Merge branch 'master' into dependabot/pip/jaxlib-lte-0.4.23

.github/workflows/links.yml

@@ -34,13 +34,14 @@ jobs:
           retry_wait_seconds: 60
           max_attempts: 3
           command: |
-            lychee 
-            --accept 403,429,500,502,999 
-            --exclude-loopback 
-            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' 
-            --exclude-path '**/ci.yaml' 
-            --github-token ${{ secrets.GITHUB_TOKEN }} 
-            './**/*.md' 
+            lychee \
+            --scheme 'https' \
+            --accept 403,429,500,502,999 \
+            --exclude-loopback \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path '**/ci.yaml' \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            './**/*.md' \
             './**/*.html'
 
       - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
@@ -51,15 +52,16 @@ jobs:
           retry_wait_seconds: 60
           max_attempts: 3
           command: |
-            lychee 
-            --accept 429,999 
-            --exclude-loopback 
-            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' 
-            --exclude-path '**/ci.yaml' 
-            --github-token ${{ secrets.GITHUB_TOKEN }} 
-            './**/*.md' 
-            './**/*.html' 
-            './**/*.yml' 
-            './**/*.yaml' 
-            './**/*.py' 
+            lychee \
+            --scheme 'https' \
+            --accept 429,999 \
+            --exclude-loopback \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path '**/ci.yaml' \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            './**/*.md' \
+            './**/*.html' \
+            './**/*.yml' \
+            './**/*.yaml' \
+            './**/*.py' \
             './**/*.ipynb'

README.md

@@ -1,7 +1,7 @@
 <div align="center">
   <p>
-    <a href="https://yolovision.ultralytics.com/" target="_blank">
-      <img width="100%" src="https://github.com/raw/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
+    <a href="http://www.ultralytics.com/blog/ultralytics-yolov8-turns-one-a-year-of-breakthroughs-and-innovations" target="_blank">
+      <img width="100%" src="https://github.com/raw/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
     <!--
     <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
       <img width="100%" src="https://github.com/raw/ultralytics/assets/main/yolov5/v70/splash.png"></a>

README.zh-CN.md

@@ -1,7 +1,7 @@
 <div align="center">
   <p>
-    <a href="https://yolovision.ultralytics.com/" target="_blank">
-      <img width="100%" src="https://github.com/raw/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
+    <a href="http://www.ultralytics.com/blog/ultralytics-yolov8-turns-one-a-year-of-breakthroughs-and-innovations" target="_blank">
+      <img width="100%" src="https://github.com/raw/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
     <!--
     <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
       <img width="100%" src="https://github.com/raw/ultralytics/assets/main/yolov5/v70/splash.png"></a>

export.py

@@ -226,28 +226,19 @@ def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:
     from openvino.tools import mo  # noqa
 
     LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...")
-    f = str(file).replace(file.suffix, f"_openvino_model{os.sep}")
+    f = str(file).replace(file.suffix, f"_{'int8_' if int8 else ''}openvino_model{os.sep}")
     f_onnx = file.with_suffix(".onnx")
     f_ov = str(Path(f) / file.with_suffix(".xml").name)
+
+    ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half)  # export
+
     if int8:
-        check_requirements("nncf>=2.4.0")  # requires at least version 2.4.0 to use the post-training quantization
+        check_requirements("nncf>=2.5.0")  # requires at least version 2.5.0 to use the post-training quantization
         import nncf
         import numpy as np
-        from openvino.runtime import Core
 
         from utils.dataloaders import create_dataloader
 
-        core = Core()
-        onnx_model = core.read_model(f_onnx)  # export
-
-        def prepare_input_tensor(image: np.ndarray):
-            input_tensor = image.astype(np.float32)  # uint8 to fp16/32
-            input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
-
-            if input_tensor.ndim == 3:
-                input_tensor = np.expand_dims(input_tensor, 0)
-            return input_tensor
-
         def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4):
             data_yaml = check_yaml(yaml_path)
             data = check_dataset(data_yaml)
@@ -268,15 +259,15 @@ def transform_fn(data_item):
             Returns:
                 input_tensor: Input data for quantization
             """
-            img = data_item[0].numpy()
-            input_tensor = prepare_input_tensor(img)
-            return input_tensor
+            assert data_item[0].dtype == torch.uint8, "input image must be uint8 for the quantization preprocessing"
+
+            img = data_item[0].numpy().astype(np.float32)  # uint8 to fp16/32
+            img /= 255.0  # 0 - 255 to 0.0 - 1.0
+            return np.expand_dims(img, 0) if img.ndim == 3 else img
 
         ds = gen_dataloader(data)
         quantization_dataset = nncf.Dataset(ds, transform_fn)
-        ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
-    else:
-        ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half)  # export
+        ov_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
 
     ov.serialize(ov_model, f_ov)  # save
     yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata)  # add metadata.yaml
@@ -555,7 +546,7 @@ def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")):
         "--quantize_uint8" if int8 else "",
         "--output_node_names=Identity,Identity_1,Identity_2,Identity_3",
         str(f_pb),
-        str(f),
+        f,
     ]
     subprocess.run([arg for arg in args if arg], check=True)
 

models/common.py

@@ -859,11 +859,17 @@ def pandas(self):
     def tolist(self):
         # return a list of Detections objects, i.e. 'for result in results.tolist():'
         r = range(self.n)  # iterable
-        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-        # for d in x:
-        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
+        return [
+            Detections(
+                [self.ims[i]],
+                [self.pred[i]],
+                [self.files[i]],
+                self.times,
+                self.names,
+                self.s,
+            )
+            for i in r
+        ]
 
     def print(self):
         LOGGER.info(self.__str__())

train.py

@@ -682,10 +682,7 @@ def main(opt, callbacks=Callbacks()):
             )
 
         # Delete the items in meta dictionary whose first value is False
-        del_ = []
-        for item in meta.keys():
-            if meta[item][0] is False:
-                del_.append(item)
+        del_ = [item for item, value_ in meta.items() if value_[0] is False]
         hyp_GA = hyp.copy()  # Make a copy of hyp dictionary
         for item in del_:
             del meta[item]  # Remove the item from meta dictionary
@@ -696,9 +693,7 @@ def main(opt, callbacks=Callbacks()):
         upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()])
 
         # Create gene_ranges list to hold the range of values for each gene in the population
-        gene_ranges = []
-        for i in range(len(upper_limit)):
-            gene_ranges.append((lower_limit[i], upper_limit[i]))
+        gene_ranges = [(lower_limit[i], upper_limit[i]) for i in range(len(upper_limit))]
 
         # Initialize the population with initial_values or random values
         initial_values = []
@@ -723,25 +718,20 @@ def main(opt, callbacks=Callbacks()):
 
         # Generate random values within the search space for the rest of the population
         if initial_values is None:
-            population = [generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size)]
-        else:
-            if pop_size > 1:
-                population = [
-                    generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size - len(initial_values))
-                ]
-                for initial_value in initial_values:
-                    population = [initial_value] + population
+            population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size)]
+        elif pop_size > 1:
+            population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size - len(initial_values))]
+            for initial_value in initial_values:
+                population = [initial_value] + population
 
         # Run the genetic algorithm for a fixed number of generations
         list_keys = list(hyp_GA.keys())
         for generation in range(opt.evolve):
             if generation >= 1:
                 save_dict = {}
                 for i in range(len(population)):
-                    little_dict = {}
-                    for j in range(len(population[i])):
-                        little_dict[list_keys[j]] = float(population[i][j])
-                    save_dict["gen" + str(generation) + "number" + str(i)] = little_dict
+                    little_dict = {list_keys[j]: float(population[i][j]) for j in range(len(population[i]))}
+                    save_dict[f"gen{str(generation)}number{str(i)}"] = little_dict
 
                 with open(save_dir / "evolve_population.yaml", "w") as outfile:
                     yaml.dump(save_dict, outfile, default_flow_style=False)
@@ -771,7 +761,7 @@ def main(opt, callbacks=Callbacks()):
 
             # Select the fittest individuals for reproduction using adaptive tournament selection
             selected_indices = []
-            for i in range(pop_size - elite_size):
+            for _ in range(pop_size - elite_size):
                 # Adaptive tournament size
                 tournament_size = max(
                     max(2, tournament_size_min),
@@ -788,7 +778,7 @@ def main(opt, callbacks=Callbacks()):
             selected_indices.extend(elite_indices)
             # Create the next generation through crossover and mutation
             next_generation = []
-            for i in range(pop_size):
+            for _ in range(pop_size):
                 parent1_index = selected_indices[random.randint(0, pop_size - 1)]
                 parent2_index = selected_indices[random.randint(0, pop_size - 1)]
                 # Adaptive crossover rate

utils/downloads.py

@@ -24,9 +24,7 @@ def is_url(url, check=True):
 def gsutil_getsize(url=""):
     # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
     output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8")
-    if output:
-        return int(output.split()[0])
-    return 0
+    return int(output.split()[0]) if output else 0
 
 
 def url_getsize(url="https://ultralytics.com/images/bus.jpg"):

utils/flask_rest_api/README.md

@@ -1,8 +1,6 @@
 # Flask REST API
 
-[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are
-commonly used to expose Machine Learning (ML)  models to other services. This folder contains an example REST API
-created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
+[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
 
 ## Requirements
 
@@ -69,5 +67,4 @@ The model inference results are returned as a JSON response:
 ]
 ```
 
-An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given
-in `example_request.py`
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`

utils/general.py

@@ -188,7 +188,7 @@ class Profile(contextlib.ContextDecorator):
     def __init__(self, t=0.0, device: torch.device = None):
         self.t = t
         self.device = device
-        self.cuda = True if (device and str(device)[:4] == "cuda") else False
+        self.cuda = bool(device and str(device).startswith("cuda"))
 
     def __enter__(self):
         self.start = self.time()

utils/loggers/__init__.py

@@ -67,9 +67,7 @@ def _json_default(value):
             value = value.item()
         except ValueError:  # "only one element tensors can be converted to Python scalars"
             pass
-    if isinstance(value, float):
-        return value
-    return str(value)
+    return value if isinstance(value, float) else str(value)
 
 
 class Loggers:
@@ -250,12 +248,12 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
                 f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
         if self.ndjson_console or self.ndjson_file:
             json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default)
-            if self.ndjson_console:
-                print(json_data)
-            if self.ndjson_file:
-                file = self.save_dir / "results.ndjson"
-                with open(file, "a") as f:
-                    print(json_data, file=f)
+        if self.ndjson_console:
+            print(json_data)
+        if self.ndjson_file:
+            file = self.save_dir / "results.ndjson"
+            with open(file, "a") as f:
+                print(json_data, file=f)
 
         if self.tb:
             for k, v in x.items():
@@ -370,10 +368,7 @@ def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")):
         if clearml and "clearml" in self.include:
             try:
                 # Hyp is not available in classification mode
-                if "hyp" not in opt:
-                    hyp = {}
-                else:
-                    hyp = opt.hyp
+                hyp = {} if "hyp" not in opt else opt.hyp
                 self.clearml = ClearmlLogger(opt, hyp)
             except Exception:
                 self.clearml = None
@@ -427,7 +422,9 @@ def log_graph(self, model, imgsz=(640, 640)):
         if self.tb:
             log_tensorboard_graph(self.tb, model, imgsz)
 
-    def log_model(self, model_path, epoch=0, metadata={}):
+    def log_model(self, model_path, epoch=0, metadata=None):
+        if metadata is None:
+            metadata = {}
         # Log model to all loggers
         if self.wandb:
             art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)

utils/loggers/clearml/README.md

@@ -34,15 +34,15 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t
 
 1. Install the `clearml` python package:
 
-   ```bash
-   pip install clearml
-   ```
+    ```bash
+    pip install clearml
+    ```
 
-1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
+2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
 
-   ```bash
-   clearml-init
-   ```
+    ```bash
+    clearml-init
+    ```
 
 That's it! You're done 😎
 
@@ -58,8 +58,7 @@ pip install clearml>=1.2.0
 
 This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
 
-If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`.
-PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
+If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
 
 ```bash
 python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
@@ -86,8 +85,7 @@ This will capture:
 - Validation images per epoch
 - ...
 
-That's a lot right? 🤯
-Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
+That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
 
 There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
 
@@ -181,8 +179,7 @@ python utils/loggers/clearml/hpo.py
 
 ## 🤯 Remote Execution (advanced)
 
-Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs.
-This is where the ClearML Agent comes into play. Check out what the agent can do here:
+Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
 
 - [YouTube video](https://youtu.be/MX3BrXnaULs)
 - [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)