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image-detection-aesthetic-v1/anime_aesthetic.py

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+import argparse
+import json
+import os
+import random
+from copy import deepcopy
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from PIL import Image
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
+from timm.models.layers import DropPath, trunc_normal_
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from torchvision.transforms import functional
+
+
+# ========= Model =========
+
+# copy from https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+
+
+class LayerNorm(nn.Module):
+    """LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+    with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.data_format = data_format
+        if self.data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError
+        self.normalized_shape = (normalized_shape,)
+
+    def forward(self, x):
+        if self.data_format == "channels_last":
+            return F.layer_norm(
+                x, self.normalized_shape, self.weight, self.bias, self.eps
+            )
+        elif self.data_format == "channels_first":
+            u = x.mean(1, keepdim=True)
+            s = (x - u).pow(2).mean(1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.eps)
+            x = self.weight[:, None, None] * x + self.bias[:, None, None]
+            return x
+
+
+class GRN(nn.Module):
+    """GRN (Global Response Normalization) layer"""
+
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+class Block(nn.Module):
+    """ConvNeXtV2 Block.
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+    """
+
+    def __init__(self, dim, drop_path=0.0):
+        super().__init__()
+        self.dwconv = nn.Conv2d(
+            dim, dim, kernel_size=7, padding=3, groups=dim
+        )  # depthwise conv
+        self.norm = LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(
+            dim, 4 * dim
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(4 * dim)
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class ConvNeXtV2(nn.Module):
+    """ConvNeXt V2
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
+        dims (int[]): Feature dimension at each stage. Default: [96, 192, 384, 768]
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
+    """
+
+    def __init__(
+            self,
+            in_chans=3,
+            num_classes=1000,
+            depths=[3, 3, 9, 3],
+            dims=[96, 192, 384, 768],
+            drop_path_rate=0.0,
+            head_init_scale=1.0,
+    ):
+        super().__init__()
+        self.depths = depths
+        self.downsample_layers = (
+            nn.ModuleList()
+        )  # stem and 3 intermediate downsampling conv layers
+        stem = nn.Sequential(
+            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
+            LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
+        )
+        self.downsample_layers.append(stem)
+        for i in range(3):
+            downsample_layer = nn.Sequential(
+                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
+                nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
+            )
+            self.downsample_layers.append(downsample_layer)
+
+        self.stages = (
+            nn.ModuleList()
+        )  # 4 feature resolution stages, each consisting of multiple residual blocks
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        cur = 0
+        for i in range(4):
+            stage = nn.Sequential(
+                *[
+                    Block(dim=dims[i], drop_path=dp_rates[cur + j])
+                    for j in range(depths[i])
+                ]
+            )
+            self.stages.append(stage)
+            cur += depths[i]
+
+        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer
+        self.head = nn.Linear(dims[-1], num_classes)
+
+        self.apply(self._init_weights)
+        self.head.weight.data.mul_(head_init_scale)
+        self.head.bias.data.mul_(head_init_scale)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv2d, nn.Linear)):
+            trunc_normal_(m.weight, std=0.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward_features(self, x):
+        for i in range(4):
+            x = self.downsample_layers[i](x)
+            x = self.stages[i](x)
+        return self.norm(
+            x.mean([-2, -1])
+        )  # global average pooling, (N, C, H, W) -> (N, C)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+
+model_cfgs = {
+    "atto": [[2, 2, 6, 2], [40, 80, 160, 320]],
+    "femto": [[2, 2, 6, 2], [48, 96, 192, 384]],
+    "pico": [[2, 2, 6, 2], [64, 128, 256, 512]],
+    "nano": [[2, 2, 8, 2], [80, 160, 320, 640]],
+    "tiny": [[3, 3, 9, 3], [96, 192, 384, 768]],
+    "base": [[3, 3, 27, 3], [128, 256, 512, 1024]],
+    "large": [[3, 3, 27, 3], [192, 384, 768, 1536]],
+    "huge": [[3, 3, 27, 3], [352, 704, 1408, 2816]],
+}
+
+
+def convnextv2(cfg_name, **kwargs):
+    cfg = model_cfgs[cfg_name]
+    model = ConvNeXtV2(depths=cfg[0], dims=cfg[1], **kwargs)
+    return model
+
+
+# ========= Dataset =========
+
+EXTENSION = [".png", ".jpg", ".jpeg"]
+
+
+def file_ext(fname):
+    return os.path.splitext(fname)[1].lower()
+
+
+def rescale_pad(image, output_size, random_pad=False):
+    h, w = image.shape[-2:]
+    if h != output_size or w != output_size:
+        r = min(output_size / h, output_size / w)
+        new_h, new_w = int(h * r), int(w * r)
+        if random_pad:
+            r2 = random.uniform(0.9, 1)
+            new_h, new_w = int(new_h * r2), int(new_w * r2)
+        ph = output_size - new_h
+        pw = output_size - new_w
+        left = random.randint(0, pw) if random_pad else pw // 2
+        right = pw - left
+        top = random.randint(0, ph) if random_pad else ph // 2
+        bottom = ph - top
+        image = transforms.functional.resize(image, [new_h, new_w])
+        image = transforms.functional.pad(
+            image, [left, top, right, bottom], random.uniform(0, 1) if random_pad else 0
+        )
+    return image
+
+
+def random_crop(image, min_rate=0.8):
+    h, w = image.shape[-2:]
+    new_h, new_w = int(h * random.uniform(min_rate, 1)), int(w * random.uniform(min_rate, 1))
+    top = np.random.randint(0, h - new_h)
+    left = np.random.randint(0, w - new_w)
+    image = image[:, top: top + new_h, left: left + new_w]
+    return image
+
+
+class AnimeAestheticDataset(Dataset):
+    def __init__(self, path, img_size, xflip=True):
+        all_files = {
+            os.path.relpath(os.path.join(root, fname), path)
+            for root, _dirs, files in os.walk(path)
+            for fname in files
+        }
+        all_images = sorted(
+            fname for fname in all_files if file_ext(fname) in EXTENSION
+        )
+        with open(os.path.join(path, "label.json"), "r", encoding="utf8") as f:
+            labels = json.load(f)
+        image_list = []
+        label_list = []
+        for fname in all_images:
+            if fname not in labels:
+                continue
+            image_list.append(fname)
+            label_list.append(labels[fname])
+        self.path = path
+        self.img_size = img_size
+        self.xflip = xflip
+        self.image_list = image_list
+        self.label_list = label_list
+
+    def __len__(self):
+        length = len(self.image_list)
+        if self.xflip:
+            length *= 2
+        return length
+
+    def __getitem__(self, index):
+        real_len = len(self.image_list)
+        fname = self.image_list[index % real_len]
+        label = self.label_list[index % real_len]
+        image = Image.open(os.path.join(self.path, fname)).convert("RGB")
+        image = transforms.functional.to_tensor(image)
+        image = random_crop(image, 0.8)
+        image = rescale_pad(image, self.img_size, True)
+        if index // real_len != 0:
+            image = transforms.functional.hflip(image)
+        label = torch.tensor([label], dtype=torch.float32)
+        return image, label
+
+
+# ========= Train =========
+
+
+class AnimeAesthetic(pl.LightningModule):
+    def __init__(self, cfg: str, drop_path_rate=0.0, ema_decay=0):
+        super().__init__()
+        self.net = convnextv2(cfg, in_chans=3, num_classes=1, drop_path_rate=drop_path_rate)
+        self.ema_decay = ema_decay
+        self.ema = None
+        if ema_decay > 0:
+            self.ema = deepcopy(self.net)
+            self.ema.requires_grad_(False)
+
+    def configure_optimizers(self):
+        optimizer = optim.Adam(
+            self.net.parameters(),
+            lr=0.001,
+            betas=(0.9, 0.999),
+            eps=1e-08,
+            weight_decay=0,
+        )
+        return optimizer
+
+    def forward(self, x, use_ema=False):
+        x = (x - 0.5) / 0.5
+        net = self.ema if use_ema else self.net
+        return net(x)
+
+    def training_step(self, batch, batch_idx):
+        images, labels = batch
+        loss = F.mse_loss(self.forward(images, False), labels)
+        self.log_dict({"train/loss": loss})
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        images, labels = batch
+        mae = F.l1_loss(self.forward(images, False), labels)
+        logs = {"val/mae": mae}
+        if self.ema is not None:
+            mae_ema = F.l1_loss(self.forward(images, True), labels)
+            logs["val/mae_ema"] = mae_ema
+        self.log_dict(logs, sync_dist=True)
+
+    def on_train_batch_end(self, outputs, batch, batch_idx):
+        if self.ema is not None:
+            with torch.no_grad():
+                for ema_v, model_v in zip(
+                        self.ema.state_dict().values(), self.net.state_dict().values()
+                ):
+                    ema_v.copy_(
+                        self.ema_decay * ema_v + (1.0 - self.ema_decay) * model_v
+                    )
+
+
+def main(opt):
+    if not os.path.exists("lightning_logs"):
+        os.mkdir("lightning_logs")
+    torch.manual_seed(0)
+    np.random.seed(0)
+    print("---load dataset---")
+    full_dataset = AnimeAestheticDataset(opt.data, opt.img_size)
+    full_dataset_len = len(full_dataset)
+    train_dataset_len = int(full_dataset_len * opt.data_split)
+    val_dataset_len = full_dataset_len - train_dataset_len
+    train_dataset, val_dataset = torch.utils.data.random_split(
+        full_dataset, [train_dataset_len, val_dataset_len]
+    )
+    train_dataloader = DataLoader(
+        train_dataset,
+        batch_size=opt.batch_size_train,
+        shuffle=True,
+        persistent_workers=True,
+        num_workers=opt.workers_train,
+        pin_memory=True,
+    )
+    val_dataloader = DataLoader(
+        val_dataset,
+        batch_size=opt.batch_size_val,
+        shuffle=False,
+        persistent_workers=True,
+        num_workers=opt.workers_val,
+        pin_memory=True,
+    )
+    print(f"train: {len(train_dataset)}")
+    print(f"val: {len(val_dataset)}")
+    print("---define model---")
+    if opt.resume != "":
+        anime_aesthetic = AnimeAesthetic.load_from_checkpoint(
+            opt.resume, cfg=opt.cfg, drop_path_rate=opt.drop_path, ema_decay=opt.ema_decay
+        )
+    else:
+        anime_aesthetic = AnimeAesthetic(cfg=opt.cfg, drop_path_rate=opt.drop_path, ema_decay=opt.ema_decay)
+
+    print("---start train---")
+
+    checkpoint_callback = ModelCheckpoint(
+        monitor="val/mae",
+        mode="min",
+        save_top_k=1,
+        save_last=True,
+        auto_insert_metric_name=False,
+        filename="epoch={epoch},mae={val/mae:.4f}",
+    )
+    callbacks = [checkpoint_callback]
+    if opt.ema_decay > 0:
+        checkpoint_ema_callback = ModelCheckpoint(
+            monitor="val/mae_ema",
+            mode="min",
+            save_top_k=1,
+            save_last=False,
+            auto_insert_metric_name=False,
+            filename="epoch={epoch},mae-ema={val/mae_ema:.4f}",
+        )
+        callbacks.append(checkpoint_ema_callback)
+    trainer = Trainer(
+        precision=32 if opt.fp32 else 16,
+        accelerator=opt.accelerator,
+        devices=opt.devices,
+        max_epochs=opt.epoch,
+        benchmark=opt.benchmark,
+        accumulate_grad_batches=opt.acc_step,
+        val_check_interval=opt.val_epoch,
+        log_every_n_steps=opt.log_step,
+        strategy="ddp_find_unused_parameters_false" if opt.devices > 1 else None,
+        callbacks=callbacks,
+    )
+    trainer.fit(anime_aesthetic, train_dataloader, val_dataloader)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # model args
+    parser.add_argument(
+        "--cfg",
+        type=str,
+        default="tiny",
+        choices=list(model_cfgs.keys()),
+        help="model configure",
+    )
+    parser.add_argument(
+        "--resume", type=str, default="", help="resume training from ckpt"
+    )
+    parser.add_argument(
+        "--img-size",
+        type=int,
+        default=768,
+        help="image size for training and validation",
+    )
+
+    # dataset args
+    parser.add_argument(
+        "--data", type=str, default="./data", help="dataset path"
+    )
+    parser.add_argument(
+        "--data-split",
+        type=float,
+        default=0.9999,
+        help="split rate for training and validation",
+    )
+
+    # training args
+    parser.add_argument("--epoch", type=int, default=100, help="epoch num")
+    parser.add_argument(
+        "--batch-size-train", type=int, default=16, help="batch size for training"
+    )
+    parser.add_argument(
+        "--batch-size-val", type=int, default=2, help="batch size for val"
+    )
+    parser.add_argument(
+        "--workers-train",
+        type=int,
+        default=4,
+        help="workers num for training dataloader",
+    )
+    parser.add_argument(
+        "--workers-val",
+        type=int,
+        default=4,
+        help="workers num for validation dataloader",
+    )
+    parser.add_argument(
+        "--acc-step", type=int, default=8, help="gradient accumulation step"
+    )
+    parser.add_argument(
+        "--drop-path", type=float, default=0.1, help="Drop path rate"
+    )
+    parser.add_argument(
+        "--ema-decay", type=float, default=0.9999, help="use ema if ema-decay > 0"
+    )
+    parser.add_argument(
+        "--accelerator",
+        type=str,
+        default="gpu",
+        choices=["cpu", "gpu", "tpu", "ipu", "hpu", "auto"],
+        help="accelerator",
+    )
+    parser.add_argument("--devices", type=int, default=4, help="devices num")
+    parser.add_argument(
+        "--fp32", action="store_true", default=False, help="disable mix precision"
+    )
+    parser.add_argument(
+        "--benchmark", action="store_true", default=True, help="enable cudnn benchmark"
+    )
+    parser.add_argument(
+        "--log-step", type=int, default=2, help="log training loss every n steps"
+    )
+    parser.add_argument(
+        "--val-epoch", type=int, default=0.025, help="valid and save every n epoch"
+    )
+
+    opt = parser.parse_args()
+    print(opt)
+
+    main(opt)

image-detection-aesthetic-v1/export.py

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+import argparse
+import torch
+
+from anime_aesthetic import AnimeAesthetic, model_cfgs
+
+
+def export_onnx(model, img_size, path):
+    import onnx
+    from onnxsim import simplify
+    torch.onnx.export(model,  # model being run
+                      torch.randn(1, 3, img_size, img_size),  # model input (or a tuple for multiple inputs)
+                      path,  # where to save the model (can be a file or file-like object)
+                      export_params=True,  # store the trained parameter weights inside the model file
+                      opset_version=11,  # the ONNX version to export the model to
+                      do_constant_folding=True,  # whether to execute constant folding for optimization
+                      input_names=["img"],  # the model's input names
+                      output_names=["score"],  # the model's output names
+                      verbose=True
+                      )
+    onnx_model = onnx.load(path)
+    model_simp, check = simplify(onnx_model)
+    assert check, "Simplified ONNX model could not be validated"
+    onnx.save(model_simp, path)
+    print('finished exporting onnx')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # model args
+    parser.add_argument(
+        "--cfg",
+        type=str,
+        default="tiny",
+        choices=list(model_cfgs.keys()),
+        help="model configure",
+    )
+    parser.add_argument('--ckpt', type=str, default='lightning_logs/version_11/checkpoints/last.ckpt',
+                        help='model checkpoint path')
+    parser.add_argument('--out', type=str, default='model.onnx',
+                        help='output path')
+    parser.add_argument('--to', type=str, default='onnx', choices=["onnx"],
+                        help='export to ()')
+    parser.add_argument('--img-size', type=int, default=768,
+                        help='input image size')
+    opt = parser.parse_args()
+    print(opt)
+
+    model = AnimeAesthetic.load_from_checkpoint(opt.ckpt, cfg=opt.cfg, ema_decay=0.999, map_location="cpu",strict=False)
+    model = model.eval()
+    if opt.to == "onnx":
+        export_onnx(model, opt.img_size, opt.out)

image-detection-aesthetic-v1/model.ckpt

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+oid sha256:cce86c82505e04c649be523db86b22eb8c21f0cb347e8f52860668414d2e817d
+size 446141885

image-detection-aesthetic-v1/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ba57a7a71f57d453133e77059183cf4266aa28bce8c034f65cc658118371fef
+size 112193244

image-detection-aesthetic-v2/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

image-detection-aesthetic-v2/README.md

@@ -0,0 +1,16 @@
+---
+license: agpl-3.0
+---
+
+# Info
+
+Since people are downloading this and I don't know why, I'll add some information. This model is an image classifier fine-tuned on `microsoft/beit-base-patch16-384`.
+Its purpose is to be used in the dataset conditioning step for the [Waifu Diffusion project](https://huggingface.co/hakurei/waifu-diffusion), a fine-tune effort for Stable Diffusion. As WD1.4 is planned to have a *significantly large dataset* (~15m images), it is infeasible to analyze every image manually to determine whether or not it should be included in the final training dataset. This image classifier is trained on approximately 3.5k real-life and anime/manga images. Its purpose is to remove aesthetically worthless images from our dataset by classifying them as "`not_aesthetic`". The image classifier was trained to **err on the side of caution** and will generally tend to include images unless they are in a "manga-like" format, have messy lines and/or are sketches, or include an unacceptable amount of text (namely text that covers the primary subject of the image). The idea is that certain images will hurt a SD fine-tune.
+
+Note: This classifier is not perfect, just like every other classifier out there. However, with a sufficiently large dataset, any imperfections or misclassifications should average themselves out due to the Law of Large Numbers.
+
+You can test out the classifier [here](https://huggingface.co/spaces/cafeai/cafe_aesthetic_demo), along with some other classifiers for the project.
+
+
+# License
+Released under the aGPLv3. Use the model as you wish for any purpose. If you make changes, share the changes.

image-detection-aesthetic-v2/all_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 2.0,
+    "train_loss": 0.42350826412439346,
+    "train_runtime": 281.0207,
+    "train_samples_per_second": 21.735,
+    "train_steps_per_second": 0.057
+}

image-detection-aesthetic-v2/config.json

@@ -0,0 +1,1053 @@
+{
+  "_name_or_path": "microsoft/beit-base-patch16-384",
+  "architectures": [
+    "BeitForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auxiliary_channels": 256,
+  "auxiliary_concat_input": false,
+  "auxiliary_loss_weight": 0.4,
+  "auxiliary_num_convs": 1,
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_aesthetic",
+    "1": "aesthetic"
+  },
+  "image_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Afghan hound, Afghan": 160,
+    "African chameleon, Chamaeleo chamaeleon": 47,
+    "African crocodile, Nile crocodile, Crocodylus niloticus": 49,
+    "African elephant, Loxodonta africana": 386,
+    "African grey, African gray, Psittacus erithacus": 87,
+    "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275,
+    "Airedale, Airedale terrier": 191,
+    "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180,
+    "American alligator, Alligator mississipiensis": 50,
+    "American black bear, black bear, Ursus americanus, Euarctos americanus": 295,
+    "American chameleon, anole, Anolis carolinensis": 40,
+    "American coot, marsh hen, mud hen, water hen, Fulica americana": 137,
+    "American egret, great white heron, Egretta albus": 132,
+    "American lobster, Northern lobster, Maine lobster, Homarus americanus": 122,
+    "Angora, Angora rabbit": 332,
+    "Appenzeller": 240,
+    "Arabian camel, dromedary, Camelus dromedarius": 354,
+    "Arctic fox, white fox, Alopex lagopus": 279,
+    "Australian terrier": 193,
+    "Band Aid": 419,
+    "Bedlington terrier": 181,
+    "Bernese mountain dog": 239,
+    "Blenheim spaniel": 156,
+    "Border collie": 232,
+    "Border terrier": 182,
+    "Boston bull, Boston terrier": 195,
+    "Bouvier des Flandres, Bouviers des Flandres": 233,
+    "Brabancon griffon": 262,
+    "Brittany spaniel": 215,
+    "CD player": 485,
+    "Cardigan, Cardigan Welsh corgi": 264,
+    "Chesapeake Bay retriever": 209,
+    "Chihuahua": 151,
+    "Christmas stocking": 496,
+    "Crock Pot": 521,
+    "Dandie Dinmont, Dandie Dinmont terrier": 194,
+    "Doberman, Doberman pinscher": 236,
+    "Dungeness crab, Cancer magister": 118,
+    "Dutch oven": 544,
+    "Egyptian cat": 285,
+    "English foxhound": 167,
+    "English setter": 212,
+    "English springer, English springer spaniel": 217,
+    "EntleBucher": 241,
+    "Eskimo dog, husky": 248,
+    "European fire salamander, Salamandra salamandra": 25,
+    "European gallinule, Porphyrio porphyrio": 136,
+    "French bulldog": 245,
+    "French horn, horn": 566,
+    "French loaf": 930,
+    "German shepherd, German shepherd dog, German police dog, alsatian": 235,
+    "German short-haired pointer": 210,
+    "Gila monster, Heloderma suspectum": 45,
+    "Gordon setter": 214,
+    "Granny Smith": 948,
+    "Great Dane": 246,
+    "Great Pyrenees": 257,
+    "Greater Swiss Mountain dog": 238,
+    "Ibizan hound, Ibizan Podenco": 173,
+    "Indian cobra, Naja naja": 63,
+    "Indian elephant, Elephas maximus": 385,
+    "Irish setter, red setter": 213,
+    "Irish terrier": 184,
+    "Irish water spaniel": 221,
+    "Irish wolfhound": 170,
+    "Italian greyhound": 171,
+    "Japanese spaniel": 152,
+    "Kerry blue terrier": 183,
+    "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48,
+    "Labrador retriever": 208,
+    "Lakeland terrier": 189,
+    "Leonberg": 255,
+    "Lhasa, Lhasa apso": 204,
+    "Loafer": 630,
+    "Madagascar cat, ring-tailed lemur, Lemur catta": 383,
+    "Maltese dog, Maltese terrier, Maltese": 153,
+    "Mexican hairless": 268,
+    "Model T": 661,
+    "Newfoundland, Newfoundland dog": 256,
+    "Norfolk terrier": 185,
+    "Norwegian elkhound, elkhound": 174,
+    "Norwich terrier": 186,
+    "Old English sheepdog, bobtail": 229,
+    "Pekinese, Pekingese, Peke": 154,
+    "Pembroke, Pembroke Welsh corgi": 263,
+    "Persian cat": 283,
+    "Petri dish": 712,
+    "Polaroid camera, Polaroid Land camera": 732,
+    "Pomeranian": 259,
+    "Rhodesian ridgeback": 159,
+    "Rottweiler": 234,
+    "Saint Bernard, St Bernard": 247,
+    "Saluki, gazelle hound": 176,
+    "Samoyed, Samoyede": 258,
+    "Scotch terrier, Scottish terrier, Scottie": 199,
+    "Scottish deerhound, deerhound": 177,
+    "Sealyham terrier, Sealyham": 190,
+    "Shetland sheepdog, Shetland sheep dog, Shetland": 230,
+    "Shih-Tzu": 155,
+    "Siamese cat, Siamese": 284,
+    "Siberian husky": 250,
+    "Staffordshire bullterrier, Staffordshire bull terrier": 179,
+    "Sussex spaniel": 220,
+    "Tibetan mastiff": 244,
+    "Tibetan terrier, chrysanthemum dog": 200,
+    "Walker hound, Walker foxhound": 166,
+    "Weimaraner": 178,
+    "Welsh springer spaniel": 218,
+    "West Highland white terrier": 203,
+    "Windsor tie": 906,
+    "Yorkshire terrier": 187,
+    "abacus": 398,
+    "abaya": 399,
+    "academic gown, academic robe, judge's robe": 400,
+    "accordion, piano accordion, squeeze box": 401,
+    "acorn": 988,
+    "acorn squash": 941,
+    "acoustic guitar": 402,
+    "admiral": 321,
+    "affenpinscher, monkey pinscher, monkey dog": 252,
+    "agama": 42,
+    "agaric": 992,
+    "aircraft carrier, carrier, flattop, attack aircraft carrier": 403,
+    "airliner": 404,
+    "airship, dirigible": 405,
+    "albatross, mollymawk": 146,
+    "alligator lizard": 44,
+    "alp": 970,
+    "altar": 406,
+    "ambulance": 407,
+    "amphibian, amphibious vehicle": 408,
+    "analog clock": 409,
+    "anemone fish": 393,
+    "ant, emmet, pismire": 310,
+    "apiary, bee house": 410,
+    "apron": 411,
+    "armadillo": 363,
+    "artichoke, globe artichoke": 944,
+    "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412,
+    "assault rifle, assault gun": 413,
+    "axolotl, mud puppy, Ambystoma mexicanum": 29,
+    "baboon": 372,
+    "backpack, back pack, knapsack, packsack, rucksack, haversack": 414,
+    "badger": 362,
+    "bagel, beigel": 931,
+    "bakery, bakeshop, bakehouse": 415,
+    "balance beam, beam": 416,
+    "bald eagle, American eagle, Haliaeetus leucocephalus": 22,
+    "balloon": 417,
+    "ballplayer, baseball player": 981,
+    "ballpoint, ballpoint pen, ballpen, Biro": 418,
+    "banana": 954,
+    "banded gecko": 38,
+    "banjo": 420,
+    "bannister, banister, balustrade, balusters, handrail": 421,
+    "barbell": 422,
+    "barber chair": 423,
+    "barbershop": 424,
+    "barn": 425,
+    "barn spider, Araneus cavaticus": 73,
+    "barometer": 426,
+    "barracouta, snoek": 389,
+    "barrel, cask": 427,
+    "barrow, garden cart, lawn cart, wheelbarrow": 428,
+    "baseball": 429,
+    "basenji": 253,
+    "basketball": 430,
+    "basset, basset hound": 161,
+    "bassinet": 431,
+    "bassoon": 432,
+    "bath towel": 434,
+    "bathing cap, swimming cap": 433,
+    "bathtub, bathing tub, bath, tub": 435,
+    "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436,
+    "beacon, lighthouse, beacon light, pharos": 437,
+    "beagle": 162,
+    "beaker": 438,
+    "bearskin, busby, shako": 439,
+    "beaver": 337,
+    "bee": 309,
+    "bee eater": 92,
+    "beer bottle": 440,
+    "beer glass": 441,
+    "bell cote, bell cot": 442,
+    "bell pepper": 945,
+    "bib": 443,
+    "bicycle-built-for-two, tandem bicycle, tandem": 444,
+    "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349,
+    "bikini, two-piece": 445,
+    "binder, ring-binder": 446,
+    "binoculars, field glasses, opera glasses": 447,
+    "birdhouse": 448,
+    "bison": 347,
+    "bittern": 133,
+    "black and gold garden spider, Argiope aurantia": 72,
+    "black grouse": 80,
+    "black stork, Ciconia nigra": 128,
+    "black swan, Cygnus atratus": 100,
+    "black widow, Latrodectus mactans": 75,
+    "black-and-tan coonhound": 165,
+    "black-footed ferret, ferret, Mustela nigripes": 359,
+    "bloodhound, sleuthhound": 163,
+    "bluetick": 164,
+    "boa constrictor, Constrictor constrictor": 61,
+    "boathouse": 449,
+    "bobsled, bobsleigh, bob": 450,
+    "bolete": 997,
+    "bolo tie, bolo, bola tie, bola": 451,
+    "bonnet, poke bonnet": 452,
+    "book jacket, dust cover, dust jacket, dust wrapper": 921,
+    "bookcase": 453,
+    "bookshop, bookstore, bookstall": 454,
+    "borzoi, Russian wolfhound": 169,
+    "bottlecap": 455,
+    "bow": 456,
+    "bow tie, bow-tie, bowtie": 457,
+    "box turtle, box tortoise": 37,
+    "boxer": 242,
+    "brain coral": 109,
+    "brambling, Fringilla montifringilla": 10,
+    "brass, memorial tablet, plaque": 458,
+    "brassiere, bra, bandeau": 459,
+    "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460,
+    "breastplate, aegis, egis": 461,
+    "briard": 226,
+    "broccoli": 937,
+    "broom": 462,
+    "brown bear, bruin, Ursus arctos": 294,
+    "bubble": 971,
+    "bucket, pail": 463,
+    "buckeye, horse chestnut, conker": 990,
+    "buckle": 464,
+    "bulbul": 16,
+    "bull mastiff": 243,
+    "bullet train, bullet": 466,
+    "bulletproof vest": 465,
+    "bullfrog, Rana catesbeiana": 30,
+    "burrito": 965,
+    "bustard": 138,
+    "butcher shop, meat market": 467,
+    "butternut squash": 942,
+    "cab, hack, taxi, taxicab": 468,
+    "cabbage butterfly": 324,
+    "cairn, cairn terrier": 192,
+    "caldron, cauldron": 469,
+    "can opener, tin opener": 473,
+    "candle, taper, wax light": 470,
+    "cannon": 471,
+    "canoe": 472,
+    "capuchin, ringtail, Cebus capucinus": 378,
+    "car mirror": 475,
+    "car wheel": 479,
+    "carbonara": 959,
+    "cardigan": 474,
+    "cardoon": 946,
+    "carousel, carrousel, merry-go-round, roundabout, whirligig": 476,
+    "carpenter's kit, tool kit": 477,
+    "carton": 478,
+    "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480,
+    "cassette": 481,
+    "cassette player": 482,
+    "castle": 483,
+    "catamaran": 484,
+    "cauliflower": 938,
+    "cello, violoncello": 486,
+    "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487,
+    "centipede": 79,
+    "chain": 488,
+    "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490,
+    "chain saw, chainsaw": 491,
+    "chainlink fence": 489,
+    "chambered nautilus, pearly nautilus, nautilus": 117,
+    "cheeseburger": 933,
+    "cheetah, chetah, Acinonyx jubatus": 293,
+    "chest": 492,
+    "chickadee": 19,
+    "chiffonier, commode": 493,
+    "chime, bell, gong": 494,
+    "chimpanzee, chimp, Pan troglodytes": 367,
+    "china cabinet, china closet": 495,
+    "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116,
+    "chocolate sauce, chocolate syrup": 960,
+    "chow, chow chow": 260,
+    "church, church building": 497,
+    "cicada, cicala": 316,
+    "cinema, movie theater, movie theatre, movie house, picture palace": 498,
+    "cleaver, meat cleaver, chopper": 499,
+    "cliff dwelling": 500,
+    "cliff, drop, drop-off": 972,
+    "cloak": 501,
+    "clog, geta, patten, sabot": 502,
+    "clumber, clumber spaniel": 216,
+    "cock": 7,
+    "cocker spaniel, English cocker spaniel, cocker": 219,
+    "cockroach, roach": 314,
+    "cocktail shaker": 503,
+    "coffee mug": 504,
+    "coffeepot": 505,
+    "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391,
+    "coil, spiral, volute, whorl, helix": 506,
+    "collie": 231,
+    "colobus, colobus monkey": 375,
+    "combination lock": 507,
+    "comic book": 917,
+    "common iguana, iguana, Iguana iguana": 39,
+    "common newt, Triturus vulgaris": 26,
+    "computer keyboard, keypad": 508,
+    "conch": 112,
+    "confectionery, confectionary, candy store": 509,
+    "consomme": 925,
+    "container ship, containership, container vessel": 510,
+    "convertible": 511,
+    "coral fungus": 991,
+    "coral reef": 973,
+    "corkscrew, bottle screw": 512,
+    "corn": 987,
+    "cornet, horn, trumpet, trump": 513,
+    "coucal": 91,
+    "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286,
+    "cowboy boot": 514,
+    "cowboy hat, ten-gallon hat": 515,
+    "coyote, prairie wolf, brush wolf, Canis latrans": 272,
+    "cradle": 516,
+    "crane": 517,
+    "crash helmet": 518,
+    "crate": 519,
+    "crayfish, crawfish, crawdad, crawdaddy": 124,
+    "crib, cot": 520,
+    "cricket": 312,
+    "croquet ball": 522,
+    "crossword puzzle, crossword": 918,
+    "crutch": 523,
+    "cucumber, cuke": 943,
+    "cuirass": 524,
+    "cup": 968,
+    "curly-coated retriever": 206,
+    "custard apple": 956,
+    "daisy": 985,
+    "dalmatian, coach dog, carriage dog": 251,
+    "dam, dike, dyke": 525,
+    "damselfly": 320,
+    "desk": 526,
+    "desktop computer": 527,
+    "dhole, Cuon alpinus": 274,
+    "dial telephone, dial phone": 528,
+    "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67,
+    "diaper, nappy, napkin": 529,
+    "digital clock": 530,
+    "digital watch": 531,
+    "dingo, warrigal, warragal, Canis dingo": 273,
+    "dining table, board": 532,
+    "dishrag, dishcloth": 533,
+    "dishwasher, dish washer, dishwashing machine": 534,
+    "disk brake, disc brake": 535,
+    "dock, dockage, docking facility": 536,
+    "dogsled, dog sled, dog sleigh": 537,
+    "dome": 538,
+    "doormat, welcome mat": 539,
+    "dough": 961,
+    "dowitcher": 142,
+    "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319,
+    "drake": 97,
+    "drilling platform, offshore rig": 540,
+    "drum, membranophone, tympan": 541,
+    "drumstick": 542,
+    "dugong, Dugong dugon": 149,
+    "dumbbell": 543,
+    "dung beetle": 305,
+    "ear, spike, capitulum": 998,
+    "earthstar": 995,
+    "echidna, spiny anteater, anteater": 102,
+    "eel": 390,
+    "eft": 27,
+    "eggnog": 969,
+    "electric fan, blower": 545,
+    "electric guitar": 546,
+    "electric locomotive": 547,
+    "electric ray, crampfish, numbfish, torpedo": 5,
+    "entertainment center": 548,
+    "envelope": 549,
+    "espresso": 967,
+    "espresso maker": 550,
+    "face powder": 551,
+    "feather boa, boa": 552,
+    "fiddler crab": 120,
+    "fig": 952,
+    "file, file cabinet, filing cabinet": 553,
+    "fire engine, fire truck": 555,
+    "fire screen, fireguard": 556,
+    "fireboat": 554,
+    "flagpole, flagstaff": 557,
+    "flamingo": 130,
+    "flat-coated retriever": 205,
+    "flatworm, platyhelminth": 110,
+    "flute, transverse flute": 558,
+    "fly": 308,
+    "folding chair": 559,
+    "football helmet": 560,
+    "forklift": 561,
+    "fountain": 562,
+    "fountain pen": 563,
+    "four-poster": 564,
+    "fox squirrel, eastern fox squirrel, Sciurus niger": 335,
+    "freight car": 565,
+    "frilled lizard, Chlamydosaurus kingi": 43,
+    "frying pan, frypan, skillet": 567,
+    "fur coat": 568,
+    "gar, garfish, garpike, billfish, Lepisosteus osseus": 395,
+    "garbage truck, dustcart": 569,
+    "garden spider, Aranea diademata": 74,
+    "garter snake, grass snake": 57,
+    "gas pump, gasoline pump, petrol pump, island dispenser": 571,
+    "gasmask, respirator, gas helmet": 570,
+    "gazelle": 353,
+    "geyser": 974,
+    "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388,
+    "giant schnauzer": 197,
+    "gibbon, Hylobates lar": 368,
+    "go-kart": 573,
+    "goblet": 572,
+    "golden retriever": 207,
+    "goldfinch, Carduelis carduelis": 11,
+    "goldfish, Carassius auratus": 1,
+    "golf ball": 574,
+    "golfcart, golf cart": 575,
+    "gondola": 576,
+    "gong, tam-tam": 577,
+    "goose": 99,
+    "gorilla, Gorilla gorilla": 366,
+    "gown": 578,
+    "grand piano, grand": 579,
+    "grasshopper, hopper": 311,
+    "great grey owl, great gray owl, Strix nebulosa": 24,
+    "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2,
+    "green lizard, Lacerta viridis": 46,
+    "green mamba": 64,
+    "green snake, grass snake": 55,
+    "greenhouse, nursery, glasshouse": 580,
+    "grey fox, gray fox, Urocyon cinereoargenteus": 280,
+    "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147,
+    "grille, radiator grille": 581,
+    "grocery store, grocery, food market, market": 582,
+    "groenendael": 224,
+    "groom, bridegroom": 982,
+    "ground beetle, carabid beetle": 302,
+    "guacamole": 924,
+    "guenon, guenon monkey": 370,
+    "guillotine": 583,
+    "guinea pig, Cavia cobaya": 338,
+    "gyromitra": 993,
+    "hair slide": 584,
+    "hair spray": 585,
+    "half track": 586,
+    "hammer": 587,
+    "hammerhead, hammerhead shark": 4,
+    "hamper": 588,
+    "hamster": 333,
+    "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589,
+    "hand-held computer, hand-held microcomputer": 590,
+    "handkerchief, hankie, hanky, hankey": 591,
+    "hard disc, hard disk, fixed disk": 592,
+    "hare": 331,
+    "harmonica, mouth organ, harp, mouth harp": 593,
+    "harp": 594,
+    "hartebeest": 351,
+    "harvester, reaper": 595,
+    "harvestman, daddy longlegs, Phalangium opilio": 70,
+    "hatchet": 596,
+    "hay": 958,
+    "head cabbage": 936,
+    "hen": 8,
+    "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996,
+    "hermit crab": 125,
+    "hip, rose hip, rosehip": 989,
+    "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344,
+    "hog, pig, grunter, squealer, Sus scrofa": 341,
+    "hognose snake, puff adder, sand viper": 54,
+    "holster": 597,
+    "home theater, home theatre": 598,
+    "honeycomb": 599,
+    "hook, claw": 600,
+    "hoopskirt, crinoline": 601,
+    "horizontal bar, high bar": 602,
+    "hornbill": 93,
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+    "necklace": 679,
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+    "night snake, Hypsiglena torquata": 60,
+    "nipple": 680,
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+    "obelisk": 682,
+    "oboe, hautboy, hautbois": 683,
+    "ocarina, sweet potato": 684,
+    "odometer, hodometer, mileometer, milometer": 685,
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+    "orange": 950,
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+    "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688,
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+    "paddlewheel, paddle wheel": 694,
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+    "parallel bars, bars": 702,
+    "park bench": 703,
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+    "pencil sharpener": 710,
+    "perfume, essence": 711,
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+    "pickup, pickup truck": 717,
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+    "pillow": 721,
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+    "ping-pong ball": 722,
+    "pinwheel": 723,
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+    "pitcher, ewer": 725,
+    "pizza, pizza pie": 963,
+    "plane, carpenter's plane, woodworking plane": 726,
+    "planetarium": 727,
+    "plastic bag": 728,
+    "plate": 923,
+    "plate rack": 729,
+    "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103,
+    "plow, plough": 730,
+    "plunger, plumber's helper": 731,
+    "pole": 733,
+    "polecat, fitch, foulmart, foumart, Mustela putorius": 358,
+    "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734,
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+    "poncho": 735,
+    "pool table, billiard table, snooker table": 736,
+    "pop bottle, soda bottle": 737,
+    "porcupine, hedgehog": 334,
+    "pot, flowerpot": 738,
+    "potpie": 964,
+    "potter's wheel": 739,
+    "power drill": 740,
+    "prairie chicken, prairie grouse, prairie fowl": 83,
+    "prayer rug, prayer mat": 741,
+    "pretzel": 932,
+    "printer": 742,
+    "prison, prison house": 743,
+    "proboscis monkey, Nasalis larvatus": 376,
+    "projectile, missile": 744,
+    "projector": 745,
+    "promontory, headland, head, foreland": 976,
+    "ptarmigan": 81,
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+    "puffer, pufferfish, blowfish, globefish": 397,
+    "pug, pug-dog": 254,
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+    "quail": 85,
+    "quill, quill pen": 749,
+    "quilt, comforter, comfort, puff": 750,
+    "racer, race car, racing car": 751,
+    "racket, racquet": 752,
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+    "radio telescope, radio reflector": 755,
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+    "ram, tup": 348,
+    "rapeseed": 984,
+    "recreational vehicle, RV, R.V.": 757,
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+    "red wine": 966,
+    "red wolf, maned wolf, Canis rufus, Canis niger": 271,
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+    "reflex camera": 759,
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+    "remote control, remote": 761,
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+    "water tower": 900,
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+    "whiskey jug": 901,
+    "whistle": 902,
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+    "window shade": 905,
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+    "wombat": 106,
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+    "wool, woolen, woollen": 911,
+    "worm fence, snake fence, snake-rail fence, Virginia fence": 912,
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+    "yawl": 914,
+    "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum": 986,
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+    "zucchini, courgette": 939
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+  "use_mask_token": false,
+  "use_mean_pooling": true,
+  "use_relative_position_bias": true,
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+}

image-detection-aesthetic-v2/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
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+  "do_center_crop": false,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "BeitFeatureExtractor",
+  "image_mean": [
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+  "reduce_labels": false,
+  "resample": 2,
+  "size": 384
+}

image-detection-aesthetic-v2/pytorch_model.bin

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image-detection-aesthetic-v2/train_results.json

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image-detection-aesthetic-v2/trainer_state.json

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image-detection-aesthetic-v2/training_args.bin

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image-detection-aesthetic-v3/config.json

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+    "necklace": 679,
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+    "ocarina, sweet potato": 684,
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+    "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688,
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+    "parallel bars, bars": 702,
+    "park bench": 703,
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+    "pencil sharpener": 710,
+    "perfume, essence": 711,
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+    "pickelhaube": 715,
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+    "pickup, pickup truck": 717,
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+    "pillow": 721,
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+    "ping-pong ball": 722,
+    "pinwheel": 723,
+    "pirate, pirate ship": 724,
+    "pitcher, ewer": 725,
+    "pizza, pizza pie": 963,
+    "plane, carpenter's plane, woodworking plane": 726,
+    "planetarium": 727,
+    "plastic bag": 728,
+    "plate": 923,
+    "plate rack": 729,
+    "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103,
+    "plow, plough": 730,
+    "plunger, plumber's helper": 731,
+    "pole": 733,
+    "polecat, fitch, foulmart, foumart, Mustela putorius": 358,
+    "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734,
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+    "pool table, billiard table, snooker table": 736,
+    "pop bottle, soda bottle": 737,
+    "porcupine, hedgehog": 334,
+    "pot, flowerpot": 738,
+    "potpie": 964,
+    "potter's wheel": 739,
+    "power drill": 740,
+    "prairie chicken, prairie grouse, prairie fowl": 83,
+    "prayer rug, prayer mat": 741,
+    "pretzel": 932,
+    "printer": 742,
+    "prison, prison house": 743,
+    "proboscis monkey, Nasalis larvatus": 376,
+    "projectile, missile": 744,
+    "projector": 745,
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+    "puffer, pufferfish, blowfish, globefish": 397,
+    "pug, pug-dog": 254,
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+    "racer, race car, racing car": 751,
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+    "rapeseed": 984,
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+    "red wine": 966,
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+    "wombat": 106,
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+    "yawl": 914,
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+  "use_mask_token": false,
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+  "use_relative_position_bias": true,
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+}

image-detection-aesthetic-v3/preprocessor_config.json

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+  "resample": 2,
+  "size": 384
+}

image-detection-aesthetic-v3/pytorch_model.bin

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image-detection-aesthetic-v3/training_args.bin

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image-detection-nsfw-v1/README.md

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+---
+license: apache-2.0
+pipeline_tag: image-classification
+---
+# Model Card: Fine-Tuned Vision Transformer (ViT) for NSFW Image Classification
+
+## Model Description
+
+The **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks.
+
+During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. The model was fine-tuned with a judiciously chosen batch size of 16. This choice not only balanced computational efficiency but also allowed for the model to effectively process and learn from a diverse array of images.
+
+To facilitate this fine-tuning process, a learning rate of 5e-5 was employed. The learning rate serves as a critical tuning parameter that dictates the magnitude of adjustments made to the model's parameters during training. In this case, a learning rate of 5e-5 was selected to strike a harmonious balance between rapid convergence and steady optimization, resulting in a model that not only learns swiftly but also steadily refines its capabilities throughout the training process.
+
+This training phase was executed using a proprietary dataset containing an extensive collection of 80,000 images, each characterized by a substantial degree of variability. The dataset was thoughtfully curated to include two distinct classes, namely "normal" and "nsfw." This diversity allowed the model to grasp nuanced visual patterns, equipping it with the competence to accurately differentiate between safe and explicit content.
+
+The overarching objective of this meticulous training process was to impart the model with a deep understanding of visual cues, ensuring its robustness and competence in tackling the specific task of NSFW image classification. The result is a model that stands ready to contribute significantly to content safety and moderation, all while maintaining the highest standards of accuracy and reliability.
+## Intended Uses & Limitations
+
+### Intended Uses
+- **NSFW Image Classification**: The primary intended use of this model is for the classification of NSFW (Not Safe for Work) images. It has been fine-tuned for this purpose, making it suitable for filtering explicit or inappropriate content in various applications.
+
+### How to use
+Here is how to use this model to classifiy an image based on 1 of 2 classes (normal,nsfw):
+
+```markdown
+
+# Use a pipeline as a high-level helper
+from PIL import Image
+from transformers import pipeline
+
+img = Image.open("<path_to_image_file>")
+classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
+classifier(img)
+
+```
+
+<hr>
+
+``` markdown
+
+# Load model directly
+import torch
+from PIL import Image
+from transformers import AutoModelForImageClassification, ViTImageProcessor
+
+img = Image.open("<path_to_image_file>")
+model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection")
+processor = ViTImageProcessor.from_pretrained('Falconsai/nsfw_image_detection')
+with torch.no_grad():
+    inputs = processor(images=img, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+predicted_label = logits.argmax(-1).item()
+model.config.id2label[predicted_label]
+
+```
+
+<hr>
+
+### Limitations
+- **Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks.
+- Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.
+
+## Training Data
+
+The model's training data includes a proprietary dataset comprising approximately 80,000 images. This dataset encompasses a significant amount of variability and consists of two distinct classes: "normal" and "nsfw." The training process on this data aimed to equip the model with the ability to distinguish between safe and explicit content effectively.
+
+### Training Stats
+``` markdown
+
+- 'eval_loss': 0.07463177293539047,
+- 'eval_accuracy': 0.980375, 
+- 'eval_runtime': 304.9846, 
+- 'eval_samples_per_second': 52.462, 
+- 'eval_steps_per_second': 3.279
+
+```
+
+<hr>
+
+
+**Note:** It's essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
+
+For more details on model fine-tuning and usage, please refer to the model's documentation and the model hub.
+
+## References
+
+- [Hugging Face Model Hub](https://huggingface.co/models)
+- [Vision Transformer (ViT) Paper](https://arxiv.org/abs/2010.11929)
+- [ImageNet-21k Dataset](http://www.image-net.org/)
+
+**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.

image-detection-nsfw-v1/config.json

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+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
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+  "id2label": {
+    "0": "normal",
+    "1": "nsfw"
+  },
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+  "initializer_range": 0.02,
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+  "label2id": {
+    "normal": "0",
+    "nsfw": "1"
+  },
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+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0"
+}

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image-detection-style-v1/README.md

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+---
+license: agpl-3.0
+---

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+    "butcher shop, meat market": 467,
+    "butternut squash": 942,
+    "cab, hack, taxi, taxicab": 468,
+    "cabbage butterfly": 324,
+    "cairn, cairn terrier": 192,
+    "caldron, cauldron": 469,
+    "can opener, tin opener": 473,
+    "candle, taper, wax light": 470,
+    "cannon": 471,
+    "canoe": 472,
+    "capuchin, ringtail, Cebus capucinus": 378,
+    "car mirror": 475,
+    "car wheel": 479,
+    "carbonara": 959,
+    "cardigan": 474,
+    "cardoon": 946,
+    "carousel, carrousel, merry-go-round, roundabout, whirligig": 476,
+    "carpenter's kit, tool kit": 477,
+    "carton": 478,
+    "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480,
+    "cassette": 481,
+    "cassette player": 482,
+    "castle": 483,
+    "catamaran": 484,
+    "cauliflower": 938,
+    "cello, violoncello": 486,
+    "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487,
+    "centipede": 79,
+    "chain": 488,
+    "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490,
+    "chain saw, chainsaw": 491,
+    "chainlink fence": 489,
+    "chambered nautilus, pearly nautilus, nautilus": 117,
+    "cheeseburger": 933,
+    "cheetah, chetah, Acinonyx jubatus": 293,
+    "chest": 492,
+    "chickadee": 19,
+    "chiffonier, commode": 493,
+    "chime, bell, gong": 494,
+    "chimpanzee, chimp, Pan troglodytes": 367,
+    "china cabinet, china closet": 495,
+    "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116,
+    "chocolate sauce, chocolate syrup": 960,
+    "chow, chow chow": 260,
+    "church, church building": 497,
+    "cicada, cicala": 316,
+    "cinema, movie theater, movie theatre, movie house, picture palace": 498,
+    "cleaver, meat cleaver, chopper": 499,
+    "cliff dwelling": 500,
+    "cliff, drop, drop-off": 972,
+    "cloak": 501,
+    "clog, geta, patten, sabot": 502,
+    "clumber, clumber spaniel": 216,
+    "cock": 7,
+    "cocker spaniel, English cocker spaniel, cocker": 219,
+    "cockroach, roach": 314,
+    "cocktail shaker": 503,
+    "coffee mug": 504,
+    "coffeepot": 505,
+    "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391,
+    "coil, spiral, volute, whorl, helix": 506,
+    "collie": 231,
+    "colobus, colobus monkey": 375,
+    "combination lock": 507,
+    "comic book": 917,
+    "common iguana, iguana, Iguana iguana": 39,
+    "common newt, Triturus vulgaris": 26,
+    "computer keyboard, keypad": 508,
+    "conch": 112,
+    "confectionery, confectionary, candy store": 509,
+    "consomme": 925,
+    "container ship, containership, container vessel": 510,
+    "convertible": 511,
+    "coral fungus": 991,
+    "coral reef": 973,
+    "corkscrew, bottle screw": 512,
+    "corn": 987,
+    "cornet, horn, trumpet, trump": 513,
+    "coucal": 91,
+    "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286,
+    "cowboy boot": 514,
+    "cowboy hat, ten-gallon hat": 515,
+    "coyote, prairie wolf, brush wolf, Canis latrans": 272,
+    "cradle": 516,
+    "crane": 517,
+    "crash helmet": 518,
+    "crate": 519,
+    "crayfish, crawfish, crawdad, crawdaddy": 124,
+    "crib, cot": 520,
+    "cricket": 312,
+    "croquet ball": 522,
+    "crossword puzzle, crossword": 918,
+    "crutch": 523,
+    "cucumber, cuke": 943,
+    "cuirass": 524,
+    "cup": 968,
+    "curly-coated retriever": 206,
+    "custard apple": 956,
+    "daisy": 985,
+    "dalmatian, coach dog, carriage dog": 251,
+    "dam, dike, dyke": 525,
+    "damselfly": 320,
+    "desk": 526,
+    "desktop computer": 527,
+    "dhole, Cuon alpinus": 274,
+    "dial telephone, dial phone": 528,
+    "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67,
+    "diaper, nappy, napkin": 529,
+    "digital clock": 530,
+    "digital watch": 531,
+    "dingo, warrigal, warragal, Canis dingo": 273,
+    "dining table, board": 532,
+    "dishrag, dishcloth": 533,
+    "dishwasher, dish washer, dishwashing machine": 534,
+    "disk brake, disc brake": 535,
+    "dock, dockage, docking facility": 536,
+    "dogsled, dog sled, dog sleigh": 537,
+    "dome": 538,
+    "doormat, welcome mat": 539,
+    "dough": 961,
+    "dowitcher": 142,
+    "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319,
+    "drake": 97,
+    "drilling platform, offshore rig": 540,
+    "drum, membranophone, tympan": 541,
+    "drumstick": 542,
+    "dugong, Dugong dugon": 149,
+    "dumbbell": 543,
+    "dung beetle": 305,
+    "ear, spike, capitulum": 998,
+    "earthstar": 995,
+    "echidna, spiny anteater, anteater": 102,
+    "eel": 390,
+    "eft": 27,
+    "eggnog": 969,
+    "electric fan, blower": 545,
+    "electric guitar": 546,
+    "electric locomotive": 547,
+    "electric ray, crampfish, numbfish, torpedo": 5,
+    "entertainment center": 548,
+    "envelope": 549,
+    "espresso": 967,
+    "espresso maker": 550,
+    "face powder": 551,
+    "feather boa, boa": 552,
+    "fiddler crab": 120,
+    "fig": 952,
+    "file, file cabinet, filing cabinet": 553,
+    "fire engine, fire truck": 555,
+    "fire screen, fireguard": 556,
+    "fireboat": 554,
+    "flagpole, flagstaff": 557,
+    "flamingo": 130,
+    "flat-coated retriever": 205,
+    "flatworm, platyhelminth": 110,
+    "flute, transverse flute": 558,
+    "fly": 308,
+    "folding chair": 559,
+    "football helmet": 560,
+    "forklift": 561,
+    "fountain": 562,
+    "fountain pen": 563,
+    "four-poster": 564,
+    "fox squirrel, eastern fox squirrel, Sciurus niger": 335,
+    "freight car": 565,
+    "frilled lizard, Chlamydosaurus kingi": 43,
+    "frying pan, frypan, skillet": 567,
+    "fur coat": 568,
+    "gar, garfish, garpike, billfish, Lepisosteus osseus": 395,
+    "garbage truck, dustcart": 569,
+    "garden spider, Aranea diademata": 74,
+    "garter snake, grass snake": 57,
+    "gas pump, gasoline pump, petrol pump, island dispenser": 571,
+    "gasmask, respirator, gas helmet": 570,
+    "gazelle": 353,
+    "geyser": 974,
+    "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388,
+    "giant schnauzer": 197,
+    "gibbon, Hylobates lar": 368,
+    "go-kart": 573,
+    "goblet": 572,
+    "golden retriever": 207,
+    "goldfinch, Carduelis carduelis": 11,
+    "goldfish, Carassius auratus": 1,
+    "golf ball": 574,
+    "golfcart, golf cart": 575,
+    "gondola": 576,
+    "gong, tam-tam": 577,
+    "goose": 99,
+    "gorilla, Gorilla gorilla": 366,
+    "gown": 578,
+    "grand piano, grand": 579,
+    "grasshopper, hopper": 311,
+    "great grey owl, great gray owl, Strix nebulosa": 24,
+    "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2,
+    "green lizard, Lacerta viridis": 46,
+    "green mamba": 64,
+    "green snake, grass snake": 55,
+    "greenhouse, nursery, glasshouse": 580,
+    "grey fox, gray fox, Urocyon cinereoargenteus": 280,
+    "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147,
+    "grille, radiator grille": 581,
+    "grocery store, grocery, food market, market": 582,
+    "groenendael": 224,
+    "groom, bridegroom": 982,
+    "ground beetle, carabid beetle": 302,
+    "guacamole": 924,
+    "guenon, guenon monkey": 370,
+    "guillotine": 583,
+    "guinea pig, Cavia cobaya": 338,
+    "gyromitra": 993,
+    "hair slide": 584,
+    "hair spray": 585,
+    "half track": 586,
+    "hammer": 587,
+    "hammerhead, hammerhead shark": 4,
+    "hamper": 588,
+    "hamster": 333,
+    "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589,
+    "hand-held computer, hand-held microcomputer": 590,
+    "handkerchief, hankie, hanky, hankey": 591,
+    "hard disc, hard disk, fixed disk": 592,
+    "hare": 331,
+    "harmonica, mouth organ, harp, mouth harp": 593,
+    "harp": 594,
+    "hartebeest": 351,
+    "harvester, reaper": 595,
+    "harvestman, daddy longlegs, Phalangium opilio": 70,
+    "hatchet": 596,
+    "hay": 958,
+    "head cabbage": 936,
+    "hen": 8,
+    "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996,
+    "hermit crab": 125,
+    "hip, rose hip, rosehip": 989,
+    "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344,
+    "hog, pig, grunter, squealer, Sus scrofa": 341,
+    "hognose snake, puff adder, sand viper": 54,
+    "holster": 597,
+    "home theater, home theatre": 598,
+    "honeycomb": 599,
+    "hook, claw": 600,
+    "hoopskirt, crinoline": 601,
+    "horizontal bar, high bar": 602,
+    "hornbill": 93,
+    "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66,
+    "horse cart, horse-cart": 603,
+    "hot pot, hotpot": 926,
+    "hotdog, hot dog, red hot": 934,
+    "hourglass": 604,
+    "house finch, linnet, Carpodacus mexicanus": 12,
+    "howler monkey, howler": 379,
+    "hummingbird": 94,
+    "hyena, hyaena": 276,
+    "iPod": 605,
+    "ibex, Capra ibex": 350,
+    "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296,
+    "ice cream, icecream": 928,
+    "ice lolly, lolly, lollipop, popsicle": 929,
+    "impala, Aepyceros melampus": 352,
+    "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14,
+    "indri, indris, Indri indri, Indri brevicaudatus": 384,
+    "iron, smoothing iron": 606,
+    "isopod": 126,
+    "jacamar": 95,
+    "jack-o'-lantern": 607,
+    "jackfruit, jak, jack": 955,
+    "jaguar, panther, Panthera onca, Felis onca": 290,
+    "jay": 17,
+    "jean, blue jean, denim": 608,
+    "jeep, landrover": 609,
+    "jellyfish": 107,
+    "jersey, T-shirt, tee shirt": 610,
+    "jigsaw puzzle": 611,
+    "jinrikisha, ricksha, rickshaw": 612,
+    "joystick": 613,
+    "junco, snowbird": 13,
+    "keeshond": 261,
+    "kelpie": 227,
+    "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148,
+    "kimono": 614,
+    "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121,
+    "king penguin, Aptenodytes patagonica": 145,
+    "king snake, kingsnake": 56,
+    "kit fox, Vulpes macrotis": 278,
+    "kite": 21,
+    "knee pad": 615,
+    "knot": 616,
+    "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105,
+    "komondor": 228,
+    "kuvasz": 222,
+    "lab coat, laboratory coat": 617,
+    "lacewing, lacewing fly": 318,
+    "ladle": 618,
+    "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301,
+    "lakeside, lakeshore": 975,
+    "lampshade, lamp shade": 619,
+    "langur": 374,
+    "laptop, laptop computer": 620,
+    "lawn mower, mower": 621,
+    "leaf beetle, chrysomelid": 304,
+    "leafhopper": 317,
+    "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34,
+    "lemon": 951,
+    "lens cap, lens cover": 622,
+    "leopard, Panthera pardus": 288,
+    "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387,
+    "letter opener, paper knife, paperknife": 623,
+    "library": 624,
+    "lifeboat": 625,
+    "lighter, light, igniter, ignitor": 626,
+    "limousine, limo": 627,
+    "limpkin, Aramus pictus": 135,
+    "liner, ocean liner": 628,
+    "lion, king of beasts, Panthera leo": 291,
+    "lionfish": 396,
+    "lipstick, lip rouge": 629,
+    "little blue heron, Egretta caerulea": 131,
+    "llama": 355,
+    "loggerhead, loggerhead turtle, Caretta caretta": 33,
+    "long-horned beetle, longicorn, longicorn beetle": 303,
+    "lorikeet": 90,
+    "lotion": 631,
+    "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632,
+    "loupe, jeweler's loupe": 633,
+    "lumbermill, sawmill": 634,
+    "lycaenid, lycaenid butterfly": 326,
+    "lynx, catamount": 287,
+    "macaque": 373,
+    "macaw": 88,
+    "magnetic compass": 635,
+    "magpie": 18,
+    "mailbag, postbag": 636,
+    "mailbox, letter box": 637,
+    "maillot": 638,
+    "maillot, tank suit": 639,
+    "malamute, malemute, Alaskan malamute": 249,
+    "malinois": 225,
+    "manhole cover": 640,
+    "mantis, mantid": 315,
+    "maraca": 641,
+    "marimba, xylophone": 642,
+    "marmoset": 377,
+    "marmot": 336,
+    "mashed potato": 935,
+    "mask": 643,
+    "matchstick": 644,
+    "maypole": 645,
+    "maze, labyrinth": 646,
+    "measuring cup": 647,
+    "meat loaf, meatloaf": 962,
+    "medicine chest, medicine cabinet": 648,
+    "meerkat, mierkat": 299,
+    "megalith, megalithic structure": 649,
+    "menu": 922,
+    "microphone, mike": 650,
+    "microwave, microwave oven": 651,
+    "military uniform": 652,
+    "milk can": 653,
+    "miniature pinscher": 237,
+    "miniature poodle": 266,
+    "miniature schnauzer": 196,
+    "minibus": 654,
+    "miniskirt, mini": 655,
+    "minivan": 656,
+    "mink": 357,
+    "missile": 657,
+    "mitten": 658,
+    "mixing bowl": 659,
+    "mobile home, manufactured home": 660,
+    "modem": 662,
+    "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323,
+    "monastery": 663,
+    "mongoose": 298,
+    "monitor": 664,
+    "moped": 665,
+    "mortar": 666,
+    "mortarboard": 667,
+    "mosque": 668,
+    "mosquito net": 669,
+    "motor scooter, scooter": 670,
+    "mountain bike, all-terrain bike, off-roader": 671,
+    "mountain tent": 672,
+    "mouse, computer mouse": 673,
+    "mousetrap": 674,
+    "moving van": 675,
+    "mud turtle": 35,
+    "mushroom": 947,
+    "muzzle": 676,
+    "nail": 677,
+    "neck brace": 678,
+    "necklace": 679,
+    "nematode, nematode worm, roundworm": 111,
+    "night snake, Hypsiglena torquata": 60,
+    "nipple": 680,
+    "notebook, notebook computer": 681,
+    "obelisk": 682,
+    "oboe, hautboy, hautbois": 683,
+    "ocarina, sweet potato": 684,
+    "odometer, hodometer, mileometer, milometer": 685,
+    "oil filter": 686,
+    "orange": 950,
+    "orangutan, orang, orangutang, Pongo pygmaeus": 365,
+    "organ, pipe organ": 687,
+    "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688,
+    "ostrich, Struthio camelus": 9,
+    "otter": 360,
+    "otterhound, otter hound": 175,
+    "overskirt": 689,
+    "ox": 345,
+    "oxcart": 690,
+    "oxygen mask": 691,
+    "oystercatcher, oyster catcher": 143,
+    "packet": 692,
+    "paddle, boat paddle": 693,
+    "paddlewheel, paddle wheel": 694,
+    "padlock": 695,
+    "paintbrush": 696,
+    "pajama, pyjama, pj's, jammies": 697,
+    "palace": 698,
+    "panpipe, pandean pipe, syrinx": 699,
+    "paper towel": 700,
+    "papillon": 157,
+    "parachute, chute": 701,
+    "parallel bars, bars": 702,
+    "park bench": 703,
+    "parking meter": 704,
+    "partridge": 86,
+    "passenger car, coach, carriage": 705,
+    "patas, hussar monkey, Erythrocebus patas": 371,
+    "patio, terrace": 706,
+    "pay-phone, pay-station": 707,
+    "peacock": 84,
+    "pedestal, plinth, footstall": 708,
+    "pelican": 144,
+    "pencil box, pencil case": 709,
+    "pencil sharpener": 710,
+    "perfume, essence": 711,
+    "photocopier": 713,
+    "pick, plectrum, plectron": 714,
+    "pickelhaube": 715,
+    "picket fence, paling": 716,
+    "pickup, pickup truck": 717,
+    "pier": 718,
+    "piggy bank, penny bank": 719,
+    "pill bottle": 720,
+    "pillow": 721,
+    "pineapple, ananas": 953,
+    "ping-pong ball": 722,
+    "pinwheel": 723,
+    "pirate, pirate ship": 724,
+    "pitcher, ewer": 725,
+    "pizza, pizza pie": 963,
+    "plane, carpenter's plane, woodworking plane": 726,
+    "planetarium": 727,
+    "plastic bag": 728,
+    "plate": 923,
+    "plate rack": 729,
+    "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103,
+    "plow, plough": 730,
+    "plunger, plumber's helper": 731,
+    "pole": 733,
+    "polecat, fitch, foulmart, foumart, Mustela putorius": 358,
+    "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734,
+    "pomegranate": 957,
+    "poncho": 735,
+    "pool table, billiard table, snooker table": 736,
+    "pop bottle, soda bottle": 737,
+    "porcupine, hedgehog": 334,
+    "pot, flowerpot": 738,
+    "potpie": 964,
+    "potter's wheel": 739,
+    "power drill": 740,
+    "prairie chicken, prairie grouse, prairie fowl": 83,
+    "prayer rug, prayer mat": 741,
+    "pretzel": 932,
+    "printer": 742,
+    "prison, prison house": 743,
+    "proboscis monkey, Nasalis larvatus": 376,
+    "projectile, missile": 744,
+    "projector": 745,
+    "promontory, headland, head, foreland": 976,
+    "ptarmigan": 81,
+    "puck, hockey puck": 746,
+    "puffer, pufferfish, blowfish, globefish": 397,
+    "pug, pug-dog": 254,
+    "punching bag, punch bag, punching ball, punchball": 747,
+    "purse": 748,
+    "quail": 85,
+    "quill, quill pen": 749,
+    "quilt, comforter, comfort, puff": 750,
+    "racer, race car, racing car": 751,
+    "racket, racquet": 752,
+    "radiator": 753,
+    "radio telescope, radio reflector": 755,
+    "radio, wireless": 754,
+    "rain barrel": 756,
+    "ram, tup": 348,
+    "rapeseed": 984,
+    "recreational vehicle, RV, R.V.": 757,
+    "red fox, Vulpes vulpes": 277,
+    "red wine": 966,
+    "red wolf, maned wolf, Canis rufus, Canis niger": 271,
+    "red-backed sandpiper, dunlin, Erolia alpina": 140,
+    "red-breasted merganser, Mergus serrator": 98,
+    "redbone": 168,
+    "redshank, Tringa totanus": 141,
+    "reel": 758,
+    "reflex camera": 759,
+    "refrigerator, icebox": 760,
+    "remote control, remote": 761,
+    "restaurant, eating house, eating place, eatery": 762,
+    "revolver, six-gun, six-shooter": 763,
+    "rhinoceros beetle": 306,
+    "rifle": 764,
+    "ringlet, ringlet butterfly": 322,
+    "ringneck snake, ring-necked snake, ring snake": 53,
+    "robin, American robin, Turdus migratorius": 15,
+    "rock beauty, Holocanthus tricolor": 392,
+    "rock crab, Cancer irroratus": 119,
+    "rock python, rock snake, Python sebae": 62,
+    "rocking chair, rocker": 765,
+    "rotisserie": 766,
+    "rubber eraser, rubber, pencil eraser": 767,
+    "ruddy turnstone, Arenaria interpres": 139,
+    "ruffed grouse, partridge, Bonasa umbellus": 82,
+    "rugby ball": 768,
+    "rule, ruler": 769,
+    "running shoe": 770,
+    "safe": 771,
+    "safety pin": 772,
+    "saltshaker, salt shaker": 773,
+    "sandal": 774,
+    "sandbar, sand bar": 977,
+    "sarong": 775,
+    "sax, saxophone": 776,
+    "scabbard": 777,
+    "scale, weighing machine": 778,
+    "schipperke": 223,
+    "school bus": 779,
+    "schooner": 780,
+    "scoreboard": 781,
+    "scorpion": 71,
+    "screen, CRT screen": 782,
+    "screw": 783,
+    "screwdriver": 784,
+    "scuba diver": 983,
+    "sea anemone, anemone": 108,
+    "sea cucumber, holothurian": 329,
+    "sea lion": 150,
+    "sea slug, nudibranch": 115,
+    "sea snake": 65,
+    "sea urchin": 328,
+    "seashore, coast, seacoast, sea-coast": 978,
+    "seat belt, seatbelt": 785,
+    "sewing machine": 786,
+    "shield, buckler": 787,
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image-detection-waifu-v1/README.md

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+---

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+    "French horn, horn": 566,
+    "French loaf": 930,
+    "German shepherd, German shepherd dog, German police dog, alsatian": 235,
+    "German short-haired pointer": 210,
+    "Gila monster, Heloderma suspectum": 45,
+    "Gordon setter": 214,
+    "Granny Smith": 948,
+    "Great Dane": 246,
+    "Great Pyrenees": 257,
+    "Greater Swiss Mountain dog": 238,
+    "Ibizan hound, Ibizan Podenco": 173,
+    "Indian cobra, Naja naja": 63,
+    "Indian elephant, Elephas maximus": 385,
+    "Irish setter, red setter": 213,
+    "Irish terrier": 184,
+    "Irish water spaniel": 221,
+    "Irish wolfhound": 170,
+    "Italian greyhound": 171,
+    "Japanese spaniel": 152,
+    "Kerry blue terrier": 183,
+    "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48,
+    "Labrador retriever": 208,
+    "Lakeland terrier": 189,
+    "Leonberg": 255,
+    "Lhasa, Lhasa apso": 204,
+    "Loafer": 630,
+    "Madagascar cat, ring-tailed lemur, Lemur catta": 383,
+    "Maltese dog, Maltese terrier, Maltese": 153,
+    "Mexican hairless": 268,
+    "Model T": 661,
+    "Newfoundland, Newfoundland dog": 256,
+    "Norfolk terrier": 185,
+    "Norwegian elkhound, elkhound": 174,
+    "Norwich terrier": 186,
+    "Old English sheepdog, bobtail": 229,
+    "Pekinese, Pekingese, Peke": 154,
+    "Pembroke, Pembroke Welsh corgi": 263,
+    "Persian cat": 283,
+    "Petri dish": 712,
+    "Polaroid camera, Polaroid Land camera": 732,
+    "Pomeranian": 259,
+    "Rhodesian ridgeback": 159,
+    "Rottweiler": 234,
+    "Saint Bernard, St Bernard": 247,
+    "Saluki, gazelle hound": 176,
+    "Samoyed, Samoyede": 258,
+    "Scotch terrier, Scottish terrier, Scottie": 199,
+    "Scottish deerhound, deerhound": 177,
+    "Sealyham terrier, Sealyham": 190,
+    "Shetland sheepdog, Shetland sheep dog, Shetland": 230,
+    "Shih-Tzu": 155,
+    "Siamese cat, Siamese": 284,
+    "Siberian husky": 250,
+    "Staffordshire bullterrier, Staffordshire bull terrier": 179,
+    "Sussex spaniel": 220,
+    "Tibetan mastiff": 244,
+    "Tibetan terrier, chrysanthemum dog": 200,
+    "Walker hound, Walker foxhound": 166,
+    "Weimaraner": 178,
+    "Welsh springer spaniel": 218,
+    "West Highland white terrier": 203,
+    "Windsor tie": 906,
+    "Yorkshire terrier": 187,
+    "abacus": 398,
+    "abaya": 399,
+    "academic gown, academic robe, judge's robe": 400,
+    "accordion, piano accordion, squeeze box": 401,
+    "acorn": 988,
+    "acorn squash": 941,
+    "acoustic guitar": 402,
+    "admiral": 321,
+    "affenpinscher, monkey pinscher, monkey dog": 252,
+    "agama": 42,
+    "agaric": 992,
+    "aircraft carrier, carrier, flattop, attack aircraft carrier": 403,
+    "airliner": 404,
+    "airship, dirigible": 405,
+    "albatross, mollymawk": 146,
+    "alligator lizard": 44,
+    "alp": 970,
+    "altar": 406,
+    "ambulance": 407,
+    "amphibian, amphibious vehicle": 408,
+    "analog clock": 409,
+    "anemone fish": 393,
+    "ant, emmet, pismire": 310,
+    "apiary, bee house": 410,
+    "apron": 411,
+    "armadillo": 363,
+    "artichoke, globe artichoke": 944,
+    "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412,
+    "assault rifle, assault gun": 413,
+    "axolotl, mud puppy, Ambystoma mexicanum": 29,
+    "baboon": 372,
+    "backpack, back pack, knapsack, packsack, rucksack, haversack": 414,
+    "badger": 362,
+    "bagel, beigel": 931,
+    "bakery, bakeshop, bakehouse": 415,
+    "balance beam, beam": 416,
+    "bald eagle, American eagle, Haliaeetus leucocephalus": 22,
+    "balloon": 417,
+    "ballplayer, baseball player": 981,
+    "ballpoint, ballpoint pen, ballpen, Biro": 418,
+    "banana": 954,
+    "banded gecko": 38,
+    "banjo": 420,
+    "bannister, banister, balustrade, balusters, handrail": 421,
+    "barbell": 422,
+    "barber chair": 423,
+    "barbershop": 424,
+    "barn": 425,
+    "barn spider, Araneus cavaticus": 73,
+    "barometer": 426,
+    "barracouta, snoek": 389,
+    "barrel, cask": 427,
+    "barrow, garden cart, lawn cart, wheelbarrow": 428,
+    "baseball": 429,
+    "basenji": 253,
+    "basketball": 430,
+    "basset, basset hound": 161,
+    "bassinet": 431,
+    "bassoon": 432,
+    "bath towel": 434,
+    "bathing cap, swimming cap": 433,
+    "bathtub, bathing tub, bath, tub": 435,
+    "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436,
+    "beacon, lighthouse, beacon light, pharos": 437,
+    "beagle": 162,
+    "beaker": 438,
+    "bearskin, busby, shako": 439,
+    "beaver": 337,
+    "bee": 309,
+    "bee eater": 92,
+    "beer bottle": 440,
+    "beer glass": 441,
+    "bell cote, bell cot": 442,
+    "bell pepper": 945,
+    "bib": 443,
+    "bicycle-built-for-two, tandem bicycle, tandem": 444,
+    "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349,
+    "bikini, two-piece": 445,
+    "binder, ring-binder": 446,
+    "binoculars, field glasses, opera glasses": 447,
+    "birdhouse": 448,
+    "bison": 347,
+    "bittern": 133,
+    "black and gold garden spider, Argiope aurantia": 72,
+    "black grouse": 80,
+    "black stork, Ciconia nigra": 128,
+    "black swan, Cygnus atratus": 100,
+    "black widow, Latrodectus mactans": 75,
+    "black-and-tan coonhound": 165,
+    "black-footed ferret, ferret, Mustela nigripes": 359,
+    "bloodhound, sleuthhound": 163,
+    "bluetick": 164,
+    "boa constrictor, Constrictor constrictor": 61,
+    "boathouse": 449,
+    "bobsled, bobsleigh, bob": 450,
+    "bolete": 997,
+    "bolo tie, bolo, bola tie, bola": 451,
+    "bonnet, poke bonnet": 452,
+    "book jacket, dust cover, dust jacket, dust wrapper": 921,
+    "bookcase": 453,
+    "bookshop, bookstore, bookstall": 454,
+    "borzoi, Russian wolfhound": 169,
+    "bottlecap": 455,
+    "bow": 456,
+    "bow tie, bow-tie, bowtie": 457,
+    "box turtle, box tortoise": 37,
+    "boxer": 242,
+    "brain coral": 109,
+    "brambling, Fringilla montifringilla": 10,
+    "brass, memorial tablet, plaque": 458,
+    "brassiere, bra, bandeau": 459,
+    "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460,
+    "breastplate, aegis, egis": 461,
+    "briard": 226,
+    "broccoli": 937,
+    "broom": 462,
+    "brown bear, bruin, Ursus arctos": 294,
+    "bubble": 971,
+    "bucket, pail": 463,
+    "buckeye, horse chestnut, conker": 990,
+    "buckle": 464,
+    "bulbul": 16,
+    "bull mastiff": 243,
+    "bullet train, bullet": 466,
+    "bulletproof vest": 465,
+    "bullfrog, Rana catesbeiana": 30,
+    "burrito": 965,
+    "bustard": 138,
+    "butcher shop, meat market": 467,
+    "butternut squash": 942,
+    "cab, hack, taxi, taxicab": 468,
+    "cabbage butterfly": 324,
+    "cairn, cairn terrier": 192,
+    "caldron, cauldron": 469,
+    "can opener, tin opener": 473,
+    "candle, taper, wax light": 470,
+    "cannon": 471,
+    "canoe": 472,
+    "capuchin, ringtail, Cebus capucinus": 378,
+    "car mirror": 475,
+    "car wheel": 479,
+    "carbonara": 959,
+    "cardigan": 474,
+    "cardoon": 946,
+    "carousel, carrousel, merry-go-round, roundabout, whirligig": 476,
+    "carpenter's kit, tool kit": 477,
+    "carton": 478,
+    "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480,
+    "cassette": 481,
+    "cassette player": 482,
+    "castle": 483,
+    "catamaran": 484,
+    "cauliflower": 938,
+    "cello, violoncello": 486,
+    "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487,
+    "centipede": 79,
+    "chain": 488,
+    "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490,
+    "chain saw, chainsaw": 491,
+    "chainlink fence": 489,
+    "chambered nautilus, pearly nautilus, nautilus": 117,
+    "cheeseburger": 933,
+    "cheetah, chetah, Acinonyx jubatus": 293,
+    "chest": 492,
+    "chickadee": 19,
+    "chiffonier, commode": 493,
+    "chime, bell, gong": 494,
+    "chimpanzee, chimp, Pan troglodytes": 367,
+    "china cabinet, china closet": 495,
+    "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116,
+    "chocolate sauce, chocolate syrup": 960,
+    "chow, chow chow": 260,
+    "church, church building": 497,
+    "cicada, cicala": 316,
+    "cinema, movie theater, movie theatre, movie house, picture palace": 498,
+    "cleaver, meat cleaver, chopper": 499,
+    "cliff dwelling": 500,
+    "cliff, drop, drop-off": 972,
+    "cloak": 501,
+    "clog, geta, patten, sabot": 502,
+    "clumber, clumber spaniel": 216,
+    "cock": 7,
+    "cocker spaniel, English cocker spaniel, cocker": 219,
+    "cockroach, roach": 314,
+    "cocktail shaker": 503,
+    "coffee mug": 504,
+    "coffeepot": 505,
+    "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391,
+    "coil, spiral, volute, whorl, helix": 506,
+    "collie": 231,
+    "colobus, colobus monkey": 375,
+    "combination lock": 507,
+    "comic book": 917,
+    "common iguana, iguana, Iguana iguana": 39,
+    "common newt, Triturus vulgaris": 26,
+    "computer keyboard, keypad": 508,
+    "conch": 112,
+    "confectionery, confectionary, candy store": 509,
+    "consomme": 925,
+    "container ship, containership, container vessel": 510,
+    "convertible": 511,
+    "coral fungus": 991,
+    "coral reef": 973,
+    "corkscrew, bottle screw": 512,
+    "corn": 987,
+    "cornet, horn, trumpet, trump": 513,
+    "coucal": 91,
+    "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286,
+    "cowboy boot": 514,
+    "cowboy hat, ten-gallon hat": 515,
+    "coyote, prairie wolf, brush wolf, Canis latrans": 272,
+    "cradle": 516,
+    "crane": 517,
+    "crash helmet": 518,
+    "crate": 519,
+    "crayfish, crawfish, crawdad, crawdaddy": 124,
+    "crib, cot": 520,
+    "cricket": 312,
+    "croquet ball": 522,
+    "crossword puzzle, crossword": 918,
+    "crutch": 523,
+    "cucumber, cuke": 943,
+    "cuirass": 524,
+    "cup": 968,
+    "curly-coated retriever": 206,
+    "custard apple": 956,
+    "daisy": 985,
+    "dalmatian, coach dog, carriage dog": 251,
+    "dam, dike, dyke": 525,
+    "damselfly": 320,
+    "desk": 526,
+    "desktop computer": 527,
+    "dhole, Cuon alpinus": 274,
+    "dial telephone, dial phone": 528,
+    "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67,
+    "diaper, nappy, napkin": 529,
+    "digital clock": 530,
+    "digital watch": 531,
+    "dingo, warrigal, warragal, Canis dingo": 273,
+    "dining table, board": 532,
+    "dishrag, dishcloth": 533,
+    "dishwasher, dish washer, dishwashing machine": 534,
+    "disk brake, disc brake": 535,
+    "dock, dockage, docking facility": 536,
+    "dogsled, dog sled, dog sleigh": 537,
+    "dome": 538,
+    "doormat, welcome mat": 539,
+    "dough": 961,
+    "dowitcher": 142,
+    "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319,
+    "drake": 97,
+    "drilling platform, offshore rig": 540,
+    "drum, membranophone, tympan": 541,
+    "drumstick": 542,
+    "dugong, Dugong dugon": 149,
+    "dumbbell": 543,
+    "dung beetle": 305,
+    "ear, spike, capitulum": 998,
+    "earthstar": 995,
+    "echidna, spiny anteater, anteater": 102,
+    "eel": 390,
+    "eft": 27,
+    "eggnog": 969,
+    "electric fan, blower": 545,
+    "electric guitar": 546,
+    "electric locomotive": 547,
+    "electric ray, crampfish, numbfish, torpedo": 5,
+    "entertainment center": 548,
+    "envelope": 549,
+    "espresso": 967,
+    "espresso maker": 550,
+    "face powder": 551,
+    "feather boa, boa": 552,
+    "fiddler crab": 120,
+    "fig": 952,
+    "file, file cabinet, filing cabinet": 553,
+    "fire engine, fire truck": 555,
+    "fire screen, fireguard": 556,
+    "fireboat": 554,
+    "flagpole, flagstaff": 557,
+    "flamingo": 130,
+    "flat-coated retriever": 205,
+    "flatworm, platyhelminth": 110,
+    "flute, transverse flute": 558,
+    "fly": 308,
+    "folding chair": 559,
+    "football helmet": 560,
+    "forklift": 561,
+    "fountain": 562,
+    "fountain pen": 563,
+    "four-poster": 564,
+    "fox squirrel, eastern fox squirrel, Sciurus niger": 335,
+    "freight car": 565,
+    "frilled lizard, Chlamydosaurus kingi": 43,
+    "frying pan, frypan, skillet": 567,
+    "fur coat": 568,
+    "gar, garfish, garpike, billfish, Lepisosteus osseus": 395,
+    "garbage truck, dustcart": 569,
+    "garden spider, Aranea diademata": 74,
+    "garter snake, grass snake": 57,
+    "gas pump, gasoline pump, petrol pump, island dispenser": 571,
+    "gasmask, respirator, gas helmet": 570,
+    "gazelle": 353,
+    "geyser": 974,
+    "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388,
+    "giant schnauzer": 197,
+    "gibbon, Hylobates lar": 368,
+    "go-kart": 573,
+    "goblet": 572,
+    "golden retriever": 207,
+    "goldfinch, Carduelis carduelis": 11,
+    "goldfish, Carassius auratus": 1,
+    "golf ball": 574,
+    "golfcart, golf cart": 575,
+    "gondola": 576,
+    "gong, tam-tam": 577,
+    "goose": 99,
+    "gorilla, Gorilla gorilla": 366,
+    "gown": 578,
+    "grand piano, grand": 579,
+    "grasshopper, hopper": 311,
+    "great grey owl, great gray owl, Strix nebulosa": 24,
+    "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2,
+    "green lizard, Lacerta viridis": 46,
+    "green mamba": 64,
+    "green snake, grass snake": 55,
+    "greenhouse, nursery, glasshouse": 580,
+    "grey fox, gray fox, Urocyon cinereoargenteus": 280,
+    "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147,
+    "grille, radiator grille": 581,
+    "grocery store, grocery, food market, market": 582,
+    "groenendael": 224,
+    "groom, bridegroom": 982,
+    "ground beetle, carabid beetle": 302,
+    "guacamole": 924,
+    "guenon, guenon monkey": 370,
+    "guillotine": 583,
+    "guinea pig, Cavia cobaya": 338,
+    "gyromitra": 993,
+    "hair slide": 584,
+    "hair spray": 585,
+    "half track": 586,
+    "hammer": 587,
+    "hammerhead, hammerhead shark": 4,
+    "hamper": 588,
+    "hamster": 333,
+    "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589,
+    "hand-held computer, hand-held microcomputer": 590,
+    "handkerchief, hankie, hanky, hankey": 591,
+    "hard disc, hard disk, fixed disk": 592,
+    "hare": 331,
+    "harmonica, mouth organ, harp, mouth harp": 593,
+    "harp": 594,
+    "hartebeest": 351,
+    "harvester, reaper": 595,
+    "harvestman, daddy longlegs, Phalangium opilio": 70,
+    "hatchet": 596,
+    "hay": 958,
+    "head cabbage": 936,
+    "hen": 8,
+    "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996,
+    "hermit crab": 125,
+    "hip, rose hip, rosehip": 989,
+    "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344,
+    "hog, pig, grunter, squealer, Sus scrofa": 341,
+    "hognose snake, puff adder, sand viper": 54,
+    "holster": 597,
+    "home theater, home theatre": 598,
+    "honeycomb": 599,
+    "hook, claw": 600,
+    "hoopskirt, crinoline": 601,
+    "horizontal bar, high bar": 602,
+    "hornbill": 93,
+    "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66,
+    "horse cart, horse-cart": 603,
+    "hot pot, hotpot": 926,
+    "hotdog, hot dog, red hot": 934,
+    "hourglass": 604,
+    "house finch, linnet, Carpodacus mexicanus": 12,
+    "howler monkey, howler": 379,
+    "hummingbird": 94,
+    "hyena, hyaena": 276,
+    "iPod": 605,
+    "ibex, Capra ibex": 350,
+    "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296,
+    "ice cream, icecream": 928,
+    "ice lolly, lolly, lollipop, popsicle": 929,
+    "impala, Aepyceros melampus": 352,
+    "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14,
+    "indri, indris, Indri indri, Indri brevicaudatus": 384,
+    "iron, smoothing iron": 606,
+    "isopod": 126,
+    "jacamar": 95,
+    "jack-o'-lantern": 607,
+    "jackfruit, jak, jack": 955,
+    "jaguar, panther, Panthera onca, Felis onca": 290,
+    "jay": 17,
+    "jean, blue jean, denim": 608,
+    "jeep, landrover": 609,
+    "jellyfish": 107,
+    "jersey, T-shirt, tee shirt": 610,
+    "jigsaw puzzle": 611,
+    "jinrikisha, ricksha, rickshaw": 612,
+    "joystick": 613,
+    "junco, snowbird": 13,
+    "keeshond": 261,
+    "kelpie": 227,
+    "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148,
+    "kimono": 614,
+    "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121,
+    "king penguin, Aptenodytes patagonica": 145,
+    "king snake, kingsnake": 56,
+    "kit fox, Vulpes macrotis": 278,
+    "kite": 21,
+    "knee pad": 615,
+    "knot": 616,
+    "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105,
+    "komondor": 228,
+    "kuvasz": 222,
+    "lab coat, laboratory coat": 617,
+    "lacewing, lacewing fly": 318,
+    "ladle": 618,
+    "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301,
+    "lakeside, lakeshore": 975,
+    "lampshade, lamp shade": 619,
+    "langur": 374,
+    "laptop, laptop computer": 620,
+    "lawn mower, mower": 621,
+    "leaf beetle, chrysomelid": 304,
+    "leafhopper": 317,
+    "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34,
+    "lemon": 951,
+    "lens cap, lens cover": 622,
+    "leopard, Panthera pardus": 288,
+    "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387,
+    "letter opener, paper knife, paperknife": 623,
+    "library": 624,
+    "lifeboat": 625,
+    "lighter, light, igniter, ignitor": 626,
+    "limousine, limo": 627,
+    "limpkin, Aramus pictus": 135,
+    "liner, ocean liner": 628,
+    "lion, king of beasts, Panthera leo": 291,
+    "lionfish": 396,
+    "lipstick, lip rouge": 629,
+    "little blue heron, Egretta caerulea": 131,
+    "llama": 355,
+    "loggerhead, loggerhead turtle, Caretta caretta": 33,
+    "long-horned beetle, longicorn, longicorn beetle": 303,
+    "lorikeet": 90,
+    "lotion": 631,
+    "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632,
+    "loupe, jeweler's loupe": 633,
+    "lumbermill, sawmill": 634,
+    "lycaenid, lycaenid butterfly": 326,
+    "lynx, catamount": 287,
+    "macaque": 373,
+    "macaw": 88,
+    "magnetic compass": 635,
+    "magpie": 18,
+    "mailbag, postbag": 636,
+    "mailbox, letter box": 637,
+    "maillot": 638,
+    "maillot, tank suit": 639,
+    "malamute, malemute, Alaskan malamute": 249,
+    "malinois": 225,
+    "manhole cover": 640,
+    "mantis, mantid": 315,
+    "maraca": 641,
+    "marimba, xylophone": 642,
+    "marmoset": 377,
+    "marmot": 336,
+    "mashed potato": 935,
+    "mask": 643,
+    "matchstick": 644,
+    "maypole": 645,
+    "maze, labyrinth": 646,
+    "measuring cup": 647,
+    "meat loaf, meatloaf": 962,
+    "medicine chest, medicine cabinet": 648,
+    "meerkat, mierkat": 299,
+    "megalith, megalithic structure": 649,
+    "menu": 922,
+    "microphone, mike": 650,
+    "microwave, microwave oven": 651,
+    "military uniform": 652,
+    "milk can": 653,
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+    "miniature poodle": 266,
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