Instructions to use jaygala223/upernet-swin-tiny-binarization-and-label-merging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaygala223/upernet-swin-tiny-binarization-and-label-merging with Transformers:
# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jaygala223/upernet-swin-tiny-binarization-and-label-merging") model = UperNetForSemanticSegmentation.from_pretrained("jaygala223/upernet-swin-tiny-binarization-and-label-merging", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "openmmlab/upernet-swin-tiny", | |
| "architectures": [ | |
| "UperNetForSemanticSegmentation" | |
| ], | |
| "auxiliary_channels": 256, | |
| "auxiliary_concat_input": false, | |
| "auxiliary_in_channels": 384, | |
| "auxiliary_loss_weight": 0.4, | |
| "auxiliary_num_convs": 1, | |
| "backbone_config": { | |
| "attention_probs_dropout_prob": 0.0, | |
| "depths": [ | |
| 2, | |
| 2, | |
| 6, | |
| 2 | |
| ], | |
| "drop_path_rate": 0.1, | |
| "embed_dim": 96, | |
| "encoder_stride": 32, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-05, | |
| "mlp_ratio": 4.0, | |
| "model_type": "swin", | |
| "num_heads": [ | |
| 3, | |
| 6, | |
| 12, | |
| 24 | |
| ], | |
| "num_layers": 4, | |
| "out_features": [ | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 1, | |
| 2, | |
| 3, | |
| 4 | |
| ], | |
| "patch_size": 4, | |
| "path_norm": true, | |
| "qkv_bias": true, | |
| "use_absolute_embeddings": false, | |
| "window_size": 7 | |
| }, | |
| "hidden_size": 512, | |
| "id2label": { | |
| "0": "non-cloud", | |
| "1": "cloud" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "airplane": 90, | |
| "animal": 126, | |
| "apparel": 92, | |
| "arcade machine": 78, | |
| "armchair": 30, | |
| "ashcan": 138, | |
| "awning": 86, | |
| "bag": 115, | |
| "ball": 119, | |
| "bannister": 95, | |
| "bar": 77, | |
| "barrel": 111, | |
| "base": 40, | |
| "basket": 112, | |
| "bathtub": 37, | |
| "bed ": 7, | |
| "bench": 69, | |
| "bicycle": 127, | |
| "blanket": 131, | |
| "blind": 63, | |
| "boat": 76, | |
| "book": 67, | |
| "bookcase": 62, | |
| "booth": 88, | |
| "bottle": 98, | |
| "box": 41, | |
| "bridge": 61, | |
| "buffet": 99, | |
| "building": 1, | |
| "bulletin board": 144, | |
| "bus": 80, | |
| "cabinet": 10, | |
| "canopy": 106, | |
| "car": 20, | |
| "case": 55, | |
| "ceiling": 5, | |
| "chair": 19, | |
| "chandelier": 85, | |
| "chest of drawers": 44, | |
| "clock": 148, | |
| "coffee table": 64, | |
| "column": 42, | |
| "computer": 74, | |
| "conveyer belt": 105, | |
| "counter": 45, | |
| "countertop": 70, | |
| "cradle": 117, | |
| "crt screen": 141, | |
| "curtain": 18, | |
| "cushion": 39, | |
| "desk": 33, | |
| "dirt track": 91, | |
| "dishwasher": 129, | |
| "door": 14, | |
| "earth": 13, | |
| "escalator": 96, | |
| "fan": 139, | |
| "fence": 32, | |
| "field": 29, | |
| "fireplace": 49, | |
| "flag": 149, | |
| "floor": 3, | |
| "flower": 66, | |
| "food": 120, | |
| "fountain": 104, | |
| "glass": 147, | |
| "grandstand": 51, | |
| "grass": 9, | |
| "hill": 68, | |
| "hood": 133, | |
| "house": 25, | |
| "hovel": 79, | |
| "kitchen island": 73, | |
| "lake": 128, | |
| "lamp": 36, | |
| "land": 94, | |
| "light": 82, | |
| "microwave": 124, | |
| "minibike": 116, | |
| "mirror": 27, | |
| "monitor": 143, | |
| "mountain": 16, | |
| "ottoman": 97, | |
| "oven": 118, | |
| "painting": 22, | |
| "palm": 72, | |
| "path": 52, | |
| "person": 12, | |
| "pier": 140, | |
| "pillow": 57, | |
| "plant": 17, | |
| "plate": 142, | |
| "plaything": 108, | |
| "pole": 93, | |
| "pool table": 56, | |
| "poster": 100, | |
| "pot": 125, | |
| "radiator": 146, | |
| "railing": 38, | |
| "refrigerator": 50, | |
| "river": 60, | |
| "road": 6, | |
| "rock": 34, | |
| "rug": 28, | |
| "runway": 54, | |
| "sand": 46, | |
| "sconce": 134, | |
| "screen": 130, | |
| "screen door": 58, | |
| "sculpture": 132, | |
| "sea": 26, | |
| "seat": 31, | |
| "shelf": 24, | |
| "ship": 103, | |
| "shower": 145, | |
| "sidewalk": 11, | |
| "signboard": 43, | |
| "sink": 47, | |
| "sky": 2, | |
| "skyscraper": 48, | |
| "sofa": 23, | |
| "stage": 101, | |
| "stairs": 53, | |
| "stairway": 59, | |
| "step": 121, | |
| "stool": 110, | |
| "stove": 71, | |
| "streetlight": 87, | |
| "swimming pool": 109, | |
| "swivel chair": 75, | |
| "table": 15, | |
| "tank": 122, | |
| "television receiver": 89, | |
| "tent": 114, | |
| "toilet": 65, | |
| "towel": 81, | |
| "tower": 84, | |
| "trade name": 123, | |
| "traffic light": 136, | |
| "tray": 137, | |
| "tree": 4, | |
| "truck": 83, | |
| "van": 102, | |
| "vase": 135, | |
| "wall": 0, | |
| "wardrobe": 35, | |
| "washer": 107, | |
| "water": 21, | |
| "waterfall": 113, | |
| "windowpane": 8 | |
| }, | |
| "loss_ignore_index": 255, | |
| "model_type": "upernet", | |
| "pool_scales": [ | |
| 1, | |
| 2, | |
| 3, | |
| 6 | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.33.0", | |
| "use_auxiliary_head": true | |
| } | |