praeclarumjj3
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Add documentation
Browse files- README.md +63 -0
- config.json +408 -0
- preprocessor_config.json +1939 -0
README.md
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@@ -14,3 +14,66 @@ widget:
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- src: https://praeclarumjj3.github.io/files/coco.jpeg
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example_title: Person
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---
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- src: https://praeclarumjj3.github.io/files/coco.jpeg
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example_title: Person
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---
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# OneFormer
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OneFormer model trained on the ADE20k dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer).
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![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/oneformer_teaser.png)
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## Model description
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OneFormer is the first multi-task universal image segmentation framework based on transformers. OneFormer needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing frameworks across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model.
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![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/oneformer_architecture.png)
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## Intended uses & limitations
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You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset.
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### How to use
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Here is how to use this model:
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```python
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from transformers import OneFormerFeatureExtractor, OneFormerForUniversalSegmentation
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from PIL import Image
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import requests
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url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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# Loading a single model for all three tasks
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feature_extractor = OneFormerFeatureExtractor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
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model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
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# Semantic Segmentation
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semantic_inputs = feature_extractor(images=image, ["semantic"] return_tensors="pt")
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semantic_outputs = model(**semantic_inputs)
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# pass through feature_extractor for postprocessing
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predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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# Instance Segmentation
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instance_inputs = feature_extractor(images=image, ["instance"] return_tensors="pt")
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instance_outputs = model(**instance_inputs)
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# pass through feature_extractor for postprocessing
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predicted_instance_map = feature_extractor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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# Panoptic Segmentation
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panoptic_inputs = feature_extractor(images=image, ["panoptic"] return_tensors="pt")
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panoptic_outputs = model(**panoptic_inputs)
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# pass through feature_extractor for postprocessing
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predicted_semantic_map = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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```
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For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
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### Citation
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```bibtex
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@article{jain2022oneformer,
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title={{OneFormer: One Transformer to Rule Universal Image Segmentation}},
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author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi},
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journal={arXiv},
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year={2022}
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}
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```
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config.json
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{
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"architectures": [
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"OneFormerForUniversalSegmentation"
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],
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"backbone_config": {
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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2,
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2
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],
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"drop_path_rate": 0.3,
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"embed_dim": 192,
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"encoder_stride": 32,
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"feature_channels": [
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192,
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384,
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768,
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1536
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],
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"image_size": 384,
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"mlp_ratio": 4.0,
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"num_channels": 3,
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"num_heads": [
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6,
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12,
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24,
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48
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],
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"patch_norm": true,
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"patch_size": 4,
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"qkv_bias": true,
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"strides": [
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4,
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8,
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16,
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32
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],
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"use_absolute_embeddings": false,
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"window_size": 12
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},
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"decoder_config": {
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"common_stride": 4,
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"conv_dim": 256,
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"decoder_layers": 10,
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"dim_feedforward": 2048,
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"dropout": 0.1,
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"encoder_feedforward_dim": 1024,
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"encoder_layers": 6,
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"enforce_input_proj": false,
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"hidden_dim": 256,
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"mask_dim": 256,
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"norm": "GN",
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"num_heads": 8,
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"pre_norm": false,
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"query_dec_layers": 2,
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"use_task_norm": true
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},
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"general_config": {
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"backbone_type": "swin",
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"class_weight": 2.0,
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"contrastive_temperature": 0.07,
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"contrastive_weight": 0.5,
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"deep_supervision": true,
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"dice_weight": 5.0,
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"ignore_value": 255,
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"importance_sample_ratio": 0.75,
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"init_std": 0.02,
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"init_xavier_std": 1.0,
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"is_train": false,
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"layer_norm_eps": 1e-05,
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"mask_weight": 5.0,
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"no_object_weight": 0.1,
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"num_classes": 150,
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"num_queries": 250,
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"output_auxiliary_logits": true,
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"oversample_ratio": 3.0,
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"train_num_points": 12544,
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"use_auxiliary_loss": true
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},
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"hidden_size": 256,
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"id2label": {
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"0": "wall",
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"1": "building",
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"2": "sky",
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"3": "floor",
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"4": "tree",
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"5": "ceiling",
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"6": "road, route",
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"7": "bed",
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"8": "window ",
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"9": "grass",
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"10": "cabinet",
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"11": "sidewalk, pavement",
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"12": "person",
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"13": "earth, ground",
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"14": "door",
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"15": "table",
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"16": "mountain, mount",
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"17": "plant",
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"18": "curtain",
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"19": "chair",
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"20": "car",
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"21": "water",
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"22": "painting, picture",
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"23": "sofa",
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"24": "shelf",
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"25": "house",
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"26": "sea",
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"27": "mirror",
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"28": "rug",
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"29": "field",
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"30": "armchair",
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"31": "seat",
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"32": "fence",
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"33": "desk",
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"34": "rock, stone",
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"35": "wardrobe, closet, press",
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"36": "lamp",
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"37": "tub",
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"38": "rail",
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"39": "cushion",
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"40": "base, pedestal, stand",
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"41": "box",
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"42": "column, pillar",
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"43": "signboard, sign",
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"44": "chest of drawers, chest, bureau, dresser",
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"45": "counter",
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"46": "sand",
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"47": "sink",
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"48": "skyscraper",
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"49": "fireplace",
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136 |
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"50": "refrigerator, icebox",
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137 |
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"51": "grandstand, covered stand",
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138 |
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"52": "path",
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139 |
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"53": "stairs",
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140 |
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"54": "runway",
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141 |
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"55": "case, display case, showcase, vitrine",
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142 |
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"56": "pool table, billiard table, snooker table",
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143 |
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"57": "pillow",
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144 |
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"58": "screen door, screen",
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145 |
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"59": "stairway, staircase",
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146 |
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"60": "river",
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147 |
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"61": "bridge, span",
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148 |
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"62": "bookcase",
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149 |
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"63": "blind, screen",
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150 |
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"64": "coffee table",
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151 |
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"65": "toilet, can, commode, crapper, pot, potty, stool, throne",
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152 |
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"66": "flower",
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153 |
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"67": "book",
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154 |
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"68": "hill",
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155 |
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"69": "bench",
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156 |
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"70": "countertop",
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157 |
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"71": "stove",
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158 |
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"72": "palm, palm tree",
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159 |
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"73": "kitchen island",
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160 |
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"74": "computer",
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161 |
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"75": "swivel chair",
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162 |
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"76": "boat",
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163 |
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"77": "bar",
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164 |
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"78": "arcade machine",
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165 |
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"79": "hovel, hut, hutch, shack, shanty",
|
166 |
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"80": "bus",
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167 |
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"81": "towel",
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168 |
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"82": "light",
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169 |
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"83": "truck",
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170 |
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"84": "tower",
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171 |
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"85": "chandelier",
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172 |
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"86": "awning, sunshade, sunblind",
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173 |
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"87": "street lamp",
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174 |
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"88": "booth",
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175 |
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"89": "tv",
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176 |
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"90": "plane",
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177 |
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"91": "dirt track",
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178 |
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"92": "clothes",
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179 |
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"93": "pole",
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180 |
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"94": "land, ground, soil",
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181 |
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"95": "bannister, banister, balustrade, balusters, handrail",
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182 |
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"96": "escalator, moving staircase, moving stairway",
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183 |
+
"97": "ottoman, pouf, pouffe, puff, hassock",
|
184 |
+
"98": "bottle",
|
185 |
+
"99": "buffet, counter, sideboard",
|
186 |
+
"100": "poster, posting, placard, notice, bill, card",
|
187 |
+
"101": "stage",
|
188 |
+
"102": "van",
|
189 |
+
"103": "ship",
|
190 |
+
"104": "fountain",
|
191 |
+
"105": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
|
192 |
+
"106": "canopy",
|
193 |
+
"107": "washer, automatic washer, washing machine",
|
194 |
+
"108": "plaything, toy",
|
195 |
+
"109": "pool",
|
196 |
+
"110": "stool",
|
197 |
+
"111": "barrel, cask",
|
198 |
+
"112": "basket, handbasket",
|
199 |
+
"113": "falls",
|
200 |
+
"114": "tent",
|
201 |
+
"115": "bag",
|
202 |
+
"116": "minibike, motorbike",
|
203 |
+
"117": "cradle",
|
204 |
+
"118": "oven",
|
205 |
+
"119": "ball",
|
206 |
+
"120": "food, solid food",
|
207 |
+
"121": "step, stair",
|
208 |
+
"122": "tank, storage tank",
|
209 |
+
"123": "trade name",
|
210 |
+
"124": "microwave",
|
211 |
+
"125": "pot",
|
212 |
+
"126": "animal",
|
213 |
+
"127": "bicycle",
|
214 |
+
"128": "lake",
|
215 |
+
"129": "dishwasher",
|
216 |
+
"130": "screen",
|
217 |
+
"131": "blanket, cover",
|
218 |
+
"132": "sculpture",
|
219 |
+
"133": "hood, exhaust hood",
|
220 |
+
"134": "sconce",
|
221 |
+
"135": "vase",
|
222 |
+
"136": "traffic light",
|
223 |
+
"137": "tray",
|
224 |
+
"138": "trash can",
|
225 |
+
"139": "fan",
|
226 |
+
"140": "pier",
|
227 |
+
"141": "crt screen",
|
228 |
+
"142": "plate",
|
229 |
+
"143": "monitor",
|
230 |
+
"144": "bulletin board",
|
231 |
+
"145": "shower",
|
232 |
+
"146": "radiator",
|
233 |
+
"147": "glass, drinking glass",
|
234 |
+
"148": "clock",
|
235 |
+
"149": "flag"
|
236 |
+
},
|
237 |
+
"init_std": 0.02,
|
238 |
+
"init_xavier_std": 1.0,
|
239 |
+
"label2id": {
|
240 |
+
"animal": 126,
|
241 |
+
"arcade machine": 78,
|
242 |
+
"armchair": 30,
|
243 |
+
"awning, sunshade, sunblind": 86,
|
244 |
+
"bag": 115,
|
245 |
+
"ball": 119,
|
246 |
+
"bannister, banister, balustrade, balusters, handrail": 95,
|
247 |
+
"bar": 77,
|
248 |
+
"barrel, cask": 111,
|
249 |
+
"base, pedestal, stand": 40,
|
250 |
+
"basket, handbasket": 112,
|
251 |
+
"bed": 7,
|
252 |
+
"bench": 69,
|
253 |
+
"bicycle": 127,
|
254 |
+
"blanket, cover": 131,
|
255 |
+
"blind, screen": 63,
|
256 |
+
"boat": 76,
|
257 |
+
"book": 67,
|
258 |
+
"bookcase": 62,
|
259 |
+
"booth": 88,
|
260 |
+
"bottle": 98,
|
261 |
+
"box": 41,
|
262 |
+
"bridge, span": 61,
|
263 |
+
"buffet, counter, sideboard": 99,
|
264 |
+
"building": 1,
|
265 |
+
"bulletin board": 144,
|
266 |
+
"bus": 80,
|
267 |
+
"cabinet": 10,
|
268 |
+
"canopy": 106,
|
269 |
+
"car": 20,
|
270 |
+
"case, display case, showcase, vitrine": 55,
|
271 |
+
"ceiling": 5,
|
272 |
+
"chair": 19,
|
273 |
+
"chandelier": 85,
|
274 |
+
"chest of drawers, chest, bureau, dresser": 44,
|
275 |
+
"clock": 148,
|
276 |
+
"clothes": 92,
|
277 |
+
"coffee table": 64,
|
278 |
+
"column, pillar": 42,
|
279 |
+
"computer": 74,
|
280 |
+
"conveyer belt, conveyor belt, conveyer, conveyor, transporter": 105,
|
281 |
+
"counter": 45,
|
282 |
+
"countertop": 70,
|
283 |
+
"cradle": 117,
|
284 |
+
"crt screen": 141,
|
285 |
+
"curtain": 18,
|
286 |
+
"cushion": 39,
|
287 |
+
"desk": 33,
|
288 |
+
"dirt track": 91,
|
289 |
+
"dishwasher": 129,
|
290 |
+
"door": 14,
|
291 |
+
"earth, ground": 13,
|
292 |
+
"escalator, moving staircase, moving stairway": 96,
|
293 |
+
"falls": 113,
|
294 |
+
"fan": 139,
|
295 |
+
"fence": 32,
|
296 |
+
"field": 29,
|
297 |
+
"fireplace": 49,
|
298 |
+
"flag": 149,
|
299 |
+
"floor": 3,
|
300 |
+
"flower": 66,
|
301 |
+
"food, solid food": 120,
|
302 |
+
"fountain": 104,
|
303 |
+
"glass, drinking glass": 147,
|
304 |
+
"grandstand, covered stand": 51,
|
305 |
+
"grass": 9,
|
306 |
+
"hill": 68,
|
307 |
+
"hood, exhaust hood": 133,
|
308 |
+
"house": 25,
|
309 |
+
"hovel, hut, hutch, shack, shanty": 79,
|
310 |
+
"kitchen island": 73,
|
311 |
+
"lake": 128,
|
312 |
+
"lamp": 36,
|
313 |
+
"land, ground, soil": 94,
|
314 |
+
"light": 82,
|
315 |
+
"microwave": 124,
|
316 |
+
"minibike, motorbike": 116,
|
317 |
+
"mirror": 27,
|
318 |
+
"monitor": 143,
|
319 |
+
"mountain, mount": 16,
|
320 |
+
"ottoman, pouf, pouffe, puff, hassock": 97,
|
321 |
+
"oven": 118,
|
322 |
+
"painting, picture": 22,
|
323 |
+
"palm, palm tree": 72,
|
324 |
+
"path": 52,
|
325 |
+
"person": 12,
|
326 |
+
"pier": 140,
|
327 |
+
"pillow": 57,
|
328 |
+
"plane": 90,
|
329 |
+
"plant": 17,
|
330 |
+
"plate": 142,
|
331 |
+
"plaything, toy": 108,
|
332 |
+
"pole": 93,
|
333 |
+
"pool": 109,
|
334 |
+
"pool table, billiard table, snooker table": 56,
|
335 |
+
"poster, posting, placard, notice, bill, card": 100,
|
336 |
+
"pot": 125,
|
337 |
+
"radiator": 146,
|
338 |
+
"rail": 38,
|
339 |
+
"refrigerator, icebox": 50,
|
340 |
+
"river": 60,
|
341 |
+
"road, route": 6,
|
342 |
+
"rock, stone": 34,
|
343 |
+
"rug": 28,
|
344 |
+
"runway": 54,
|
345 |
+
"sand": 46,
|
346 |
+
"sconce": 134,
|
347 |
+
"screen": 130,
|
348 |
+
"screen door, screen": 58,
|
349 |
+
"sculpture": 132,
|
350 |
+
"sea": 26,
|
351 |
+
"seat": 31,
|
352 |
+
"shelf": 24,
|
353 |
+
"ship": 103,
|
354 |
+
"shower": 145,
|
355 |
+
"sidewalk, pavement": 11,
|
356 |
+
"signboard, sign": 43,
|
357 |
+
"sink": 47,
|
358 |
+
"sky": 2,
|
359 |
+
"skyscraper": 48,
|
360 |
+
"sofa": 23,
|
361 |
+
"stage": 101,
|
362 |
+
"stairs": 53,
|
363 |
+
"stairway, staircase": 59,
|
364 |
+
"step, stair": 121,
|
365 |
+
"stool": 110,
|
366 |
+
"stove": 71,
|
367 |
+
"street lamp": 87,
|
368 |
+
"swivel chair": 75,
|
369 |
+
"table": 15,
|
370 |
+
"tank, storage tank": 122,
|
371 |
+
"tent": 114,
|
372 |
+
"toilet, can, commode, crapper, pot, potty, stool, throne": 65,
|
373 |
+
"towel": 81,
|
374 |
+
"tower": 84,
|
375 |
+
"trade name": 123,
|
376 |
+
"traffic light": 136,
|
377 |
+
"trash can": 138,
|
378 |
+
"tray": 137,
|
379 |
+
"tree": 4,
|
380 |
+
"truck": 83,
|
381 |
+
"tub": 37,
|
382 |
+
"tv": 89,
|
383 |
+
"van": 102,
|
384 |
+
"vase": 135,
|
385 |
+
"wall": 0,
|
386 |
+
"wardrobe, closet, press": 35,
|
387 |
+
"washer, automatic washer, washing machine": 107,
|
388 |
+
"water": 21,
|
389 |
+
"window ": 8
|
390 |
+
},
|
391 |
+
"model_type": "oneformer",
|
392 |
+
"num_attention_heads": 8,
|
393 |
+
"num_hidden_layers": 10,
|
394 |
+
"output_attentions": true,
|
395 |
+
"output_hidden_states": true,
|
396 |
+
"text_encoder_config": {
|
397 |
+
"max_seq_len": 77,
|
398 |
+
"task_seq_len": 77,
|
399 |
+
"text_encoder_context_length": 77,
|
400 |
+
"text_encoder_n_ctx": 16,
|
401 |
+
"text_encoder_num_layers": 6,
|
402 |
+
"text_encoder_proj_layers": 2,
|
403 |
+
"text_encoder_vocab_size": 49408,
|
404 |
+
"text_encoder_width": 256
|
405 |
+
},
|
406 |
+
"torch_dtype": "float32",
|
407 |
+
"transformers_version": "4.25.0.dev0"
|
408 |
+
}
|
preprocessor_config.json
ADDED
@@ -0,0 +1,1939 @@
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