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segformer-b3-fashion/.gitignore

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+checkpoint-*/

segformer-b3-fashion/README.md

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+---
+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}
+}

segformer-b3-fashion/config.json

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+{
+  "_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"
+}

segformer-b3-fashion/model.safetensors

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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
+  }
+}

segformer-b3-fashion/pytorch_model.bin

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segformer-b3-fashion/training_args.bin

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