shi-labs
/

oneformer_ade20k_swin_large

 - src: https://praeclarumjj3.github.io/files/coco.jpeg
   example_title: Person
 ---
+# OneFormer
+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).
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/oneformer_teaser.png)
+## Model description
+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.
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/oneformer_architecture.png)
+## Intended uses & limitations
+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.
+### How to use
+Here is how to use this model:
+```python
+from transformers import OneFormerFeatureExtractor, OneFormerForUniversalSegmentation
+from PIL import Image
+import requests
+url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+# Loading a single model for all three tasks
+feature_extractor = OneFormerFeatureExtractor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
+model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
+# Semantic Segmentation
+semantic_inputs = feature_extractor(images=image, ["semantic"] return_tensors="pt")
+semantic_outputs = model(**semantic_inputs)
+# pass through feature_extractor for postprocessing
+predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+# Instance Segmentation
+instance_inputs = feature_extractor(images=image, ["instance"] return_tensors="pt")
+instance_outputs = model(**instance_inputs)
+# pass through feature_extractor for postprocessing
+predicted_instance_map = feature_extractor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
+# Panoptic Segmentation
+panoptic_inputs = feature_extractor(images=image, ["panoptic"] return_tensors="pt")
+panoptic_outputs = model(**panoptic_inputs)
+# pass through feature_extractor for postprocessing
+predicted_semantic_map = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
+```
+For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
+### Citation
+```bibtex
+@article{jain2022oneformer,
+      title={{OneFormer: One Transformer to Rule Universal Image Segmentation}},
+      author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi},
+      journal={arXiv},
+      year={2022}
+    }
+```

config.json ADDED Viewed

	@@ -0,0 +1,408 @@

+{
+  "architectures": [
+    "OneFormerForUniversalSegmentation"
+  ],
+  "backbone_config": {
+    "attention_probs_dropout_prob": 0.0,
+    "depths": [
+      2,
+      2,
+      18,
+      2
+    ],
+    "drop_path_rate": 0.3,
+    "embed_dim": 192,
+    "encoder_stride": 32,
+    "feature_channels": [
+      192,
+      384,
+      768,
+      1536
+    ],
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "image_size": 384,
+    "mlp_ratio": 4.0,
+    "num_channels": 3,
+    "num_heads": [
+      6,
+      12,
+      24,
+      48
+    ],
+    "patch_norm": true,
+    "patch_size": 4,
+    "qkv_bias": true,
+    "strides": [
+      4,
+      8,
+      16,
+      32
+    ],
+    "use_absolute_embeddings": false,
+    "window_size": 12
+  },
+  "decoder_config": {
+    "common_stride": 4,
+    "conv_dim": 256,
+    "decoder_layers": 10,
+    "dim_feedforward": 2048,
+    "dropout": 0.1,
+    "encoder_feedforward_dim": 1024,
+    "encoder_layers": 6,
+    "enforce_input_proj": false,
+    "hidden_dim": 256,
+    "mask_dim": 256,
+    "norm": "GN",
+    "num_heads": 8,
+    "pre_norm": false,
+    "query_dec_layers": 2,
+    "use_task_norm": true
+  },
+  "general_config": {
+    "backbone_type": "swin",
+    "class_weight": 2.0,
+    "contrastive_temperature": 0.07,
+    "contrastive_weight": 0.5,
+    "deep_supervision": true,
+    "dice_weight": 5.0,
+    "ignore_value": 255,
+    "importance_sample_ratio": 0.75,
+    "init_std": 0.02,
+    "init_xavier_std": 1.0,
+    "is_train": false,
+    "layer_norm_eps": 1e-05,
+    "mask_weight": 5.0,
+    "no_object_weight": 0.1,
+    "num_classes": 150,
+    "num_queries": 250,
+    "output_auxiliary_logits": true,
+    "oversample_ratio": 3.0,
+    "train_num_points": 12544,
+    "use_auxiliary_loss": true
+  },
+  "hidden_size": 256,
+  "id2label": {
+    "0": "wall",
+    "1": "building",
+    "2": "sky",
+    "3": "floor",
+    "4": "tree",
+    "5": "ceiling",
+    "6": "road, route",
+    "7": "bed",
+    "8": "window ",
+    "9": "grass",
+    "10": "cabinet",
+    "11": "sidewalk, pavement",
+    "12": "person",
+    "13": "earth, ground",
+    "14": "door",
+    "15": "table",
+    "16": "mountain, mount",
+    "17": "plant",
+    "18": "curtain",
+    "19": "chair",
+    "20": "car",
+    "21": "water",
+    "22": "painting, picture",
+    "23": "sofa",
+    "24": "shelf",
+    "25": "house",
+    "26": "sea",
+    "27": "mirror",
+    "28": "rug",
+    "29": "field",
+    "30": "armchair",
+    "31": "seat",
+    "32": "fence",
+    "33": "desk",
+    "34": "rock, stone",
+    "35": "wardrobe, closet, press",
+    "36": "lamp",
+    "37": "tub",
+    "38": "rail",
+    "39": "cushion",
+    "40": "base, pedestal, stand",
+    "41": "box",
+    "42": "column, pillar",
+    "43": "signboard, sign",
+    "44": "chest of drawers, chest, bureau, dresser",
+    "45": "counter",
+    "46": "sand",
+    "47": "sink",
+    "48": "skyscraper",
+    "49": "fireplace",
+    "50": "refrigerator, icebox",
+    "51": "grandstand, covered stand",
+    "52": "path",
+    "53": "stairs",
+    "54": "runway",
+    "55": "case, display case, showcase, vitrine",
+    "56": "pool table, billiard table, snooker table",
+    "57": "pillow",
+    "58": "screen door, screen",
+    "59": "stairway, staircase",
+    "60": "river",
+    "61": "bridge, span",
+    "62": "bookcase",
+    "63": "blind, screen",
+    "64": "coffee table",
+    "65": "toilet, can, commode, crapper, pot, potty, stool, throne",
+    "66": "flower",
+    "67": "book",
+    "68": "hill",
+    "69": "bench",
+    "70": "countertop",
+    "71": "stove",
+    "72": "palm, palm tree",
+    "73": "kitchen island",
+    "74": "computer",
+    "75": "swivel chair",
+    "76": "boat",
+    "77": "bar",
+    "78": "arcade machine",
+    "79": "hovel, hut, hutch, shack, shanty",
+    "80": "bus",
+    "81": "towel",
+    "82": "light",
+    "83": "truck",
+    "84": "tower",
+    "85": "chandelier",
+    "86": "awning, sunshade, sunblind",
+    "87": "street lamp",
+    "88": "booth",
+    "89": "tv",
+    "90": "plane",
+    "91": "dirt track",
+    "92": "clothes",
+    "93": "pole",
+    "94": "land, ground, soil",
+    "95": "bannister, banister, balustrade, balusters, handrail",
+    "96": "escalator, moving staircase, moving stairway",
+    "97": "ottoman, pouf, pouffe, puff, hassock",
+    "98": "bottle",
+    "99": "buffet, counter, sideboard",
+    "100": "poster, posting, placard, notice, bill, card",
+    "101": "stage",
+    "102": "van",
+    "103": "ship",
+    "104": "fountain",
+    "105": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+    "106": "canopy",
+    "107": "washer, automatic washer, washing machine",
+    "108": "plaything, toy",
+    "109": "pool",
+    "110": "stool",
+    "111": "barrel, cask",
+    "112": "basket, handbasket",
+    "113": "falls",
+    "114": "tent",
+    "115": "bag",
+    "116": "minibike, motorbike",
+    "117": "cradle",
+    "118": "oven",
+    "119": "ball",
+    "120": "food, solid food",
+    "121": "step, stair",
+    "122": "tank, storage tank",
+    "123": "trade name",
+    "124": "microwave",
+    "125": "pot",
+    "126": "animal",
+    "127": "bicycle",
+    "128": "lake",
+    "129": "dishwasher",
+    "130": "screen",
+    "131": "blanket, cover",
+    "132": "sculpture",
+    "133": "hood, exhaust hood",
+    "134": "sconce",
+    "135": "vase",
+    "136": "traffic light",
+    "137": "tray",
+    "138": "trash can",
+    "139": "fan",
+    "140": "pier",
+    "141": "crt screen",
+    "142": "plate",
+    "143": "monitor",
+    "144": "bulletin board",
+    "145": "shower",
+    "146": "radiator",
+    "147": "glass, drinking glass",
+    "148": "clock",
+    "149": "flag"
+  },
+  "init_std": 0.02,
+  "init_xavier_std": 1.0,
+  "label2id": {
+    "animal": 126,
+    "arcade machine": 78,
+    "armchair": 30,
+    "awning, sunshade, sunblind": 86,
+    "bag": 115,
+    "ball": 119,
+    "bannister, banister, balustrade, balusters, handrail": 95,
+    "bar": 77,
+    "barrel, cask": 111,
+    "base, pedestal, stand": 40,
+    "basket, handbasket": 112,
+    "bed": 7,
+    "bench": 69,
+    "bicycle": 127,
+    "blanket, cover": 131,
+    "blind, screen": 63,
+    "boat": 76,
+    "book": 67,
+    "bookcase": 62,
+    "booth": 88,
+    "bottle": 98,
+    "box": 41,
+    "bridge, span": 61,
+    "buffet, counter, sideboard": 99,
+    "building": 1,
+    "bulletin board": 144,
+    "bus": 80,
+    "cabinet": 10,
+    "canopy": 106,
+    "car": 20,
+    "case, display case, showcase, vitrine": 55,
+    "ceiling": 5,
+    "chair": 19,
+    "chandelier": 85,
+    "chest of drawers, chest, bureau, dresser": 44,
+    "clock": 148,
+    "clothes": 92,
+    "coffee table": 64,
+    "column, pillar": 42,
+    "computer": 74,
+    "conveyer belt, conveyor belt, conveyer, conveyor, transporter": 105,
+    "counter": 45,
+    "countertop": 70,
+    "cradle": 117,
+    "crt screen": 141,
+    "curtain": 18,
+    "cushion": 39,
+    "desk": 33,
+    "dirt track": 91,
+    "dishwasher": 129,
+    "door": 14,
+    "earth, ground": 13,
+    "escalator, moving staircase, moving stairway": 96,
+    "falls": 113,
+    "fan": 139,
+    "fence": 32,
+    "field": 29,
+    "fireplace": 49,
+    "flag": 149,
+    "floor": 3,
+    "flower": 66,
+    "food, solid food": 120,
+    "fountain": 104,
+    "glass, drinking glass": 147,
+    "grandstand, covered stand": 51,
+    "grass": 9,
+    "hill": 68,
+    "hood, exhaust hood": 133,
+    "house": 25,
+    "hovel, hut, hutch, shack, shanty": 79,
+    "kitchen island": 73,
+    "lake": 128,
+    "lamp": 36,
+    "land, ground, soil": 94,
+    "light": 82,
+    "microwave": 124,
+    "minibike, motorbike": 116,
+    "mirror": 27,
+    "monitor": 143,
+    "mountain, mount": 16,
+    "ottoman, pouf, pouffe, puff, hassock": 97,
+    "oven": 118,
+    "painting, picture": 22,
+    "palm, palm tree": 72,
+    "path": 52,
+    "person": 12,
+    "pier": 140,
+    "pillow": 57,
+    "plane": 90,
+    "plant": 17,
+    "plate": 142,
+    "plaything, toy": 108,
+    "pole": 93,
+    "pool": 109,
+    "pool table, billiard table, snooker table": 56,
+    "poster, posting, placard, notice, bill, card": 100,
+    "pot": 125,
+    "radiator": 146,
+    "rail": 38,
+    "refrigerator, icebox": 50,
+    "river": 60,
+    "road, route": 6,
+    "rock, stone": 34,
+    "rug": 28,
+    "runway": 54,
+    "sand": 46,
+    "sconce": 134,
+    "screen": 130,
+    "screen door, screen": 58,
+    "sculpture": 132,
+    "sea": 26,
+    "seat": 31,
+    "shelf": 24,
+    "ship": 103,
+    "shower": 145,
+    "sidewalk, pavement": 11,
+    "signboard, sign": 43,
+    "sink": 47,
+    "sky": 2,
+    "skyscraper": 48,
+    "sofa": 23,
+    "stage": 101,
+    "stairs": 53,
+    "stairway, staircase": 59,
+    "step, stair": 121,
+    "stool": 110,
+    "stove": 71,
+    "street lamp": 87,
+    "swivel chair": 75,
+    "table": 15,
+    "tank, storage tank": 122,
+    "tent": 114,
+    "toilet, can, commode, crapper, pot, potty, stool, throne": 65,
+    "towel": 81,
+    "tower": 84,
+    "trade name": 123,
+    "traffic light": 136,
+    "trash can": 138,
+    "tray": 137,
+    "tree": 4,
+    "truck": 83,
+    "tub": 37,
+    "tv": 89,
+    "van": 102,
+    "vase": 135,
+    "wall": 0,
+    "wardrobe, closet, press": 35,
+    "washer, automatic washer, washing machine": 107,
+    "water": 21,
+    "window ": 8
+  },
+  "model_type": "oneformer",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 10,
+  "output_attentions": true,
+  "output_hidden_states": true,
+  "text_encoder_config": {
+    "max_seq_len": 77,
+    "task_seq_len": 77,
+    "text_encoder_context_length": 77,
+    "text_encoder_n_ctx": 16,
+    "text_encoder_num_layers": 6,
+    "text_encoder_proj_layers": 2,
+    "text_encoder_vocab_size": 49408,
+    "text_encoder_width": 256
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0"
+}

preprocessor_config.json ADDED Viewed

	@@ -0,0 +1,1939 @@