readseek 0.6.4

// SPDX-License-Identifier: LGPL-2.1-or-later
// Copyright (c) 2026 Jarkko Sakkinen

// Based on YOLOv8 architecture code from https://github.com/huggingface/candle,
// which is licensed with MIT OR Apache-2.0.

#![allow(dead_code, clippy::all, clippy::pedantic)]
use candle::{D, DType, IndexOp, Result, Tensor};
use candle_nn::{Conv2d, Conv2dConfig, Module, VarBuilder, batch_norm, conv2d, conv2d_no_bias};

#[derive(Clone, Copy, PartialEq, Debug)]
pub struct Multiples {
    depth: f64,
    width: f64,
    ratio: f64,
}

impl Multiples {
    pub fn n() -> Self {
        Self {
            depth: 0.33,
            width: 0.25,
            ratio: 2.0,
        }
    }
    pub fn s() -> Self {
        Self {
            depth: 0.33,
            width: 0.50,
            ratio: 2.0,
        }
    }
    pub fn m() -> Self {
        Self {
            depth: 0.67,
            width: 0.75,
            ratio: 1.5,
        }
    }
    pub fn l() -> Self {
        Self {
            depth: 1.00,
            width: 1.00,
            ratio: 1.0,
        }
    }
    pub fn x() -> Self {
        Self {
            depth: 1.00,
            width: 1.25,
            ratio: 1.0,
        }
    }

    fn filters(&self) -> (usize, usize, usize) {
        let f1 = (256. * self.width) as usize;
        let f2 = (512. * self.width) as usize;
        let f3 = (512. * self.width * self.ratio) as usize;
        (f1, f2, f3)
    }
}

#[derive(Debug)]
struct Upsample {
    scale_factor: usize,
}

impl Upsample {
    fn new(scale_factor: usize) -> Result<Self> {
        Ok(Upsample { scale_factor })
    }
}

impl Module for Upsample {
    fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
        let (_b_size, _channels, h, w) = xs.dims4()?;
        xs.upsample_nearest2d(self.scale_factor * h, self.scale_factor * w)
    }
}

#[derive(Debug)]
struct ConvBlock {
    conv: Conv2d,
}

impl ConvBlock {
    fn load(
        vb: VarBuilder,
        c1: usize,
        c2: usize,
        k: usize,
        stride: usize,
        padding: Option<usize>,
    ) -> Result<Self> {
        let padding = padding.unwrap_or(k / 2);
        let cfg = Conv2dConfig {
            padding,
            stride,
            groups: 1,
            dilation: 1,
            cudnn_fwd_algo: None,
        };
        let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;
        let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?.absorb_bn(&bn)?;
        Ok(Self { conv })
    }
}

impl Module for ConvBlock {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let xs = self.conv.forward(xs)?;
        candle_nn::ops::silu(&xs)
    }
}

#[derive(Debug)]
struct Bottleneck {
    cv1: ConvBlock,
    cv2: ConvBlock,
    residual: bool,
}

impl Bottleneck {
    fn load(vb: VarBuilder, c1: usize, c2: usize, shortcut: bool) -> Result<Self> {
        let channel_factor = 1.;
        let c_ = (c2 as f64 * channel_factor) as usize;
        let cv1 = ConvBlock::load(vb.pp("cv1"), c1, c_, 3, 1, None)?;
        let cv2 = ConvBlock::load(vb.pp("cv2"), c_, c2, 3, 1, None)?;
        let residual = c1 == c2 && shortcut;
        Ok(Self { cv1, cv2, residual })
    }
}

impl Module for Bottleneck {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let ys = self.cv2.forward(&self.cv1.forward(xs)?)?;
        if self.residual { xs + ys } else { Ok(ys) }
    }
}

#[derive(Debug)]
struct C2f {
    cv1: ConvBlock,
    cv2: ConvBlock,
    bottleneck: Vec<Bottleneck>,
}

impl C2f {
    fn load(vb: VarBuilder, c1: usize, c2: usize, n: usize, shortcut: bool) -> Result<Self> {
        let c = (c2 as f64 * 0.5) as usize;
        let cv1 = ConvBlock::load(vb.pp("cv1"), c1, 2 * c, 1, 1, None)?;
        let cv2 = ConvBlock::load(vb.pp("cv2"), (2 + n) * c, c2, 1, 1, None)?;
        let mut bottleneck = Vec::with_capacity(n);
        for idx in 0..n {
            let b = Bottleneck::load(vb.pp(format!("bottleneck.{idx}")), c, c, shortcut)?;
            bottleneck.push(b)
        }
        Ok(Self {
            cv1,
            cv2,
            bottleneck,
        })
    }
}

impl Module for C2f {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let ys = self.cv1.forward(xs)?;
        let mut ys = ys.chunk(2, 1)?;
        for m in self.bottleneck.iter() {
            ys.push(m.forward(ys.last().unwrap())?)
        }
        let zs = Tensor::cat(ys.as_slice(), 1)?;
        self.cv2.forward(&zs)
    }
}

#[derive(Debug)]
struct Sppf {
    cv1: ConvBlock,
    cv2: ConvBlock,
    k: usize,
}

impl Sppf {
    fn load(vb: VarBuilder, c1: usize, c2: usize, k: usize) -> Result<Self> {
        let c_ = c1 / 2;
        let cv1 = ConvBlock::load(vb.pp("cv1"), c1, c_, 1, 1, None)?;
        let cv2 = ConvBlock::load(vb.pp("cv2"), c_ * 4, c2, 1, 1, None)?;
        Ok(Self { cv1, cv2, k })
    }
}

impl Module for Sppf {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let (_, _, _, _) = xs.dims4()?;
        let xs = self.cv1.forward(xs)?;
        let xs2 = xs
            .pad_with_zeros(2, self.k / 2, self.k / 2)?
            .pad_with_zeros(3, self.k / 2, self.k / 2)?
            .max_pool2d_with_stride(self.k, 1)?;
        let xs3 = xs2
            .pad_with_zeros(2, self.k / 2, self.k / 2)?
            .pad_with_zeros(3, self.k / 2, self.k / 2)?
            .max_pool2d_with_stride(self.k, 1)?;
        let xs4 = xs3
            .pad_with_zeros(2, self.k / 2, self.k / 2)?
            .pad_with_zeros(3, self.k / 2, self.k / 2)?
            .max_pool2d_with_stride(self.k, 1)?;
        self.cv2.forward(&Tensor::cat(&[&xs, &xs2, &xs3, &xs4], 1)?)
    }
}

#[derive(Debug)]
struct Dfl {
    conv: Conv2d,
    num_classes: usize,
}

impl Dfl {
    fn load(vb: VarBuilder, num_classes: usize) -> Result<Self> {
        let conv = conv2d_no_bias(num_classes, 1, 1, Default::default(), vb.pp("conv"))?;
        Ok(Self { conv, num_classes })
    }
}

impl Module for Dfl {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let (b_sz, _channels, anchors) = xs.dims3()?;
        let xs = xs
            .reshape((b_sz, 4, self.num_classes, anchors))?
            .transpose(2, 1)?;
        let xs = candle_nn::ops::softmax(&xs, 1)?;
        self.conv.forward(&xs)?.reshape((b_sz, 4, anchors))
    }
}

#[derive(Debug)]
struct DarkNet {
    b1_0: ConvBlock,
    b1_1: ConvBlock,
    b2_0: C2f,
    b2_1: ConvBlock,
    b2_2: C2f,
    b3_0: ConvBlock,
    b3_1: C2f,
    b4_0: ConvBlock,
    b4_1: C2f,
    b5: Sppf,
}

impl DarkNet {
    fn load(vb: VarBuilder, m: Multiples) -> Result<Self> {
        let (w, r, d) = (m.width, m.ratio, m.depth);
        let b1_0 = ConvBlock::load(vb.pp("b1.0"), 3, (64. * w) as usize, 3, 2, Some(1))?;
        let b1_1 = ConvBlock::load(
            vb.pp("b1.1"),
            (64. * w) as usize,
            (128. * w) as usize,
            3,
            2,
            Some(1),
        )?;
        let b2_0 = C2f::load(
            vb.pp("b2.0"),
            (128. * w) as usize,
            (128. * w) as usize,
            (3. * d).round() as usize,
            true,
        )?;
        let b2_1 = ConvBlock::load(
            vb.pp("b2.1"),
            (128. * w) as usize,
            (256. * w) as usize,
            3,
            2,
            Some(1),
        )?;
        let b2_2 = C2f::load(
            vb.pp("b2.2"),
            (256. * w) as usize,
            (256. * w) as usize,
            (6. * d).round() as usize,
            true,
        )?;
        let b3_0 = ConvBlock::load(
            vb.pp("b3.0"),
            (256. * w) as usize,
            (512. * w) as usize,
            3,
            2,
            Some(1),
        )?;
        let b3_1 = C2f::load(
            vb.pp("b3.1"),
            (512. * w) as usize,
            (512. * w) as usize,
            (6. * d).round() as usize,
            true,
        )?;
        let b4_0 = ConvBlock::load(
            vb.pp("b4.0"),
            (512. * w) as usize,
            (512. * w * r) as usize,
            3,
            2,
            Some(1),
        )?;
        let b4_1 = C2f::load(
            vb.pp("b4.1"),
            (512. * w * r) as usize,
            (512. * w * r) as usize,
            (3. * d).round() as usize,
            true,
        )?;
        let b5 = Sppf::load(
            vb.pp("b5.0"),
            (512. * w * r) as usize,
            (512. * w * r) as usize,
            5,
        )?;
        Ok(Self {
            b1_0,
            b1_1,
            b2_0,
            b2_1,
            b2_2,
            b3_0,
            b3_1,
            b4_0,
            b4_1,
            b5,
        })
    }

    fn forward(&self, xs: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
        let x1 = self.b1_1.forward(&self.b1_0.forward(xs)?)?;
        let x2 = self
            .b2_2
            .forward(&self.b2_1.forward(&self.b2_0.forward(&x1)?)?)?;
        let x3 = self.b3_1.forward(&self.b3_0.forward(&x2)?)?;
        let x4 = self.b4_1.forward(&self.b4_0.forward(&x3)?)?;
        let x5 = self.b5.forward(&x4)?;
        Ok((x2, x3, x5))
    }
}

#[derive(Debug)]
struct YoloV8Neck {
    up: Upsample,
    n1: C2f,
    n2: C2f,
    n3: ConvBlock,
    n4: C2f,
    n5: ConvBlock,
    n6: C2f,
}

impl YoloV8Neck {
    fn load(vb: VarBuilder, m: Multiples) -> Result<Self> {
        let up = Upsample::new(2)?;
        let (w, r, d) = (m.width, m.ratio, m.depth);
        let n = (3. * d).round() as usize;
        let n1 = C2f::load(
            vb.pp("n1"),
            (512. * w * (1. + r)) as usize,
            (512. * w) as usize,
            n,
            false,
        )?;
        let n2 = C2f::load(
            vb.pp("n2"),
            (768. * w) as usize,
            (256. * w) as usize,
            n,
            false,
        )?;
        let n3 = ConvBlock::load(
            vb.pp("n3"),
            (256. * w) as usize,
            (256. * w) as usize,
            3,
            2,
            Some(1),
        )?;
        let n4 = C2f::load(
            vb.pp("n4"),
            (768. * w) as usize,
            (512. * w) as usize,
            n,
            false,
        )?;
        let n5 = ConvBlock::load(
            vb.pp("n5"),
            (512. * w) as usize,
            (512. * w) as usize,
            3,
            2,
            Some(1),
        )?;
        let n6 = C2f::load(
            vb.pp("n6"),
            (512. * w * (1. + r)) as usize,
            (512. * w * r) as usize,
            n,
            false,
        )?;
        Ok(Self {
            up,
            n1,
            n2,
            n3,
            n4,
            n5,
            n6,
        })
    }

    fn forward(&self, p3: &Tensor, p4: &Tensor, p5: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
        let x = self
            .n1
            .forward(&Tensor::cat(&[&self.up.forward(p5)?, p4], 1)?)?;
        let head_1 = self
            .n2
            .forward(&Tensor::cat(&[&self.up.forward(&x)?, p3], 1)?)?;
        let head_2 = self
            .n4
            .forward(&Tensor::cat(&[&self.n3.forward(&head_1)?, &x], 1)?)?;
        let head_3 = self
            .n6
            .forward(&Tensor::cat(&[&self.n5.forward(&head_2)?, p5], 1)?)?;
        Ok((head_1, head_2, head_3))
    }
}

#[derive(Debug)]
struct DetectionHead {
    dfl: Dfl,
    cv2: [(ConvBlock, ConvBlock, Conv2d); 3],
    cv3: [(ConvBlock, ConvBlock, Conv2d); 3],
    ch: usize,
    no: usize,
}

#[derive(Debug)]
struct PoseHead {
    detect: DetectionHead,
    cv4: [(ConvBlock, ConvBlock, Conv2d); 3],
    kpt: (usize, usize),
}

fn make_anchors(
    xs0: &Tensor,
    xs1: &Tensor,
    xs2: &Tensor,
    (s0, s1, s2): (usize, usize, usize),
    grid_cell_offset: f64,
) -> Result<(Tensor, Tensor)> {
    let dev = xs0.device();
    let mut anchor_points = vec![];
    let mut stride_tensor = vec![];
    for (xs, stride) in [(xs0, s0), (xs1, s1), (xs2, s2)] {
        // xs is only used to extract the h and w dimensions.
        let (_, _, h, w) = xs.dims4()?;
        let sx = (Tensor::arange(0, w as u32, dev)?.to_dtype(DType::F32)? + grid_cell_offset)?;
        let sy = (Tensor::arange(0, h as u32, dev)?.to_dtype(DType::F32)? + grid_cell_offset)?;
        let sx = sx
            .reshape((1, sx.elem_count()))?
            .repeat((h, 1))?
            .flatten_all()?;
        let sy = sy
            .reshape((sy.elem_count(), 1))?
            .repeat((1, w))?
            .flatten_all()?;
        anchor_points.push(Tensor::stack(&[&sx, &sy], D::Minus1)?);
        stride_tensor.push((Tensor::ones(h * w, DType::F32, dev)? * stride as f64)?);
    }
    let anchor_points = Tensor::cat(anchor_points.as_slice(), 0)?;
    let stride_tensor = Tensor::cat(stride_tensor.as_slice(), 0)?.unsqueeze(1)?;
    Ok((anchor_points, stride_tensor))
}
fn dist2bbox(distance: &Tensor, anchor_points: &Tensor) -> Result<Tensor> {
    let chunks = distance.chunk(2, 1)?;
    let lt = &chunks[0];
    let rb = &chunks[1];
    let x1y1 = anchor_points.sub(lt)?;
    let x2y2 = anchor_points.add(rb)?;
    let c_xy = ((&x1y1 + &x2y2)? * 0.5)?;
    let wh = (&x2y2 - &x1y1)?;
    Tensor::cat(&[c_xy, wh], 1)
}

struct DetectionHeadOut {
    pred: Tensor,
    anchors: Tensor,
    strides: Tensor,
}

impl DetectionHead {
    fn load(vb: VarBuilder, nc: usize, filters: (usize, usize, usize)) -> Result<Self> {
        let ch = 16;
        let dfl = Dfl::load(vb.pp("dfl"), ch)?;
        let c1 = usize::max(filters.0, nc);
        let c2 = usize::max(filters.0 / 4, ch * 4);
        let cv3 = [
            Self::load_cv3(vb.pp("cv3.0"), c1, nc, filters.0)?,
            Self::load_cv3(vb.pp("cv3.1"), c1, nc, filters.1)?,
            Self::load_cv3(vb.pp("cv3.2"), c1, nc, filters.2)?,
        ];
        let cv2 = [
            Self::load_cv2(vb.pp("cv2.0"), c2, ch, filters.0)?,
            Self::load_cv2(vb.pp("cv2.1"), c2, ch, filters.1)?,
            Self::load_cv2(vb.pp("cv2.2"), c2, ch, filters.2)?,
        ];
        let no = nc + ch * 4;
        Ok(Self {
            dfl,
            cv2,
            cv3,
            ch,
            no,
        })
    }

    fn load_cv3(
        vb: VarBuilder,
        c1: usize,
        nc: usize,
        filter: usize,
    ) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
        let block0 = ConvBlock::load(vb.pp("0"), filter, c1, 3, 1, None)?;
        let block1 = ConvBlock::load(vb.pp("1"), c1, c1, 3, 1, None)?;
        let conv = conv2d(c1, nc, 1, Default::default(), vb.pp("2"))?;
        Ok((block0, block1, conv))
    }

    fn load_cv2(
        vb: VarBuilder,
        c2: usize,
        ch: usize,
        filter: usize,
    ) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
        let block0 = ConvBlock::load(vb.pp("0"), filter, c2, 3, 1, None)?;
        let block1 = ConvBlock::load(vb.pp("1"), c2, c2, 3, 1, None)?;
        let conv = conv2d(c2, 4 * ch, 1, Default::default(), vb.pp("2"))?;
        Ok((block0, block1, conv))
    }

    fn forward(&self, xs0: &Tensor, xs1: &Tensor, xs2: &Tensor) -> Result<DetectionHeadOut> {
        let forward_cv = |xs, i: usize| {
            let xs_2 = self.cv2[i].0.forward(xs)?;
            let xs_2 = self.cv2[i].1.forward(&xs_2)?;
            let xs_2 = self.cv2[i].2.forward(&xs_2)?;

            let xs_3 = self.cv3[i].0.forward(xs)?;
            let xs_3 = self.cv3[i].1.forward(&xs_3)?;
            let xs_3 = self.cv3[i].2.forward(&xs_3)?;
            Tensor::cat(&[&xs_2, &xs_3], 1)
        };
        let xs0 = forward_cv(xs0, 0)?;
        let xs1 = forward_cv(xs1, 1)?;
        let xs2 = forward_cv(xs2, 2)?;

        let (anchors, strides) = make_anchors(&xs0, &xs1, &xs2, (8, 16, 32), 0.5)?;
        let anchors = anchors.transpose(0, 1)?.unsqueeze(0)?;
        let strides = strides.transpose(0, 1)?;

        let reshape = |xs: &Tensor| {
            let d = xs.dim(0)?;
            let el = xs.elem_count();
            xs.reshape((d, self.no, el / (d * self.no)))
        };
        let ys0 = reshape(&xs0)?;
        let ys1 = reshape(&xs1)?;
        let ys2 = reshape(&xs2)?;

        let x_cat = Tensor::cat(&[ys0, ys1, ys2], 2)?;
        let box_ = x_cat.i((.., ..self.ch * 4))?;
        let cls = x_cat.i((.., self.ch * 4..))?;

        let dbox = dist2bbox(&self.dfl.forward(&box_)?, &anchors)?;
        let dbox = dbox.broadcast_mul(&strides)?;
        let pred = Tensor::cat(&[dbox, candle_nn::ops::sigmoid(&cls)?], 1)?;
        Ok(DetectionHeadOut {
            pred,
            anchors,
            strides,
        })
    }
}

impl PoseHead {
    // kpt: keypoints, (17, 3)
    // nc: num-classes, 80
    fn load(
        vb: VarBuilder,
        nc: usize,
        kpt: (usize, usize),
        filters: (usize, usize, usize),
    ) -> Result<Self> {
        let detect = DetectionHead::load(vb.clone(), nc, filters)?;
        let nk = kpt.0 * kpt.1;
        let c4 = usize::max(filters.0 / 4, nk);
        let cv4 = [
            Self::load_cv4(vb.pp("cv4.0"), c4, nk, filters.0)?,
            Self::load_cv4(vb.pp("cv4.1"), c4, nk, filters.1)?,
            Self::load_cv4(vb.pp("cv4.2"), c4, nk, filters.2)?,
        ];
        Ok(Self { detect, cv4, kpt })
    }

    fn load_cv4(
        vb: VarBuilder,
        c1: usize,
        nc: usize,
        filter: usize,
    ) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
        let block0 = ConvBlock::load(vb.pp("0"), filter, c1, 3, 1, None)?;
        let block1 = ConvBlock::load(vb.pp("1"), c1, c1, 3, 1, None)?;
        let conv = conv2d(c1, nc, 1, Default::default(), vb.pp("2"))?;
        Ok((block0, block1, conv))
    }

    fn forward(&self, xs0: &Tensor, xs1: &Tensor, xs2: &Tensor) -> Result<Tensor> {
        let d = self.detect.forward(xs0, xs1, xs2)?;
        let forward_cv = |xs: &Tensor, i: usize| {
            let (b_sz, _, h, w) = xs.dims4()?;
            let xs = self.cv4[i].0.forward(xs)?;
            let xs = self.cv4[i].1.forward(&xs)?;
            let xs = self.cv4[i].2.forward(&xs)?;
            xs.reshape((b_sz, self.kpt.0 * self.kpt.1, h * w))
        };
        let xs0 = forward_cv(xs0, 0)?;
        let xs1 = forward_cv(xs1, 1)?;
        let xs2 = forward_cv(xs2, 2)?;
        let xs = Tensor::cat(&[xs0, xs1, xs2], D::Minus1)?;
        let (b_sz, _nk, hw) = xs.dims3()?;
        let xs = xs.reshape((b_sz, self.kpt.0, self.kpt.1, hw))?;

        let ys01 = ((xs.i((.., .., 0..2))? * 2.)?.broadcast_add(&d.anchors)? - 0.5)?
            .broadcast_mul(&d.strides)?;
        let ys2 = candle_nn::ops::sigmoid(&xs.i((.., .., 2..3))?)?;
        let ys = Tensor::cat(&[ys01, ys2], 2)?.flatten(1, 2)?;
        Tensor::cat(&[d.pred, ys], 1)
    }
}

#[derive(Debug)]
pub struct YoloV8 {
    net: DarkNet,
    fpn: YoloV8Neck,
    head: DetectionHead,
}

impl YoloV8 {
    pub fn load(vb: VarBuilder, m: Multiples, num_classes: usize) -> Result<Self> {
        let net = DarkNet::load(vb.pp("net"), m)?;
        let fpn = YoloV8Neck::load(vb.pp("fpn"), m)?;
        let head = DetectionHead::load(vb.pp("head"), num_classes, m.filters())?;
        Ok(Self { net, fpn, head })
    }
}

impl Module for YoloV8 {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let (xs1, xs2, xs3) = self.net.forward(xs)?;
        let (xs1, xs2, xs3) = self.fpn.forward(&xs1, &xs2, &xs3)?;
        Ok(self.head.forward(&xs1, &xs2, &xs3)?.pred)
    }
}

#[derive(Debug)]
pub struct YoloV8Pose {
    net: DarkNet,
    fpn: YoloV8Neck,
    head: PoseHead,
}

impl YoloV8Pose {
    pub fn load(
        vb: VarBuilder,
        m: Multiples,
        num_classes: usize,
        kpt: (usize, usize),
    ) -> Result<Self> {
        let net = DarkNet::load(vb.pp("net"), m)?;
        let fpn = YoloV8Neck::load(vb.pp("fpn"), m)?;
        let head = PoseHead::load(vb.pp("head"), num_classes, kpt, m.filters())?;
        Ok(Self { net, fpn, head })
    }
}

impl Module for YoloV8Pose {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let (xs1, xs2, xs3) = self.net.forward(xs)?;
        let (xs1, xs2, xs3) = self.fpn.forward(&xs1, &xs2, &xs3)?;
        self.head.forward(&xs1, &xs2, &xs3)
    }
}

pub const COCO_CLASSES: [&str; 80] = [
    "person",
    "bicycle",
    "car",
    "motorbike",
    "aeroplane",
    "bus",
    "train",
    "truck",
    "boat",
    "traffic light",
    "fire hydrant",
    "stop sign",
    "parking meter",
    "bench",
    "bird",
    "cat",
    "dog",
    "horse",
    "sheep",
    "cow",
    "elephant",
    "bear",
    "zebra",
    "giraffe",
    "backpack",
    "umbrella",
    "handbag",
    "tie",
    "suitcase",
    "frisbee",
    "skis",
    "snowboard",
    "sports ball",
    "kite",
    "baseball bat",
    "baseball glove",
    "skateboard",
    "surfboard",
    "tennis racket",
    "bottle",
    "wine glass",
    "cup",
    "fork",
    "knife",
    "spoon",
    "bowl",
    "banana",
    "apple",
    "sandwich",
    "orange",
    "broccoli",
    "carrot",
    "hot dog",
    "pizza",
    "donut",
    "cake",
    "chair",
    "sofa",
    "pottedplant",
    "bed",
    "diningtable",
    "toilet",
    "tvmonitor",
    "laptop",
    "mouse",
    "remote",
    "keyboard",
    "cell phone",
    "microwave",
    "oven",
    "toaster",
    "sink",
    "refrigerator",
    "book",
    "clock",
    "vase",
    "scissors",
    "teddy bear",
    "hair drier",
    "toothbrush",
];