diff --git a/vcoder_llava/eval/model_batched_inference.py b/vcoder_llava/eval/model_batched_inference.py
new file mode 100644
index 0000000..905fba3
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+++ b/vcoder_llava/eval/model_batched_inference.py
@@ -0,0 +1,350 @@
+import argparse
+import glob
+import math
+import random
+import os
+from typing import List, Optional, Union
+
+import torch
+from PIL import Image
+from tqdm import tqdm
+from torch.utils.data import Dataset, DataLoader
+
+from vcoder_llava.constants import (
+    DEFAULT_DEPTH_TOKEN,
+    DEFAULT_IMAGE_TOKEN,
+    DEFAULT_SEG_TOKEN,
+    DEPTH_TOKEN_INDEX,
+    IMAGE_TOKEN_INDEX,
+    SEG_TOKEN_INDEX,
+)
+from vcoder_llava.mm_utils import (
+    get_model_name_from_path,
+    process_images,
+    tokenizer_depth_seg_token,
+    tokenizer_seg_token,
+    tokenizer_image_token,
+)
+from vcoder_llava.model.builder import load_pretrained_model
+from vcoder_llava.utils import disable_torch_init
+from vcoder_llava.vcoder_conversation import conv_templates, SeparatorStyle
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+class VCoderDataset(Dataset):
+    def __init__(
+        self,
+        prompt: Optional[Union[str, List[str]]],
+        tokenizer,
+        image_dir,
+        image_processor,
+        seg_image_dir: Optional[str],
+        seg_image_processor,
+        depth_image_dir: Optional[str],
+        depth_image_processor,
+        model_config,
+        num_chunks: Optional[int] = None,
+        chunk_idx: Optional[int] = None,
+        conv_mode: str = "llava_v1",
+        ext: str =".jpg",
+    ):
+        self.tokenizer = tokenizer
+        self.image_processor = image_processor
+        self.seg_image_processor = seg_image_processor
+        self.depth_image_processor = depth_image_processor
+        self.model_config = model_config
+        self.conv_mode = conv_mode
+
+        if prompt is None:
+            self.prompts = ["What objects can be seen in the image?"]
+        elif isinstance(prompt, str):
+            self.prompts = [prompt]
+        else:
+            self.prompts = prompt
+        
+        chunk_data = num_chunks is not None
+        if chunk_data and chunk_idx is None:
+            raise ValueError("If `num_chunks` is provided, `chunk_idx` must be provided as well.")
+
+        self.images = glob.glob(os.path.join(image_dir, f"*{ext}"))
+        if chunk_data:
+            self.images = get_chunk(self.images, num_chunks, chunk_idx)
+
+        if seg_image_dir is not None:
+            self.seg_images = glob.glob(os.path.join(seg_image_dir, f"*{ext}"))
+            if chunk_data:
+                self.seg_images = get_chunk(self.seg_images, num_chunks, chunk_idx)
+            if len(self.images) != len(self.seg_images):
+                raise ValueError(
+                    f"The number of images is {len(self.images)} and segmented images is {len(self.seg_images)}, but"
+                    f" the number of images and segmented images must be equal."
+                )
+        else:
+            self.seg_images = None
+        
+        if depth_image_dir is not None:
+            self.depth_images = glob.glob(os.path.join(depth_image_dir, f"*{ext}"))
+            if chunk_data:
+                self.depth_images = get_chunk(self.depth_images, num_chunks, chunk_idx)
+            if len(self.images) != len(self.depth_images):
+                raise ValueError(
+                    f"The number of images is {len(self.images)} and depth images is {len(self.depth_images)}, but the"
+                    f" number of images and depth images must be equal."
+                )
+        else:
+            self.depth_images = None
+
+    def __getitem__(self, index):
+        image_file = self.images[index]
+        if self.seg_images is not None:
+            seg_image_file = self.seg_images[index]
+        else:
+            seg_image_file = None
+        if self.depth_images is not None:
+            depth_image_file = self.depth_images[index]
+        else:
+            depth_image_file = None
+        
+        prompt = random.choice(self.prompts)
+        prompt = DEFAULT_IMAGE_TOKEN + '\n' + prompt
+
+        image = Image.open(os.path.join(image_file)).convert('RGB')
+        image_tensor = process_images([image], self.image_processor, self.model_config)[0]
+        if seg_image_file is not None:
+            seg_image = Image.open(os.path.join(seg_image_file)).convert('RGB')
+            seg_image_tensor = process_images([seg_image], self.seg_image_processor, self.model_config)[0]
+            prompt = DEFAULT_SEG_TOKEN + '\n' + prompt
+        else:
+            seg_image_tensor = image_tensor
+        if depth_image_file is not None:
+            depth_image = Image.open(os.path.join(depth_image_file)).convert('RGB')
+            depth_image_tensor = process_images([depth_image], self.depth_image_processor, self.model_config)[0]
+            prompt = DEFAULT_DEPTH_TOKEN + '\n' + prompt
+        else:
+            depth_image_tensor = image_tensor
+        
+        conv = conv_templates[args.conv_mode].copy()
+        conv.append_message(conv.roles[0], prompt)
+        conv.append_message(conv.roles[1], None)
+        conv_prompt = conv.get_prompt()
+
+        if seg_image_file is None and depth_image_file is None:
+            input_ids = tokenizer_image_token(conv_prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
+        elif seg_image_file is not None and depth_image_file is None:
+            input_ids = tokenizer_seg_token(
+                conv_prompt, self.tokenizer, IMAGE_TOKEN_INDEX, SEG_TOKEN_INDEX, return_tensors='pt'
+            )
+        else:
+            input_ids = tokenizer_depth_seg_token(
+                conv_prompt, self.tokenizer, IMAGE_TOKEN_INDEX, SEG_TOKEN_INDEX, DEPTH_TOKEN_INDEX, return_tensors='pt'
+            )
+        
+        return input_ids, image_tensor, seg_image_tensor, depth_image_tensor, image_file.split("/")[-1], prompt
+
+    def __len__(self):
+        return len(self.images)
+
+
+def create_dataloader(
+    prompt,
+    tokenizer,
+    image_dir,
+    image_processor,
+    seg_image_dir,
+    seg_image_processor,
+    depth_image_dir,
+    depth_image_processor,
+    model_config,
+    num_chunks: Optional[int] = None,
+    chunk_idx: Optional[int] = None,
+    conv_mode: str = "llava_v1",
+    ext: str =".jpg",
+    batch_size: int = 1,
+    num_workers: int = 4,
+):
+    dataset = VCoderDataset(
+        prompt,
+        tokenizer,
+        image_dir,
+        image_processor,
+        seg_image_dir,
+        seg_image_processor,
+        depth_image_dir,
+        depth_image_processor,
+        model_config,
+        num_chunks=num_chunks,
+        chunk_idx=chunk_idx,
+        conv_mode=conv_mode,
+        ext=ext,
+    )
+    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
+    return dataloader
+
+
+def postprocess_output(output: str, stop_str: str):
+    output = output.strip()
+    if output.endswith(stop_str):
+        output = output[:-len(stop_str)]
+    output = output.strip()
+    output = output.strip('\n')
+    return output
+
+
+def evaluate_model(
+    model,
+    dataloader,
+    tokenizer,
+    output_file,
+    conv_mode: str = "llava_v1",
+    temperature: float = 0.2,
+    top_p: Optional[float] = None,
+    num_beams: int = 1,
+    max_new_tokens: int = 512,
+):
+    model.eval()
+    stop_str = (
+        conv_templates[conv_mode].sep
+        if conv_templates[conv_mode].sep_style != SeparatorStyle.TWO
+        else conv_templates[conv_mode].sep2
+    )
+    do_sample = True if temperature > 0 else False
+
+    nsamples = len(dataloader)
+    desc = "Generating answers..."
+    for input_ids, images, seg_images, depth_images, image_files, prompts in tqdm(dataloader, total=nsamples, desc=desc):
+        input_ids = input_ids.to(device='cuda', non_blocking=True)
+        images = images.to(dtype=torch.float16, device='cuda', non_blocking=True)
+        seg_images = seg_images.to(dtype=torch.float16, device='cuda', non_blocking=True)
+        depth_images = depth_images.to(dtype=torch.float16, device='cuda', non_blocking=True)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=images,
+                segs=seg_images,
+                depths=depth_images,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                num_beams=num_beams,
+                max_new_tokens=max_new_tokens,
+                use_cache=True,
+            )
+        
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)
+        answers = [postprocess_output(output, stop_str) for output in outputs]
+
+        with open(output_file, 'a') as f:
+            for i in range(len(answers)):
+                f.write(f'Image: {image_files[i]}\n')
+                f.write(f'<<PROMPT>>: {prompts[i]}\n')
+                f.write(f'<<ANSWER>>: {answers[i]}\n')
+                f.write('-------------------------------------------------------\n')
+
+
+def main(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    (
+        tokenizer,
+        model,
+        image_processor,
+        seg_image_processor,
+        depth_image_processor,
+        context_len,
+    ) = load_pretrained_model(
+        model_path,
+        args.model_base,
+        model_name,
+        load_8bit=args.load_8bit,
+        load_4bit=args.load_4bit,
+        device=args.device,
+    )
+
+    if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
+        args.conv_mode = args.conv_mode + '_mmtag'
+        print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
+    
+    os.makedirs(args.output_folder, exist_ok=True)
+    answer_filename = args.output_file_name
+    if args.num_chunks is not None and args.chunk_idx is not None:
+        answer_filename += f"_{args.num_chunks}_{args.chunk_idx}"
+    answer_filename += ".txt"
+    answer_path = os.path.join(args.output_folder, answer_filename)
+
+    dataloader = create_dataloader(
+        args.prompt,
+        tokenizer,
+        args.image_folder,
+        image_processor,
+        args.seg_image_folder,
+        seg_image_processor,
+        args.depth_image_folder,
+        depth_image_processor,
+        model.config,
+        num_chunks=args.num_chunks,
+        chunk_idx=args.chunk_idx,
+        conv_mode=args.conv_mode,
+        ext=args.extension,
+        batch_size=args.batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    evaluate_model(
+        model,
+        dataloader,
+        tokenizer,
+        answer_path,
+        conv_mode=args.conv_mode,
+        temperature=args.temperature,
+        top_p=args.top_p,
+        num_beams=args.num_beams,
+        max_new_tokens=args.max_new_tokens,
+    )
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--model-path", type=str, default="shi-labs/vcoder_ds_llava-v1.5-13b")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--image-folder", type=str, default=None, required=True)
+    parser.add_argument("--seg-image-folder", type=str, default=None)
+    parser.add_argument("--depth-image-folder", type=str, default=None)
+    parser.add_argument("--output-folder", type=str, default=None, required=True)
+    parser.add_argument("--output-file-name", type=str, default="output")
+    parser.add_argument("--prompt", type=str, default=None)
+    parser.add_argument("--num-chunks", type=int, default=None)
+    parser.add_argument("--chunk-idx", type=int, default=None)
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--extension", type=str, default=".jpg")
+    parser.add_argument("--dataloader-num-workers", type=int, default=4)
+    parser.add_argument("--batch-size", type=int, default=1)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+
+    args = parser.parse_args()
+
+    main(args)