add rtdetrv2

README.md

@@ -28,17 +28,25 @@ English | [简体中文](README_cn.md)
 
 ---
 
+
+This is the official implementation of papers 
+- [DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)
+- [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://arxiv.org/abs/2407.17140)
+
+
+<details>
+<summary>Fig</summary>
+
 <div align="center">
   <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/0ede1dc1-a854-43b6-9986-cf9090f11a61" width=500 >
 </div>
 
+</details>
 
 
 
-This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)".
-
-
 ## 🚀 Updates
+- \[2024.07.24\] Release RT-DETRv2!
 - \[2024.02.27\] Our work has been accepted to CVPR 2024!
 - \[2024.01.23\] Fix difference on data augmentation with paper in rtdetr_pytorch [#84](https://github.com/lyuwenyu/RT-DETR/commit/5dc64138e439247b4e707dd6cebfe19d8d77f5b1).
 - \[2023.11.07\] Add pytorch ✅ *rtdetr_r34vd* for requests [#107](https://github.com/lyuwenyu/RT-DETR/issues/107), [#114](https://github.com/lyuwenyu/RT-DETR/issues/114).
@@ -56,33 +64,36 @@ This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time
 - \[2023.04.17\] Release RT-DETR-R50, RT-DETR-R101, RT-DETR-L, RT-DETR-X.
 
 ## 📍 Implementations
-- 🔥 RT-DETR paddle: [code](./rtdetr_paddle), [weights](./rtdetr_paddle)
-- 🔥 RT-DETR pytorch: [code](./rtdetr_pytorch), [weights](./rtdetr_pytorch)
-
-
-| Model | Epoch | Input shape | Dataset | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS)
-|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|:---:|
-| RT-DETR-R18 | 6x | 640 | COCO | 46.5 | 63.8 | 20 | 60 | 217 |
-| RT-DETR-R34 | 6x | 640 | COCO | 48.9 | 66.8 | 31 | 92 | 161 |
-| RT-DETR-R50-m | 6x | 640 | COCO | 51.3 | 69.6 | 36 | 100 | 145 |
-| RT-DETR-R50 | 6x |  640 | COCO | 53.1 | 71.3 | 42 | 136 | 108 |
-| RT-DETR-R101 | 6x | 640 | COCO | 54.3 | 72.7 | 76 | 259 | 74 |
-| RT-DETR-HGNetv2-L | 6x | 640 | COCO | 53.0 | 71.6 | 32 | 110 | 114 |
-| RT-DETR-HGNetv2-X | 6x | 640 | COCO | 54.8 | 73.1 | 67 | 234 | 74 |
-| RT-DETR-R18 | 5x | 640 | COCO + Objects365 | **49.2** | **66.6** | 20 | 60 | **217** |
-| RT-DETR-R50 | 2x | 640 | COCO + Objects365 | **55.3** | **73.4** | 42 | 136 | **108** |
-| RT-DETR-R101 | 2x | 640 | COCO + Objects365 | **56.2** | **74.6** | 76 | 259 | **74** |
+- 🔥 RT-DETRv2
+  - paddle: [code&weight](./rtdetrv2_paddle/)
+  - pytorch: [code&weight](./rtdetrv2_pytorch/)
+- 🔥 RT-DETR 
+  - paddle: [code&weight](./rtdetr_paddle)
+  - pytorch: [code&weight](./rtdetr_pytorch)
+
+
+| Model | Input shape | Dataset | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS)
+|:---:|:---:| :---:|:---:|:---:|:---:|:---:|:---:|
+| RT-DETR-R18 | 640 | COCO | 46.5 | 63.8 | 20 | 60 | 217 |
+| RT-DETR-R34 | 640 | COCO | 48.9 | 66.8 | 31 | 92 | 161 |
+| RT-DETR-R50-m | 640 | COCO | 51.3 | 69.6 | 36 | 100 | 145 |
+| RT-DETR-R50 |  640 | COCO | 53.1 | 71.3 | 42 | 136 | 108 |
+| RT-DETR-R101 | 640 | COCO | 54.3 | 72.7 | 76 | 259 | 74 |
+| RT-DETR-HGNetv2-L | 640 | COCO | 53.0 | 71.6 | 32 | 110 | 114 |
+| RT-DETR-HGNetv2-X | 640 | COCO | 54.8 | 73.1 | 67 | 234 | 74 |
+| RT-DETR-R18 | 640 | COCO + Objects365 | **49.2** | **66.6** | 20 | 60 | **217** |
+| RT-DETR-R50 | 640 | COCO + Objects365 | **55.3** | **73.4** | 42 | 136 | **108** |
+| RT-DETR-R101 | 640 | COCO + Objects365 | **56.2** | **74.6** | 76 | 259 | **74** |
+**RT-DETRv2-S** | 640 | COCO  | **47.9** <font color=green>(+1.4)</font> | **64.9** | 20 | 60 | 217 |
+**RT-DETRv2-M** | 640 | COCO  | **49.9** <font color=green>(+1.0)</font> | **67.5** | 31 | 92 | 161 |
+**RT-DETRv2-M**<sup>*<sup> | 640 | COCO | **51.9** <font color=green>(+0.6)</font> | **69.9** | 36 | 100 | 145 |
+**RT-DETRv2-L** | 640 | COCO | **53.4** <font color=green>(+0.3)</font> | **71.6** | 42 | 136 | 108 |
+**RT-DETRv2-X** | 640 | COCO | 54.3 | **72.8** <font color=green>(+0.1)</font>  | 76 | 259| 74 |
 
 **Notes:**
 - `COCO + Objects365` in the table means finetuned model on COCO using pretrained weights trained on Objects365.
 
 
-## 💡 Introduction
-We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR, aka RTDETR), the first real-time end-to-end object detector to our best knowledge. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy. Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP.
-<div align="center">
-  <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/c211a164-ddce-4084-8b71-fb73f29f363b" width=500 >
-</div>
-
 ## 🦄 Performance
 
 ### 🏕️ Complex Scenarios
@@ -96,7 +107,7 @@ We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR, aka RTDET
 </div>
 
 ## Citation
-If you use `RT-DETR` in your work, please use the following BibTeX entries:
+If you use `RT-DETR` or `RTDETRv2` in your work, please use the following BibTeX entries:
 ```
 @misc{lv2023detrs,
       title={DETRs Beat YOLOs on Real-time Object Detection},
@@ -106,4 +117,14 @@ If you use `RT-DETR` in your work, please use the following BibTeX entries:
       archivePrefix={arXiv},
       primaryClass={cs.CV}
 }
+
+@misc{lv2024rtdetrv2improvedbaselinebagoffreebies,
+      title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer}, 
+      author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu},
+      year={2024},
+      eprint={2407.17140},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.17140}, 
+}
 ```

README_cn.md

@@ -2,23 +2,33 @@
 
 # RT-DETR 
 
-This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)".
+文章"[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)"和"[RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://arxiv.org/abs/2407.17140)"的官方实现.
+
+<details>
+<summary>Fig</summary>
 
 <div align="center">
   <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/0ede1dc1-a854-43b6-9986-cf9090f11a61" width=500 >
 </div>
 
-## 最新动态
+</details>
 
+
+## 最新动态
+- 发布RT-DETRv2系列模型
 - 发布RT-DETR-R50, RT-DETR-R101模型
 - 发布RT-DETR-R50-m模型（scale模型的范例）
 - 发布RT-DETR-R34, RT-DETR-R18模型
 - 发布RT-DETR-L, RT-DETR-X模型
 
 
 ## 代码仓库
-- [RT-DETR-paddle](./rtdetr_paddle)
-- [RT-DETR--pytorch](./rtdetr_pytorch)
+- 🔥 RT-DETRv2
+  - paddle: [code&weight](./rtdetrv2_paddle/)
+  - pytorch: [code&weight](./rtdetrv2_pytorch/)
+- 🔥 RT-DETR 
+  - paddle: [code&weight](./rtdetr_paddle)
+  - pytorch: [code&weight](./rtdetr_pytorch)
 
 
 ## 简介
@@ -41,4 +51,14 @@ RT-DETR是第一个实时端到端目标检测器。具体而言，我们设计
       archivePrefix={arXiv},
       primaryClass={cs.CV}
 }
+
+@misc{lv2024rtdetrv2improvedbaselinebagoffreebies,
+      title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer}, 
+      author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu},
+      year={2024},
+      eprint={2407.17140},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.17140}, 
+}
 ```

rtdetr_paddle/ppdet/modeling/transformers/utils.py

@@ -299,12 +299,18 @@ def get_contrastive_denoising_training_group(targets,
     if label_noise_ratio > 0:
         input_query_class = input_query_class.flatten()
         pad_gt_mask = pad_gt_mask.flatten()
+
+        # Convert pad_gt_mask to bool if it's not already
+        pad_gt_mask = pad_gt_mask.astype('bool')
+
         # half of bbox prob
         mask = paddle.rand(input_query_class.shape) < (label_noise_ratio * 0.5)
         chosen_idx = paddle.nonzero(mask * pad_gt_mask).squeeze(-1)
+
         # randomly put a new one here
         new_label = paddle.randint_like(
             chosen_idx, 0, num_classes, dtype=input_query_class.dtype)
+
         input_query_class.scatter_(chosen_idx, new_label)
         input_query_class.reshape_([bs, num_denoising])
         pad_gt_mask.reshape_([bs, num_denoising])

rtdetrv2_paddle/readme.md

@@ -0,0 +1 @@
+see https://github.com/PaddlePaddle/PaddleDetection

rtdetrv2_pytorch/README.md

@@ -0,0 +1,153 @@
+
+## Quick start
+
+<details open>
+<summary>Setup</summary>
+
+```shell
+
+pip install -r requirements.txt
+```
+
+The following is the corresponding `torch` and `torchvision` versions.
+`rtdetr` | `torch` | `torchvision`
+|---|---|---|
+| `-` | `2.2` | `0.17` |
+| `-` | `2.1` | `0.16` |
+| `-` | `2.0` | `0.15` |
+
+</details>
+
+
+
+## Model Zoo
+
+### Base models
+
+| Model | Dataset | Input Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | #Params(M) | FPS | config| checkpoint | 
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
+**RT-DETRv2-S** | COCO | 640 | **47.9** <font color=green>(+1.4)</font> | **64.9** | 20 | 217 | [config](./configs/rtdetrv2/rtdetrv2_r18vd_120e_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_120e_coco.pth) |
+**RT-DETRv2-M** | COCO | 640 | **49.9** <font color=green>(+1.0)</font> | **67.5** | 31 | 161 | [config](./configs/rtdetrv2/rtdetrv2_r34vd_120e_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r34vd_120e_coco_ema.pth)
+**RT-DETRv2-M**<sup>*<sup> | COCO | 640 | **51.9** <font color=green>(+0.6)</font> | **69.9** | 36 | 145 | [config](./configs/rtdetrv2/rtdetrv2_r50vd_m_7x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_m_7x_coco_ema.pth)
+**RT-DETRv2-L** | COCO | 640 | **53.4** <font color=green>(+0.3)</font> | **71.6** | 42 | 108 | [config](./configs/rtdetrv2/rtdetrv2_r50vd_6x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_6x_coco_ema.pth)
+**RT-DETRv2-X** | COCO | 640 | 54.3 | **72.8** <font color=green>(+0.1)</font> | 76 | 74 | [config](./configs/rtdetrv2/rtdetrv2_r101vd_6x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r101vd_6x_coco_from_paddle.pth)
+<!-- rtdetrv2_hgnetv2_l | COCO | 640 | 52.9 | 71.5 | 32 | 114 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_hgnetv2_l_6x_coco_from_paddle.pth) 
+rtdetrv2_hgnetv2_x | COCO | 640 | 54.7 | 72.9 | 67 | 74 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_hgnetv2_x_6x_coco_from_paddle.pth) 
+rtdetrv2_hgnetv2_h | COCO | 640 | 56.3 | 74.8 | 123 | 40 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_hgnetv2_h_6x_coco_from_paddle.pth) 
+rtdetrv2_18vd | COCO+Objects365 | 640 | 49.0 | 66.5 | 20 | 217 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_5x_coco_objects365_from_paddle.pth)
+rtdetrv2_r50vd | COCO+Objects365 | 640 | 55.2 | 73.4 | 42 | 108 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_2x_coco_objects365_from_paddle.pth)
+rtdetrv2_r101vd | COCO+Objects365 | 640 | 56.2 | 74.5 | 76 | 74 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r101vd_2x_coco_objects365_from_paddle.pth)
+ -->
+
+**Notes:**
+- `AP` is evaluated on *MSCOCO val2017* dataset.
+- `FPS` is evaluated on a single T4 GPU with $batch\\_size = 1$, $fp16$, and $TensorRT>=8.5.1$.
+- `COCO + Objects365` in the table means finetuned model on `COCO` using pretrained weights trained on `Objects365`.
+
+
+
+### Models of discrete sampling
+
+| Model | Sampling Method | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | config| checkpoint 
+| :---: | :---: | :---: | :---: | :---: | :---: |
+**RT-DETRv2-S_dsp** | discrete_sampling | 47.4 | 64.8 <font color=red>(-0.1)</font> | [config](./configs/rtdetrv2/rtdetrv2_r18vd_dsp_3x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_dsp_3x_coco.pth)
+**RT-DETRv2-M_dsp** | discrete_sampling | 49.2 | 67.1 <font color=red>(-0.4)</font> | [config](./configs/rtdetrv2/rtdetrv2_r34vd_dsp_1x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rrtdetrv2_r34vd_dsp_1x_coco.pth)
+**RT-DETRv2-M**<sup>*</sup>**_dsp** | discrete_sampling | 51.4 | 69.7 <font color=red>(-0.2)</font> | [config](./configs/rtdetrv2/rtdetrv2_r50vd_m_dsp_3x_coco.yml) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_m_dsp_3x_coco.pth)
+**RT-DETRv2-L_dsp** | discrete_sampling | 52.9 | 71.3 <font color=red>(-0.3)</font> |[config](./configs/rtdetrv2/rtdetrv2_r50vd_dsp_1x_coco.yml)| [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_dsp_1x_coco.pth)
+
+
+<!-- **rtdetrv2_r18vd_dsp1** | discrete_sampling | 21600 | 46.3 | 63.9 | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_dsp1_1x_coco.pth) -->
+
+<!-- rtdetrv2_r18vd_dsp1 | discrete_sampling | 21600 | 45.5 | 63.0 | 4.34 | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_dsp1_120e_coco.pth) -->
+<!-- 4.3 -->
+
+**Notes:**
+- The impact on inference speed is related to specific device and software.
+- `*_dsp*` is the model inherit `*_sp*` model's knowledge and adapt to `discrete_sampling` strategy. **You can use TensorRT 8.4 (or even older versions) to inference for these models**
+<!-- - `grid_sampling` use `grid_sample` to sample attention map, `discrete_sampling` use `index_select` method to sample attention map.  -->
+
+
+### Ablation on sampling points
+
+<!-- Flexible samping strategy in cross attenstion layer for devices that do **not** optimize (or not support) `grid_sampling` well. You can choose models based on specific scenarios and the trade-off between speed and accuracy. -->
+
+| Model | Sampling Method | #Points | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | checkpoint 
+| :---: | :---: | :---: | :---: | :---: | :---: |
+**rtdetrv2_r18vd_sp1** | grid_sampling | 21,600 | 47.3 | 64.3 <font color=red>(-0.6) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_sp1_120e_coco.pth)
+**rtdetrv2_r18vd_sp2** | grid_sampling | 43,200 | 47.7 | 64.7 <font color=red>(-0.2) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_sp2_120e_coco.pth)
+**rtdetrv2_r18vd_sp3** | grid_sampling | 64,800 | 47.8 | 64.8 <font color=red>(-0.1) | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_sp3_120e_coco.pth)
+rtdetrv2_r18vd(_sp4)| grid_sampling | 86,400 | 47.9 | 64.9 | [url](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r18vd_120e_coco.pth) 
+
+**Notes:**
+- The impact on inference speed is related to specific device and software.
+- `#points` the total number of sampling points in decoder for per image inference.
+
+
+## Usage
+<details>
+<summary> details </summary>
+
+<!-- <summary>1. Training </summary> -->
+1. Training
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=9909 --nproc_per_node=4 tools/train.py -c path/to/config --use-amp --seed=0 &> log.txt 2>&1 &
+```
+
+<!-- <summary>2. Testing </summary> -->
+2. Testing
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=9909 --nproc_per_node=4 tools/train.py -c path/to/config -r path/to/checkpoint --test-only
+```
+
+<!-- <summary>3. Tuning </summary> -->
+3. Tuning
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=9909 --nproc_per_node=4 tools/train.py -c path/to/config -t path/to/checkpoint --use-amp --seed=0 &> log.txt 2>&1 &
+```
+
+<!-- <summary>4. Export onnx </summary> -->
+4. Export onnx
+```shell
+python tools/export_onnx.py -c path/to/config -r path/to/checkpoint --check
+```
+
+<!-- <summary>5. Inference </summary> -->
+5. Inference
+
+Support torch, onnxruntime, tensorrt and openvino, see details in *references/deploy*
+```shell
+python references/deploy/rtdetrv2_onnx.py --onnx-file=model.onnx --im-file=xxxx
+python references/deploy/rtdetrv2_tensorrt.py --trt-file=model.trt --im-file=xxxx
+python references/deploy/rtdetrv2_torch.py -c path/to/config -r path/to/checkpoint --im-file=xxx --device=cuda:0
+```
+</details>
+
+
+
+## Citation
+If you use `RTDETR` or `RTDETRv2` in your work, please use the following BibTeX entries:
+
+<details>
+<summary> bibtex </summary>
+
+```latex
+@misc{lv2023detrs,
+      title={DETRs Beat YOLOs on Real-time Object Detection},
+      author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
+      year={2023},
+      eprint={2304.08069},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{lv2024rtdetrv2improvedbaselinebagoffreebies,
+      title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer}, 
+      author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu},
+      year={2024},
+      eprint={2407.17140},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.17140}, 
+}
+```
+</details>

rtdetrv2_pytorch/configs/dataset/coco_detection.yml

@@ -0,0 +1,48 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+# num_classes: 365
+# remap_mscoco_category: False
+
+# num_classes: 91
+# remap_mscoco_category: False
+
+num_classes: 80
+remap_mscoco_category: True
+
+
+train_dataloader: 
+  type: DataLoader
+  dataset: 
+    type: CocoDetection
+    img_folder: ./dataset/coco/train2017/
+    ann_file: ./dataset/coco/annotations/instances_train2017.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True 
+  collate_fn:
+    type: BatchImageCollateFuncion
+
+
+val_dataloader:
+  type: DataLoader
+  dataset: 
+    type: CocoDetection
+    img_folder: ./dataset/coco/val2017/
+    ann_file: ./dataset/coco/annotations/instances_val2017.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~ 
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFuncion