update installation and cc3m dataset

configs/pretrain/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_cc3mlite_train_lvis_minival.py

@@ -0,0 +1,183 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_x_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+# text_model_name = '../pretrained_models/clip-vit-base-patch32-projection'
+text_model_name = 'openai/clip-vit-base-patch32'
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name=text_model_name,
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWorldPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv')),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    use_bn_head=True,
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+
+cc3m_train_dataset = dict(type='YOLOv5GeneralGroundingDataset',
+                          data_root='data/cc3m/',
+                          ann_file='annotations/cc3m_pseudo_annotations.json',
+                          data_prefix=dict(img='training'),
+                          filter_cfg=dict(filter_empty_gt=True, min_size=32),
+                          pipeline=train_pipeline)
+
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset,
+                                         mg_train_dataset,
+                                         cc3m_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_minival_inserted_image_name.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

docs/installation.md

@@ -1,5 +1,7 @@
 ## Installation Guide
 
+YOLO-World is built based on `pytorch=1.11.0` and `mmcv=2.0.0`.
+
 We provide the `requirements` files in [./requirements](./../requirements/):
 
 * `basic_requirements`: training, finetuning, evaluation.

docs/prompt_yolo_world.md

@@ -3,16 +3,16 @@
 
 ### 1. Simple YOLO-World with Embeddings
 
-For simplifying YOLO-World and get rid of the language model, we define a new basic detector `YOLOWorldPromptDetector`:
+For simplifying YOLO-World and get rid of the language model, we define a new basic detector `SimpleYOLOWorldDetector`:
 
-The `YOLOWorldPromptDetector` supports prompt embeddings as the input and doesn't not contain a language model anymore!
+The `SimpleYOLOWorldDetector` supports prompt embeddings as the input and doesn't not contain a language model anymore!
 Now, YOLO-World adopts `embeddings` as language inputs, and the embeddings support several kinds: (1) text embeddings from the language model, e.g., CLIP language encoder, (2) image embeddings from a vision model, e.g., CLIP vision encoder, and (3) image-text fused embeddings, and (4) random embeddings.
 The (1)(2)(3) supports zero-shot inference and (4), including (1)(2)(3) are designed for prompt tuning on your custom data.
 
 The basic detector is defined as follows:
 
 ```python
-class YOLOWorldPromptDetector(YOLODetector):
+class SimpleYOLOWorldDetector(YOLODetector):
     """Implementation of YOLO World Series"""
 
     def __init__(self,
@@ -40,7 +40,7 @@ For more details about writing configs for prompt tuning, you can refer to [`pro
 1. Use random prompts
 
 ```python
-dict(type='YOLOWorldPromptDetector',
+dict(type='SimpleYOLOWorldDetector',
              mm_neck=True,
              num_train_classes=num_training_classes,
              num_test_classes=num_classes,
@@ -54,7 +54,7 @@ dict(type='YOLOWorldPromptDetector',
 the `clip_vit_b32_coco_80_embeddings.npy` can be downloaded at [HuggingFace](https://huggingface.co/wondervictor/YOLO-World/blob/main/clip_vit_b32_coco_80_embeddings.npy).
 
 ```python
-dict(type='YOLOWorldPromptDetector',
+dict(type='SimpleYOLOWorldDetector',
              mm_neck=True,
              num_train_classes=num_training_classes,
              num_test_classes=num_classes,

pyproject.toml

@@ -29,7 +29,7 @@ requires-python = ">= 3.7"
 
 dependencies = [
     "wheel",
-    "torch>=2.1.0",
+    "torch>=1.11.0",
     "torchvision>=0.16.2",
     "transformers",
     "tokenizers",
@@ -38,8 +38,9 @@ dependencies = [
     "supervision==0.19.0",
     "openmim",
     "mmcv-lite>=2.0.0rc4",
-    "mmdet>=3.0.0",
+    "mmdet==3.0.0",
     "mmengine>=0.7.1",
+    "openmim",
     "mmcv",
     'mmyolo @ git+https://github.com/onuralpszr/mmyolo.git',
 

yolo_world/datasets/__init__.py

@@ -8,10 +8,12 @@
 from .transformers import *  # NOQA
 from .yolov5_v3det import YOLOv5V3DetDataset
 from .yolov5_lvis import YOLOv5LVISV1Dataset
+from .yolov5_cc3m_grounding import YOLOv5GeneralGroundingDataset
 
 __all__ = [
     'MultiModalDataset', 'YOLOv5Objects365V1Dataset',
     'YOLOv5Objects365V2Dataset', 'YOLOv5MixedGroundingDataset',
     'YOLOv5V3DetDataset', 'yolow_collate',
     'YOLOv5LVISV1Dataset', 'MultiModalMixedDataset',
+    'YOLOv5GeneralGroundingDataset'
 ]