from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
 
 
 class BaseTensor(SimpleClass):
@@ -233,6 +234,80 @@ class Results(SimpleClass):
 
         return annotator.result()
 
+    def verbose(self):
+        """
+        Return log string for each tasks.
+        """
+        log_string = ''
+        probs = self.probs
+        boxes = self.boxes
+        if len(self) == 0:
+            return log_string if probs is not None else log_string + '(no detections), '
+        if probs is not None:
+            n5 = min(len(self.names), 5)
+            top5i = probs.argsort(0, descending=True)[:n5].tolist()  # top 5 indices
+            log_string += f"{', '.join(f'{self.names[j]} {probs[j]:.2f}' for j in top5i)}, "
+        if boxes:
+            for c in boxes.cls.unique():
+                n = (boxes.cls == c).sum()  # detections per class
+                log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "
+        return log_string
+
+    def save_txt(self, txt_file, save_conf=False):
+        """Save predictions into txt file.
+
+        Args:
+            txt_file (str): txt file path.
+            save_conf (bool): save confidence score or not.
+        """
+        boxes = self.boxes
+        masks = self.masks
+        probs = self.probs
+        kpts = self.keypoints
+        texts = []
+        if probs is not None:
+            # classify
+            n5 = min(len(self.names), 5)
+            top5i = probs.argsort(0, descending=True)[:n5].tolist()  # top 5 indices
+            [texts.append(f'{probs[j]:.2f} {self.names[j]}') for j in top5i]
+        elif boxes:
+            # detect/segment/pose
+            for j, d in enumerate(boxes):
+                c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
+                line = (c, *d.xywhn.view(-1))
+                if masks:
+                    seg = masks[j].xyn[0].copy().reshape(-1)  # reversed mask.xyn, (n,2) to (n*2)
+                    line = (c, *seg)
+                if kpts is not None:
+                    kpt = (kpts[j][:, :2] / d.orig_shape[[1, 0]]).reshape(-1).tolist()
+                    line += (*kpt, )
+                line += (conf, ) * save_conf + (() if id is None else (id, ))
+                texts.append(('%g ' * len(line)).rstrip() % line)
+
+        with open(txt_file, 'a') as f:
+            for text in texts:
+                f.write(text + '\n')
+
+    def save_crop(self, save_dir, file_name=Path('im.jpg')):
+        """Save cropped predictions to `save_dir/cls/file_name.jpg`.
+
+        Args:
+            save_dir (str | pathlib.Path): Save path.
+            file_name (str | pathlib.Path): File name.
+        """
+        if self.probs is not None:
+            LOGGER.warning('Warning: Classify task do not support `save_crop`.')
+            return
+        if isinstance(save_dir, str):
+            save_dir = Path(save_dir)
+        if isinstance(file_name, str):
+            file_name = Path(file_name)
+        for d in self.boxes:
+            save_one_box(d.xyxy,
+                         self.orig_img.copy(),
+                         file=save_dir / self.names[int(d.cls)] / f'{file_name.stem}.jpg',
+                         BGR=True)
+
 
 class Boxes(BaseTensor):
     """
@@ -339,6 +414,8 @@ class Masks(BaseTensor):
     """
 
     def __init__(self, masks, orig_shape) -> None:
+        if masks.ndim == 2:
+            masks = masks[None, :]
         self.masks = masks  # N, h, w
         self.orig_shape = orig_shape
 

ultralytics/yolo/engine/trainer.py

@@ -552,7 +552,7 @@ class BaseTrainer:
         if self.resume:
             assert start_epoch > 0, \
                 f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
-                f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
+                f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
             LOGGER.info(
                 f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
         if self.epochs < start_epoch:

ultralytics/yolo/utils/__init__.py

@@ -490,6 +490,7 @@ def get_user_config_dir(sub_dir='Ultralytics'):
 
 
 USER_CONFIG_DIR = Path(os.getenv('YOLO_CONFIG_DIR', get_user_config_dir()))  # Ultralytics settings dir
+SETTINGS_YAML = USER_CONFIG_DIR / 'settings.yaml'
 
 
 def emojis(string=''):
@@ -591,7 +592,7 @@ def set_sentry():
             logging.getLogger(logger).setLevel(logging.CRITICAL)
 
 
-def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.3'):
+def get_settings(file=SETTINGS_YAML, version='0.0.3'):
     """
     Loads a global Ultralytics settings YAML file or creates one with default values if it does not exist.
 
@@ -640,7 +641,7 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.3'):
         return settings
 
 
-def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
+def set_settings(kwargs, file=SETTINGS_YAML):
     """
     Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
     directories.

ultralytics/yolo/v8/classify/predict.py

@@ -5,14 +5,10 @@ import torch
 from ultralytics.yolo.engine.predictor import BasePredictor
 from ultralytics.yolo.engine.results import Results
 from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
-from ultralytics.yolo.utils.plotting import Annotator
 
 
 class ClassificationPredictor(BasePredictor):
 
-    def get_annotator(self, img):
-        return Annotator(img, example=str(self.model.names), pil=True)
-
     def preprocess(self, img):
         img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
         return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
@@ -27,43 +23,6 @@ class ClassificationPredictor(BasePredictor):
 
         return results
 
-    def write_results(self, idx, results, batch):
-        p, im, im0 = batch
-        log_string = ''
-        if len(im.shape) == 3:
-            im = im[None]  # expand for batch dim
-        self.seen += 1
-        im0 = im0.copy()
-        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
-            log_string += f'{idx}: '
-            frame = self.dataset.count
-        else:
-            frame = getattr(self.dataset, 'frame', 0)
-
-        self.data_path = p
-        # save_path = str(self.save_dir / p.name)  # im.jpg
-        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
-        log_string += '%gx%g ' % im.shape[2:]  # print string
-
-        result = results[idx]
-        if len(result) == 0:
-            return log_string
-        prob = result.probs
-        # Print results
-        n5 = min(len(self.model.names), 5)
-        top5i = prob.argsort(0, descending=True)[:n5].tolist()  # top 5 indices
-        log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
-
-        # write
-        if self.args.save or self.args.show:  # Add bbox to image
-            self.plotted_img = result.plot()
-        if self.args.save_txt:  # Write to file
-            text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
-            with open(f'{self.txt_path}.txt', 'a') as f:
-                f.write(text + '\n')
-
-        return log_string
-
 
 def predict(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"

ultralytics/yolo/v8/detect/predict.py

@@ -5,7 +5,6 @@ import torch
 from ultralytics.yolo.engine.predictor import BasePredictor
 from ultralytics.yolo.engine.results import Results
 from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
-from ultralytics.yolo.utils.plotting import save_one_box
 
 
 class DetectionPredictor(BasePredictor):
@@ -34,48 +33,6 @@ class DetectionPredictor(BasePredictor):
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
 
-    def write_results(self, idx, results, batch):
-        p, im, im0 = batch
-        log_string = ''
-        if len(im.shape) == 3:
-            im = im[None]  # expand for batch dim
-        self.seen += 1
-        imc = im0.copy() if self.args.save_crop else im0
-        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
-            log_string += f'{idx}: '
-            frame = self.dataset.count
-        else:
-            frame = getattr(self.dataset, 'frame', 0)
-        self.data_path = p
-        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
-        log_string += '%gx%g ' % im.shape[2:]  # print string
-
-        result = results[idx]  # TODO: make boxes inherit from tensors
-        if len(result) == 0:
-            return f'{log_string}(no detections), '
-        det = result.boxes
-        for c in det.cls.unique():
-            n = (det.cls == c).sum()  # detections per class
-            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
-
-        if self.args.save or self.args.show:  # Add bbox to image
-            self.plotted_img = result.plot(line_width=self.args.line_thickness)
-
-        # write
-        for d in reversed(det):
-            c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
-            if self.args.save_txt:  # Write to file
-                line = (c, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
-                with open(f'{self.txt_path}.txt', 'a') as f:
-                    f.write(('%g ' * len(line)).rstrip() % line + '\n')
-            if self.args.save_crop:
-                save_one_box(d.xyxy,
-                             imc,
-                             file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
-                             BGR=True)
-
-        return log_string
-
 
 def predict(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or 'yolov8n.pt'

ultralytics/yolo/v8/pose/predict.py

@@ -2,7 +2,6 @@
 
 from ultralytics.yolo.engine.results import Results
 from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
-from ultralytics.yolo.utils.plotting import save_one_box
 from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
 
@@ -34,50 +33,6 @@ class PosePredictor(DetectionPredictor):
                         keypoints=pred_kpts))
         return results
 
-    def write_results(self, idx, results, batch):
-        p, im, im0 = batch
-        log_string = ''
-        if len(im.shape) == 3:
-            im = im[None]  # expand for batch dim
-        self.seen += 1
-        imc = im0.copy() if self.args.save_crop else im0
-        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
-            log_string += f'{idx}: '
-            frame = self.dataset.count
-        else:
-            frame = getattr(self.dataset, 'frame', 0)
-        self.data_path = p
-        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
-        log_string += '%gx%g ' % im.shape[2:]  # print string
-
-        result = results[idx]  # TODO: make boxes inherit from tensors
-        if len(result) == 0:
-            return f'{log_string}(no detections), '
-        det = result.boxes
-        for c in det.cls.unique():
-            n = (det.cls == c).sum()  # detections per class
-            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
-
-        if self.args.save or self.args.show:  # Add bbox to image
-            self.plotted_img = result.plot(line_width=self.args.line_thickness, boxes=self.args.boxes)
-
-        # write
-        for j, d in enumerate(reversed(det)):
-            c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
-            if self.args.save_txt:  # Write to file
-                kpt = (result[j].keypoints[:, :2] / d.orig_shape[[1, 0]]).reshape(-1).tolist()
-                box = d.xywhn.view(-1).tolist()
-                line = (c, *box, *kpt) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
-                with open(f'{self.txt_path}.txt', 'a') as f:
-                    f.write(('%g ' * len(line)).rstrip() % line + '\n')
-            if self.args.save_crop:
-                save_one_box(d.xyxy,
-                             imc,
-                             file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
-                             BGR=True)
-
-        return log_string
-
 
 def predict(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or 'yolov8n-pose.pt'

ultralytics/yolo/v8/segment/predict.py

@@ -4,7 +4,6 @@ import torch
 
 from ultralytics.yolo.engine.results import Results
 from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
-from ultralytics.yolo.utils.plotting import save_one_box
 from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
 
@@ -40,55 +39,6 @@ class SegmentationPredictor(DetectionPredictor):
                 Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results
 
-    def write_results(self, idx, results, batch):
-        p, im, im0 = batch
-        log_string = ''
-        if len(im.shape) == 3:
-            im = im[None]  # expand for batch dim
-        self.seen += 1
-        imc = im0.copy() if self.args.save_crop else im0
-        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
-            log_string += f'{idx}: '
-            frame = self.dataset.count
-        else:
-            frame = getattr(self.dataset, 'frame', 0)
-
-        self.data_path = p
-        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
-        log_string += '%gx%g ' % im.shape[2:]  # print string
-
-        result = results[idx]
-        if len(result) == 0:
-            return f'{log_string}(no detections), '
-        det, mask = result.boxes, result.masks  # getting tensors TODO: mask mask,box inherit for tensor
-
-        # Print results
-        for c in det.cls.unique():
-            n = (det.cls == c).sum()  # detections per class
-            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
-
-        # Mask plotting
-        if self.args.save or self.args.show:
-            im_gpu = torch.as_tensor(im0, dtype=torch.float16, device=mask.masks.device).permute(
-                2, 0, 1).flip(0).contiguous() / 255 if self.args.retina_masks else im[idx]
-            self.plotted_img = result.plot(line_width=self.args.line_thickness, im_gpu=im_gpu, boxes=self.args.boxes)
-
-        # Write results
-        for j, d in enumerate(reversed(det)):
-            c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
-            if self.args.save_txt:  # Write to file
-                seg = mask.xyn[len(det) - j - 1].copy().reshape(-1)  # reversed mask.xyn, (n,2) to (n*2)
-                line = (c, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
-                with open(f'{self.txt_path}.txt', 'a') as f:
-                    f.write(('%g ' * len(line)).rstrip() % line + '\n')
-            if self.args.save_crop:
-                save_one_box(d.xyxy,
-                             imc,
-                             file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
-                             BGR=True)
-
-        return log_string
-
 
 def predict(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or 'yolov8n-seg.pt'