Update README.md

- Add TF weights (d474213c2b02d17db3fa814f371fe6fdb14d3929)

README.md

@@ -0,0 +1,75 @@
+---
+license: other
+tags:
+- vision
+- image-segmentation
+datasets:
+- cityscapes
+widget:
+- src: https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png
+  example_title: Road
+---
+
+# SegFormer (b5-sized) model fine-tuned on CityScapes
+
+SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). 
+
+Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
+
+## Intended uses & limitations
+
+You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
+from PIL import Image
+import requests
+
+feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b5-finetuned-cityscapes-1024-1024")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-cityscapes-1024-1024")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
+
+### 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}
+}
+```

config.json

@@ -1,6 +1,6 @@
 {
   "architectures": [
-    "SegformerForImageSegmentation"
+    "SegformerForSemanticSegmentation"
   ],
   "attention_probs_dropout_prob": 0.0,
   "classifier_dropout_prob": 0.1,

preprocessor_config.json

@@ -1,12 +1,5 @@
 {
-  "align": false,
-  "crop_size": [
-    512,
-    512
-  ],
   "do_normalize": true,
-  "do_pad": true,
-  "do_random_crop": false,
   "do_resize": true,
   "feature_extractor_type": "SegformerFeatureExtractor",
   "image_mean": [
@@ -14,19 +7,12 @@
     0.456,
     0.406
   ],
-  "image_scale": [
-    512,
-    512
-  ],
   "image_std": [
     0.229,
     0.224,
     0.225
   ],
-  "keep_ratio": false,
-  "padding_value": 0,
-  "reduce_zero_label": false,
+  "reduce_labels": false,
   "resample": 2,
-  "segmentation_padding_value": -100,
-  "size_divisor": 32
+  "size": 1024
 }