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ComfyUI/models/segformer_b2_clothes/README.md

@@ -0,0 +1,109 @@
+---
+license: mit
+tags:
+- vision
+- image-segmentation
+widget:
+- src: https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
+  example_title: Person
+- src: https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
+  example_title: Person
+datasets:
+- mattmdjaga/human_parsing_dataset
+---
+# Segformer B2 fine-tuned for clothes segmentation
+
+SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
+The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
+
+
+**NEW** - 
+**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version
+ and a blog post with it to make it more friendly. 
+
+```python
+from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+
+processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
+model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
+
+url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits.cpu()
+
+upsampled_logits = nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],
+    mode="bilinear",
+    align_corners=False,
+)
+
+pred_seg = upsampled_logits.argmax(dim=1)[0]
+plt.imshow(pred_seg)
+```
+
+Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf"
+
+### Evaluation
+
+|  Label Index  |    Label Name    | Category Accuracy | Category IoU |
+|:-------------:|:----------------:|:-----------------:|:------------:|
+|       0       |    Background    |       0.99        |     0.99     |
+|       1       |        Hat       |       0.73        |     0.68     |
+|       2       |        Hair      |       0.91        |     0.82     |
+|       3       |    Sunglasses    |       0.73        |     0.63     |
+|       4       |  Upper-clothes   |       0.87        |     0.78     |
+|       5       |       Skirt      |       0.76        |     0.65     |
+|       6       |       Pants      |       0.90        |     0.84     |
+|       7       |       Dress      |       0.74        |     0.55     |
+|       8       |       Belt       |       0.35        |     0.30     |
+|       9       |    Left-shoe     |       0.74        |     0.58     |
+|      10       |   Right-shoe     |       0.75        |     0.60     |
+|      11       |       Face       |       0.92        |     0.85     |
+|      12       |    Left-leg      |       0.90        |     0.82     |
+|      13       |   Right-leg      |       0.90        |     0.81     |
+|      14       |    Left-arm      |       0.86        |     0.74     |
+|      15       |   Right-arm      |       0.82        |     0.73     |
+|      16       |        Bag       |       0.91        |     0.84     |
+|      17       |       Scarf      |       0.63        |     0.29     |
+
+Overall Evaluation Metrics:
+- Evaluation Loss: 0.15
+- Mean Accuracy: 0.80
+- Mean IoU: 0.69
+
+### License
+
+The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2105-15203,
+  author    = {Enze Xie and
+               Wenhai Wang and
+               Zhiding Yu and
+               Anima Anandkumar and
+               Jose M. Alvarez and
+               Ping Luo},
+  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
+               Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2105.15203},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2105.15203},
+  eprinttype = {arXiv},
+  eprint    = {2105.15203},
+  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

ComfyUI/models/segformer_b2_clothes/config.json

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+{
+  "_name_or_path": "nvidia/mit-b2",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "Background",
+    "1": "Hat",
+    "2": "Hair",
+    "3": "Sunglasses",
+    "4": "Upper-clothes",
+    "5": "Skirt",
+    "6": "Pants",
+    "7": "Dress",
+    "8": "Belt",
+    "9": "Left-shoe",
+    "10": "Right-shoe",
+    "11": "Face",
+    "12": "Left-leg",
+    "13": "Right-leg",
+    "14": "Left-arm",
+    "15": "Right-arm",
+    "16": "Bag",
+    "17": "Scarf"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "Background": 0,
+    "Bag": 16,
+    "Belt": 8,
+    "Dress": 7,
+    "Face": 11,
+    "Hair": 2,
+    "Hat": 1,
+    "Left-arm": 14,
+    "Left-leg": 12,
+    "Left-shoe": 9,
+    "Pants": 6,
+    "Right-arm": 15,
+    "Right-leg": 13,
+    "Right-shoe": 10,
+    "Scarf": 17,
+    "Skirt": 5,
+    "Sunglasses": 3,
+    "Upper-clothes": 4
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0"
+}

ComfyUI/models/segformer_b2_clothes/handler.py

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+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+from transformers import AutoModelForSemanticSegmentation, AutoFeatureExtractor
+import base64
+import torch
+from torch import nn
+
+class EndpointHandler():
+    def __init__(self, path="."):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = AutoModelForSemanticSegmentation.from_pretrained(path).to(self.device).eval()
+        self.feature_extractor = AutoFeatureExtractor.from_pretrained(path)
+    
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            images (:obj:`PIL.Image`)
+            candiates (:obj:`list`)
+      Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+        """
+        inputs = data.pop("inputs", data)
+
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+        
+        # preprocess image
+        encoding = self.feature_extractor(images=image, return_tensors="pt")
+        pixel_values = encoding["pixel_values"].to(self.device)
+        with torch.no_grad():
+            outputs = self.model(pixel_values=pixel_values)
+        logits = outputs.logits
+        upsampled_logits = nn.functional.interpolate(logits, 
+            size=image.size[::-1],
+            mode="bilinear",
+            align_corners=False,)
+        pred_seg = upsampled_logits.argmax(dim=1)[0]
+        return pred_seg.tolist()

ComfyUI/models/segformer_b2_clothes/model.safetensors

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+size 109493236

ComfyUI/models/segformer_b2_clothes/optimizer.pt

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+size 219104837

ComfyUI/models/segformer_b2_clothes/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "SegformerFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "reduce_labels": false,
+  "resample": 2,
+  "size": 512
+}

ComfyUI/models/segformer_b2_clothes/pytorch_model.bin

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+size 109579005

ComfyUI/models/segformer_b2_clothes/rng_state.pth

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+oid sha256:a7c38376dfee2c075efd2b37186139541f47970794c545ba17f510796313aaa8
+size 14575

ComfyUI/models/segformer_b2_clothes/scheduler.pt

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ComfyUI/models/segformer_b2_clothes/training_args.bin

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+size 3323

ComfyUI/models/segformer_b3_clothes/README.md

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+---
+license: mit
+tags:
+- vision
+- image-segmentation
+widget:
+- src: >-
+    https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
+  example_title: Person
+- src: >-
+    https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
+  example_title: Person
+datasets:
+- mattmdjaga/human_parsing_dataset
+pipeline_tag: image-segmentation
+---
+# Segformer B3 fine-tuned for clothes segmentation
+
+SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
+The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
+
+
+**NEW** - 
+**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version
+ and a blog post with it to make it more friendly. 
+
+```python
+from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+
+processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer_b3_clothes")
+model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_clothes")
+
+url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits.cpu()
+
+upsampled_logits = nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],
+    mode="bilinear",
+    align_corners=False,
+)
+
+pred_seg = upsampled_logits.argmax(dim=1)[0]
+plt.imshow(pred_seg)
+```
+
+Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf"
+
+### Evaluation
+
+|  Label Index  |    Label Name    | Category Accuracy | Category IoU |
+|:-------------:|:----------------:|:-----------------:|:------------:|
+|       0       |    Background    |       0.99        |     0.99     |
+|       1       |        Hat       |       0.73        |     0.68     |
+|       2       |        Hair      |       0.91        |     0.82     |
+|       3       |    Sunglasses    |       0.73        |     0.63     |
+|       4       |  Upper-clothes   |       0.87        |     0.78     |
+|       5       |       Skirt      |       0.76        |     0.65     |
+|       6       |       Pants      |       0.90        |     0.84     |
+|       7       |       Dress      |       0.74        |     0.55     |
+|       8       |       Belt       |       0.35        |     0.30     |
+|       9       |    Left-shoe     |       0.74        |     0.58     |
+|      10       |   Right-shoe     |       0.75        |     0.60     |
+|      11       |       Face       |       0.92        |     0.85     |
+|      12       |    Left-leg      |       0.90        |     0.82     |
+|      13       |   Right-leg      |       0.90        |     0.81     |
+|      14       |    Left-arm      |       0.86        |     0.74     |
+|      15       |   Right-arm      |       0.82        |     0.73     |
+|      16       |        Bag       |       0.91        |     0.84     |
+|      17       |       Scarf      |       0.63        |     0.29     |
+
+Overall Evaluation Metrics:
+- Evaluation Loss: 0.15
+- Mean Accuracy: 0.80
+- Mean IoU: 0.69
+
+### License
+
+The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2105-15203,
+  author    = {Enze Xie and
+               Wenhai Wang and
+               Zhiding Yu and
+               Anima Anandkumar and
+               Jose M. Alvarez and
+               Ping Luo},
+  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
+               Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2105.15203},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2105.15203},
+  eprinttype = {arXiv},
+  eprint    = {2105.15203},
+  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

ComfyUI/models/segformer_b3_clothes/config.json

@@ -0,0 +1,110 @@
+{
+  "_name_or_path": "nvidia/mit-b3",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    18,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "Background",
+    "1": "Hat",
+    "10": "Right-shoe",
+    "11": "Face",
+    "12": "Left-leg",
+    "13": "Right-leg",
+    "14": "Left-arm",
+    "15": "Right-arm",
+    "16": "Bag",
+    "17": "Scarf",
+    "2": "Hair",
+    "3": "Sunglasses",
+    "4": "Upper-clothes",
+    "5": "Skirt",
+    "6": "Pants",
+    "7": "Dress",
+    "8": "Belt",
+    "9": "Left-shoe"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "Background": "0",
+    "Bag": "16",
+    "Belt": "8",
+    "Dress": "7",
+    "Face": "11",
+    "Hair": "2",
+    "Hat": "1",
+    "Left-arm": "14",
+    "Left-leg": "12",
+    "Left-shoe": "9",
+    "Pants": "6",
+    "Right-arm": "15",
+    "Right-leg": "13",
+    "Right-shoe": "10",
+    "Scarf": "17",
+    "Skirt": "5",
+    "Sunglasses": "3",
+    "Upper-clothes": "4"
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1"
+}

ComfyUI/models/segformer_b3_clothes/model.safetensors

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+size 189029000

ComfyUI/models/segformer_b3_clothes/preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_reduce_labels": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "SegformerImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 512,
+    "width": 512
+  }
+}

ComfyUI/models/segformer_b3_fashion/README.md

@@ -0,0 +1,92 @@
+---
+license: other
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+widget:
+- src: >-
+    https://media.istockphoto.com/id/515788534/photo/cheerful-and-confidant.jpg?s=612x612&w=0&k=20&c=T0Z4DfameRpyGhzevPomrm-wjZp7wmGjpAyjGcTzpkA=
+  example_title: Person
+- src: >-
+    https://storage.googleapis.com/pai-images/1484fd9ea9d746eb9f1de0d6778dbea2.jpeg
+  example_title: Person
+datasets:
+- sayeed99/fashion_segmentation
+model-index:
+- name: segformer-b3-fashion
+  results: []
+pipeline_tag: image-segmentation
+---
+
+
+# segformer-b3-fashion
+
+This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the sayeed99/fashion_segmentation dataset using original image sizes without resizing.
+
+
+```python
+from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+
+processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer-b3-fashion")
+model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer-b3-fashion")
+
+url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits.cpu()
+
+upsampled_logits = nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],
+    mode="bilinear",
+    align_corners=False,
+)
+
+pred_seg = upsampled_logits.argmax(dim=1)[0]
+plt.imshow(pred_seg)
+```
+
+Labels : {"0":"Unlabelled", "1": "shirt, blouse", "2": "top, t-shirt, sweatshirt", "3": "sweater", "4": "cardigan", "5": "jacket", "6": "vest", "7": "pants", "8": "shorts", "9": "skirt", "10": "coat", "11": "dress", "12": "jumpsuit", "13": "cape", "14": "glasses", "15": "hat", "16": "headband, head covering, hair accessory", "17": "tie", "18": "glove", "19": "watch", "20": "belt", "21": "leg warmer", "22": "tights, stockings", "23": "sock", "24": "shoe", "25": "bag, wallet", "26": "scarf", "27": "umbrella", "28": "hood", "29": "collar", "30": "lapel", "31": "epaulette", "32": "sleeve", "33": "pocket", "34": "neckline", "35": "buckle", "36": "zipper", "37": "applique", "38": "bead", "39": "bow", "40": "flower", "41": "fringe", "42": "ribbon", "43": "rivet", "44": "ruffle", "45": "sequin", "46": "tassel"}
+
+### Framework versions
+
+- Transformers 4.30.0
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.13.3
+
+
+### License
+
+The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2105-15203,
+  author    = {Enze Xie and
+               Wenhai Wang and
+               Zhiding Yu and
+               Anima Anandkumar and
+               Jose M. Alvarez and
+               Ping Luo},
+  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
+               Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2105.15203},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2105.15203},
+  eprinttype = {arXiv},
+  eprint    = {2105.15203},
+  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}

ComfyUI/models/segformer_b3_fashion/config.json

@@ -0,0 +1,168 @@
+{
+  "_name_or_path": "nvidia/mit-b3",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    18,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "unlabelled",
+    "1": "shirt, blouse",
+    "2": "top, t-shirt, sweatshirt",
+    "3": "sweater",
+    "4": "cardigan",
+    "5": "jacket",
+    "6": "vest",
+    "7": "pants",
+    "8": "shorts",
+    "9": "skirt",
+    "10": "coat",
+    "11": "dress",
+    "12": "jumpsuit",
+    "13": "cape",
+    "14": "glasses",
+    "15": "hat",
+    "16": "headband, head covering, hair accessory",
+    "17": "tie",
+    "18": "glove",
+    "19": "watch",
+    "20": "belt",
+    "21": "leg warmer",
+    "22": "tights, stockings",
+    "23": "sock",
+    "24": "shoe",
+    "25": "bag, wallet",
+    "26": "scarf",
+    "27": "umbrella",
+    "28": "hood",
+    "29": "collar",
+    "30": "lapel",
+    "31": "epaulette",
+    "32": "sleeve",
+    "33": "pocket",
+    "34": "neckline",
+    "35": "buckle",
+    "36": "zipper",
+    "37": "applique",
+    "38": "bead",
+    "39": "bow",
+    "40": "flower",
+    "41": "fringe",
+    "42": "ribbon",
+    "43": "rivet",
+    "44": "ruffle",
+    "45": "sequin",
+    "46": "tassel"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "applique": 37,
+    "bag, wallet": 25,
+    "bead": 38,
+    "belt": 20,
+    "bow": 39,
+    "buckle": 35,
+    "cape": 13,
+    "cardigan": 4,
+    "coat": 10,
+    "collar": 29,
+    "dress": 11,
+    "epaulette": 31,
+    "flower": 40,
+    "fringe": 41,
+    "glasses": 14,
+    "glove": 18,
+    "hat": 15,
+    "headband, head covering, hair accessory": 16,
+    "hood": 28,
+    "jacket": 5,
+    "jumpsuit": 12,
+    "lapel": 30,
+    "leg warmer": 21,
+    "neckline": 34,
+    "pants": 7,
+    "pocket": 33,
+    "ribbon": 42,
+    "rivet": 43,
+    "ruffle": 44,
+    "scarf": 26,
+    "sequin": 45,
+    "shirt, blouse": 1,
+    "shoe": 24,
+    "shorts": 8,
+    "skirt": 9,
+    "sleeve": 32,
+    "sock": 23,
+    "sweater": 3,
+    "tassel": 46,
+    "tie": 17,
+    "tights, stockings": 22,
+    "top, t-shirt, sweatshirt": 2,
+    "umbrella": 27,
+    "unlabelled": 0,
+    "vest": 6,
+    "watch": 19,
+    "zipper": 36
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0"
+}

ComfyUI/models/segformer_b3_fashion/model.safetensors

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+size 189118204

ComfyUI/models/segformer_b3_fashion/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_reduce_labels": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "SegformerImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 512,
+    "width": 512
+  }
+}

ComfyUI/models/segformer_b3_fashion/pytorch_model_2.bin

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ComfyUI/models/segformer_b3_fashion/training_args.bin

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