release app

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,6 +1,6 @@
 ---
-title: Yolobench
-emoji: 🌖
+title: YOLOBench
+emoji: 🚀
 colorFrom: purple
 colorTo: indigo
 sdk: gradio

app.py

@@ -0,0 +1,285 @@
+import gradio as gr
+
+from plotting import create_yolobench_plots, get_pareto_table
+from utils import DEEPLITE_DARK_BLUE_GRADIO
+
+def get_hw_description(hw_name):
+    HW_URLS = {
+        'Jetson Nano (GPU, ONNX Runtime, FP32)': 'https://8074457.fs1.hubspotusercontent-na1.net/hubfs/8074457/YOLOBench%20Hardware%20product%20sheets/JetsonNano_DataSheet_DS09366001v1.1.pdf',
+        'Raspberry Pi 4 Model B (CPU, TFLite, FP32)': 'https://8074457.fs1.hubspotusercontent-na1.net/hubfs/8074457/YOLOBench%20Hardware%20product%20sheets/raspberry-pi-4-datasheet.pdf',
+        'Intel® Core™i7-10875H (CPU, OpenVINO, FP32)': 'https://8074457.fs1.hubspotusercontent-na1.net/hubfs/8074457/YOLOBench%20Hardware%20product%20sheets/Intel_ARK_SpecificationsChart_2023_10_11.pdf',
+        'Khadas VIM3 (NPU, INT16)': 'https://8074457.fs1.hubspotusercontent-na1.net/hubfs/8074457/YOLOBench%20Hardware%20product%20sheets/khadas_vim3_specs.pdf',
+        'Orange Pi 5 (NPU, FP16)': 'https://8074457.fs1.hubspotusercontent-na1.net/hubfs/8074457/YOLOBench%20Hardware%20product%20sheets/OrangePi_5_RK3588S_User%20Manual_v1.5.pdf',
+    }
+
+    hw_url = HW_URLS[hw_name]
+    DESC = f"""
+    🔸 <span style="font-size:16px">Click </span>[<span style="font-size:16px">here</span>]({hw_url})<span style="font-size:16px"> for more information on the selected hardware platform.</span>
+    🔸 <span style="font-size:16px">Refer to the [Deeplite Torch Zoo](https://github.com/Deeplite/deeplite-torch-zoo/tree/develop/results/yolobench) for details about latency measurement experiments.</span>
+    """
+    return DESC
+
+
+with gr.Blocks(theme=gr.themes.Default(secondary_hue=DEEPLITE_DARK_BLUE_GRADIO),
+               css="table { width: 100%; }", analytics_enabled=True) as demo:
+
+    gr.HTML(
+        """
+        <div align="center">
+        <img src="file/banner.png"/>
+        </div>
+        """
+    )
+
+    # switch to light theme by default
+    demo.load(
+        None,
+        _js="""
+        () => {
+            let mediaQueryObj = window.matchMedia('(prefers-color-scheme: dark)');
+            let systemDarkTheme = window.location.href.includes("theme=system") && mediaQueryObj.matches;
+            if (mediaQueryObj.matches){
+                document.body.classList.toggle('dark');
+                document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
+            }
+        }
+        """,
+    )
+
+    demo.load(
+        None,
+        _js="""
+        () => {
+            const script2 = document.createElement("script");
+            script2.src = "https://www.googletagmanager.com/gtag/js?id=G-01G83VTHE0";
+            script2.async = true;
+            document.head.appendChild(script2);
+            window.dataLayer = window.dataLayer || [];
+            function gtag(){dataLayer.push(arguments);}
+            gtag('js', new Date());
+            gtag('config', 'G-01G83VTHE0', {
+                'page_path': "/spaces/deepliteai/yolobench",
+                'page_title': 'yolobench',
+                'cookie_flags': 'SameSite=None;Secure',
+                'debug_mode':true,
+            });
+        }
+        """,
+    )
+
+    with gr.Row():
+        gr.Markdown(
+       """
+        <span style="font-size:16px">
+
+        🚀 <b>YOLOBench</b> 🚀 is a latency-accuracy benchmark of popular single-stage detectors from the YOLO series. Major highlights of this work are:
+
+        🔸 includes architectures from YOLOv3 to YOLOv8, <br>
+        🔸 trained on <span style="font-weight:bold">four</span> popular object detection datasets (COCO, VOC, WIDER FACE, SKU-110k), <br>
+        🔸 latency measured on <span style="font-weight:bold">five embedded hardware platforms</span> (Jetson Nano GPU, ARM CPU, Intel CPU, Khadas VIM3 NPU, Orange Pi NPU), <br>
+        🔸 all models are trained with <span style="font-weight:bold">the same</span> training loop and hyperparameters (as implemented in the [Ultralytics YOLOv8 codebase](https://github.com/ultralytics/ultralytics)), <br>
+        🔸 both <span style="font-weight:bold">the detection head structure</span> and <span style="font-weight:bold"> the loss function </span> used are that of YOLOv8, giving a chance to isolate the contribution of the backbone/neck architecture on the latency-accuracy trade-off of YOLO models. <br>
+        In particular, we show that older backbone/neck structures like those of YOLOv3 and YOLOv4 are still competitive compared to more recent architectures in a controlled environment. For more details, please refer to the [arXiv preprint](https://arxiv.org/abs/2307.13901) and the [codebase](https://github.com/Deeplite/deeplite-torch-zoo).
+
+        #
+
+        </span>
+
+        #
+        """
+        )
+
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            hardware_name = gr.Dropdown(
+                choices=[
+                    'Jetson Nano (GPU, ONNX Runtime, FP32)',
+                    'Raspberry Pi 4 Model B (CPU, TFLite, FP32)',
+                    'Intel® Core™i7-10875H (CPU, OpenVINO, FP32)',
+                    'Khadas VIM3 (NPU, INT16)',
+                    'Orange Pi 5 (NPU, FP16)',
+                ],
+                value='Jetson Nano (GPU, ONNX Runtime, FP32)',
+                label='Hardware',
+            )
+        with gr.Column():
+            dataset_name = gr.Dropdown(
+                choices=['COCO', 'PASCAL VOC', 'SKU-110K', 'WIDERFACE'],
+                value='COCO',
+                label='Dataset',
+            )
+
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            hardware_desc = gr.Markdown(get_hw_description(hardware_name.value))
+
+        with gr.Column():
+            metric_name = gr.Radio(
+                ['mAP@0.5:0.95', 'mAP@0.5', 'Precision', 'Recall'],
+                value='mAP@0.5:0.95',
+                label='Accuracy metric to plot',
+            )
+
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            gr.Markdown("""
+                        <span style="font-size:16px">
+
+                        🚀 <span style="font-weight:bold">Want to add your own hardware benchmarks to YOLOBench?</span> 🚀
+                        Contact us [here](https://info.deeplite.ai/add_yolobench_data) for your benchmarking kit and we'll set you up!
+
+                        </span>
+                        """)
+
+        with gr.Column():
+            vis_options = gr.CheckboxGroup(
+                [
+                 'Model family',
+                 'Highlight Pareto',
+                 'Show Pareto only',
+                 'Log x-axis'
+                ],
+                value=['Model family',],
+                label='Visualization options',
+            )
+
+
+    with gr.Row():
+        upper_panel_fig = gr.Plot(show_label=False)
+
+    gr.Markdown(
+        """
+        ##
+        <span style="font-size:16px">
+
+        Models from this benchmark can be loaded using [Deeplite Torch Zoo](https://github.com/Deeplite/deeplite-torch-zoo) as follows:
+
+        </span>
+
+        ##
+
+        ```python
+        from deeplite_torch_zoo import get_model
+        model = get_model(
+            model_name='yolo4n',        # create a YOLOv4n model for the COCO dataset
+            dataset_name='coco',        # (`n` corresponds to width factor 0.25, depth factor 0.33)
+            pretrained=False,           #
+            custom_head='v8'            # attach a YOLOv8 detection head to YOLOv4n backbone+neck
+        )
+        ```
+
+        <span style="font-size:16px">
+
+        To train a model, run
+
+        </span>
+
+        ```python
+        from deeplite_torch_zoo.trainer import Detector
+        model = Detector(torch_model=model)                            # previously created YOLOv4n model
+        model.train(data='VOC.yaml', epochs=100, imgsz=480)            # same arguments as the Ultralytics trainer object
+        ```
+
+        ##
+
+        <details>
+        <summary>Model naming conventions</summary>
+
+        ##
+
+        The model naming convention is that a model named `yolo8d67w25` is a YOLOv8 model with a depth factor of 0.67 and width factor of 0.25. Conventional depth/width factor value namings (n, s, m, l models) are used where possible. YOLOv6(s, m, l) models are considered to be different architectures due to differences other than the depth/width factor value. For every architecture, there are 3 variations in depth factor (0.33, 0.67, 1.0) and 4 variations in width factor (0.25, 0.5, 0.75, 1.0), except for YOLOv7 models, for which only width factor variations are considered while depth is fixed.
+        </details>
+
+        ##
+
+        <span style="font-size:20px">
+        Pareto-optimal models
+        </span>
+
+        ##
+
+        COCO pre-trained models are ready for download.  Other models coming soon!
+        """
+    )
+
+    table_mode = gr.Radio(
+        ['Show top-10 models', 'Show all'],
+        value='Show top-10 models',
+        label='Pareto model table'
+    )
+
+    with gr.Row():
+        # pareto_table = gr.DataFrame(interactive=False)
+        pareto_table = gr.HTML()
+
+    gr.Markdown(
+        """
+        ## Citation
+        ```
+        Accepted at ICCV 2023 Workshop on Resource-Efficient Deep Learning for Computer Vision (RCV'23)
+        @article{lazarevich2023yolobench,
+            title={YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems},
+            author={Lazarevich, Ivan and Grimaldi, Matteo and Kumar, Ravish and Mitra, Saptarshi and Khan, Shahrukh and Sah, Sudhakar},
+            journal={arXiv preprint arXiv:2307.13901},
+            year={2023}
+        }
+        ```
+        """
+        )
+
+    inputs = [dataset_name, hardware_name, metric_name, vis_options, table_mode]
+
+    # plot by default (VOC, Raspi4)
+    demo.load(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+
+    demo.load(
+        fn=get_pareto_table,
+        inputs=[dataset_name, hardware_name, metric_name],
+        outputs=[pareto_table],
+    )
+
+    # update in case of dataset selection
+    dataset_name.change(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+    # update in case of metric selection
+    metric_name.change(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+
+    vis_options.change(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+
+    table_mode.change(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+
+    # update in case of device selection
+    hardware_name.change(
+        fn=create_yolobench_plots,
+        inputs=inputs,
+        outputs=[upper_panel_fig, pareto_table],
+    )
+
+    hardware_name.change(
+        fn=get_hw_description,
+        inputs=[hardware_name],
+        outputs=[hardware_desc],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

plotting.py

@@ -0,0 +1,205 @@
+import plotly.express as px
+import plotly.graph_objects as go
+
+from utils import DEEPLITE_LIGHT_BLUE_HEX, load_yolobench_data
+
+
+df, pareto_indices = load_yolobench_data()
+
+
+METRIC_NAME_MAPPING = {
+    'mAP@0.5': 'mAP_0.5',
+    'mAP@0.5:0.95': 'mAP_0.5:0.95',
+    'Precision': 'precision',
+    'Recall': 'recall',
+}
+
+METRIC_KEYS_TO_NAMES = {v: k for k, v in METRIC_NAME_MAPPING.items()}
+
+
+LATENCY_KEYS = {
+    'Raspberry Pi 4 Model B (CPU, TFLite, FP32)': 'raspi4_tflite_latency',
+    'Jetson Nano (GPU, ONNX Runtime, FP32)': 'nano_gpu_latency',
+    'Intel® Core™i7-10875H (CPU, OpenVINO, FP32)': 'openvino_latency',
+    'Khadas VIM3 (NPU, INT16)': 'vim3_latency',
+    'Orange Pi 5 (NPU, FP16)': 'orange_pi_latency',
+}
+
+LATENCY_KEYS_TO_NAMES = {v: k for k, v in LATENCY_KEYS.items()}
+
+DATASET_TAGS = {
+    'PASCAL VOC': 'voc',
+    'SKU-110K': 'sku',
+    'WIDERFACE': 'wider',
+    'COCO': 'coco',
+}
+
+DATASET_TAGS_TO_NAMES = {v: k for k, v in DATASET_TAGS.items()}
+
+
+def get_scatter_plot(
+        dataset_tag,
+        metric_tag,
+        latency_key,
+        model_family_coloring=True,
+        add_pareto_frontier=False,
+        plot_pareto_only=False,
+        log_axis=False,
+    ):
+    fig_opts, layout_opts = {'opacity': 0.5, 'color_discrete_sequence': [DEEPLITE_LIGHT_BLUE_HEX]}, {}
+    if model_family_coloring:
+        fig_opts = {
+            'color': 'model_family',
+            'opacity': 0.75,
+            'color_discrete_sequence': px.colors.qualitative.Plotly,
+        }
+        layout_opts = {
+            'legend': dict(
+                title='Model family<br>(click to toggle)',
+            )
+        }
+
+    frontier = None
+    if plot_pareto_only:
+        metric_key = f'{metric_tag}_{dataset_tag}'
+        frontier = pareto_indices[metric_key][latency_key]
+
+    fig = px.scatter(
+        df if frontier is None else df.iloc[frontier, :],
+        x=latency_key,
+        y=f'{metric_tag}_{dataset_tag}',
+        title=f'{METRIC_KEYS_TO_NAMES[metric_tag]}-latency scatter plot',
+        hover_data={
+            'model_name': True,
+            'model_family': False,
+            latency_key: ':.2f',
+            f'{metric_tag}_{dataset_tag}': ':.2f',
+        },
+        labels={
+            'model_name': 'Model name',
+            latency_key: 'Latency',
+            f'{metric_tag}_{dataset_tag}': METRIC_KEYS_TO_NAMES[metric_tag],
+        },
+        template='plotly_white',
+        **fig_opts,
+    )
+    if log_axis:
+        fig.update_xaxes(type='log')
+
+    fig.update_layout(
+        height=600,
+        modebar_remove=['lasso', 'autoscale', 'zoomin', 'zoomout', 'select2d', 'select'],
+        xaxis_title=f'{LATENCY_KEYS_TO_NAMES[latency_key]} latency, ms',
+        yaxis_title=f"{METRIC_KEYS_TO_NAMES[metric_tag]}",
+        xaxis=dict(
+            rangeslider=dict(
+                visible=True,
+                bgcolor=DEEPLITE_LIGHT_BLUE_HEX,
+                thickness=0.02,
+            ),
+        ),
+        yaxis=dict(
+            fixedrange=False,
+        ),
+        hoverlabel=dict(
+            # bgcolor="white",
+            font_size=14,
+            font_family='Source Sans Pro'
+        ),
+        **layout_opts,
+    )
+    if add_pareto_frontier:
+        fig = pareto_frontier_layer(fig, dataset_tag, metric_tag, latency_key)
+    return fig
+
+
+def create_yolobench_plots(
+        dataset_name,
+        hardware_name,
+        metric_name,
+        vis_options,
+        table_mode,
+    ):
+    model_family_coloring = 'Model family' in vis_options
+    add_pareto_frontier = 'Highlight Pareto' in vis_options
+    plot_pareto_only = 'Show Pareto only' in vis_options
+    log_axis = 'Log x-axis' in vis_options
+    fig = get_scatter_plot(
+        DATASET_TAGS[dataset_name],
+        METRIC_NAME_MAPPING[metric_name],
+        LATENCY_KEYS[hardware_name],
+        model_family_coloring,
+        add_pareto_frontier,
+        plot_pareto_only,
+        log_axis,
+    )
+    pareto_table = get_pareto_table(
+        dataset_name, hardware_name, metric_name, expand_table='Show all' in table_mode
+    )
+    return fig, pareto_table
+
+
+def pareto_frontier_layer(
+        fig,
+        dataset_tag,
+        metric_tag,
+        latency_key,
+    ):
+    metric_key = f'{metric_tag}_{dataset_tag}'
+    frontier = pareto_indices[metric_key][latency_key]
+    fig.add_trace(
+        go.Scatter(
+            x=df.iloc[frontier, :][latency_key],
+            y=df.iloc[frontier, :][metric_key],
+            mode='lines',
+            opacity=0.5,
+            line=go.scatter.Line(color='grey'),
+            showlegend=False,
+            name=metric_key,
+        )
+    )
+    return fig
+
+
+def get_pareto_table(
+    dataset_name, hardware_name, metric_name, expand_table=False,
+):
+    dataset_tag = DATASET_TAGS[dataset_name]
+    metric_tag = METRIC_NAME_MAPPING[metric_name]
+    latency_key = LATENCY_KEYS[hardware_name]
+    metric_key = f'{metric_tag}_{dataset_tag}'
+
+    latency_key_final = f'{LATENCY_KEYS_TO_NAMES[latency_key]} latency, ms'
+    metric_key_final = METRIC_KEYS_TO_NAMES[metric_tag]
+
+    frontier = pareto_indices[metric_key][latency_key]
+    table_df = df.iloc[frontier, :][['model_name', metric_key, latency_key]]
+    table_df['Input resolution (px)'] = table_df['model_name'].apply(lambda name: name.split('_')[-1])
+    table_df['Model name'] = table_df['model_name'].apply(lambda name: name.split('_')[0])
+    table_df[metric_key_final] = table_df[metric_key].apply(lambda val: round(val, 3))
+    table_df[latency_key_final] = table_df[latency_key].apply(lambda val: round(val, 2))
+
+    def make_clickable(url, name):
+        return f'<a href="{url}">{name}</a>'
+
+
+    if dataset_name == 'COCO':
+        table_df['Download link'] = table_df['model_name'].apply(
+            lambda name: f'https://download.deeplite.ai/zoo/models/YOLOBench/{name.split("_")[0]}_640.pt'
+        )
+        table_df['Download link'] = table_df.apply(lambda x: make_clickable(x['Download link'], 'Weights download'), axis=1)
+    else:
+        table_df['Download link'] = table_df['model_name'].apply(lambda s: 'Coming soon')
+
+
+    table_df = table_df[['Model name', 'Input resolution (px)',
+                         metric_key_final, latency_key_final, 'Download link']].sort_values(by=metric_key_final, ascending=False)
+    if not expand_table:
+        table_df = table_df.iloc[:10, :]
+
+    table_df = table_df.to_html(
+        classes='table',
+        escape=False, render_links=True, index=False
+    )
+
+    return table_df

requirements.txt

@@ -0,0 +1,3 @@
+gradio==3.41.2
+plotly==5.16.1
+pandas==1.4.4

utils.py

@@ -0,0 +1,58 @@
+import urllib.parse
+
+import pandas as pd
+from gradio.themes.utils.colors import Color
+
+
+DATA_URL = 'https://raw.githubusercontent.com/Deeplite/deeplite-torch-zoo/develop/results/yolobench/'
+
+DEEPLITE_DARK_BLUE_RGB = (0, 66, 107)
+DEEPLITE_DARK_BLUE_HEX = '#00426B'
+
+DEEPLITE_LIGHT_BLUE_RGB = (0, 148, 206)
+DEEPLITE_LIGHT_BLUE_HEX = '#0094CE'
+
+DEEPLITE_DARK_BLUE_GRADIO = Color(
+    name='deeplite_dark_blue',
+    c50=DEEPLITE_DARK_BLUE_HEX,
+    c100=DEEPLITE_DARK_BLUE_HEX,
+    c200=DEEPLITE_DARK_BLUE_HEX,
+    c300=DEEPLITE_DARK_BLUE_HEX,
+    c400=DEEPLITE_DARK_BLUE_HEX,
+    c500=DEEPLITE_DARK_BLUE_HEX,
+    c600=DEEPLITE_DARK_BLUE_HEX,
+    c700=DEEPLITE_DARK_BLUE_HEX,
+    c800=DEEPLITE_DARK_BLUE_HEX,
+    c900=DEEPLITE_DARK_BLUE_HEX,
+    c950=DEEPLITE_DARK_BLUE_HEX,
+)
+
+DEEPLITE_LIGHT_BLUE_GRADIO = Color(
+    name='deeplite_dark_blue',
+    c50=DEEPLITE_LIGHT_BLUE_HEX,
+    c100=DEEPLITE_LIGHT_BLUE_HEX,
+    c200=DEEPLITE_LIGHT_BLUE_HEX,
+    c300=DEEPLITE_LIGHT_BLUE_HEX,
+    c400=DEEPLITE_LIGHT_BLUE_HEX,
+    c500=DEEPLITE_LIGHT_BLUE_HEX,
+    c600=DEEPLITE_LIGHT_BLUE_HEX,
+    c700=DEEPLITE_LIGHT_BLUE_HEX,
+    c800=DEEPLITE_LIGHT_BLUE_HEX,
+    c900=DEEPLITE_LIGHT_BLUE_HEX,
+    c950=DEEPLITE_LIGHT_BLUE_HEX,
+)
+
+
+def load_yolobench_data():
+    df = pd.read_csv(urllib.parse.urljoin(DATA_URL, 'merged_results.csv'))
+    pareto_indices_df = pd.read_csv(urllib.parse.urljoin(DATA_URL, 'pareto_indices.csv'))
+    pareto_indices = {}
+    for row_idx in range(pareto_indices_df.shape[0]):
+        data_key = pareto_indices_df.iloc[row_idx, :]['data']
+        if data_key not in pareto_indices:
+            pareto_indices[data_key] = {}
+        hw_key = pareto_indices_df.iloc[row_idx, :]['hardware']
+        indices = pareto_indices_df.iloc[row_idx, :]['pareto_indices']
+        indices = [int(val) for val in indices.split(',')]
+        pareto_indices[data_key][hw_key] = indices
+    return df, pareto_indices