clone from https://huggingface.co/shehan97/mobilevitv2-1.0-voc-deeplabv3

README.md

@@ -1,3 +1,66 @@
 ---
 license: other
+library_name: transformers
+tags:
+- vision
+- image-segmentation
 ---
+
+# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)
+
+<!-- Provide a quick summary of what the model is/does. -->
+MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
+It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari, and first released in [this](https://github.com/apple/ml-cvnets) repository. The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
+
+Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+MobileViTv2 is constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
+
+The model in this repo adds a [DeepLabV3](https://arxiv.org/abs/1706.05587) head to the MobileViT backbone for semantic segmentation.
+
+### Intended uses & limitations
+
+You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevitv2) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import MobileViTv2FeatureExtractor, MobileViTv2ForSemanticSegmentation
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+feature_extractor = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
+model = MobileViTv2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
+
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits
+
+predicted_mask = logits.argmax(1).squeeze(0)
+```
+
+Currently, both the feature extractor and model support PyTorch.
+
+## Training data
+
+The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{vision-transformer,
+title = {Separable Self-attention for Mobile Vision Transformers},
+author = {Sachin Mehta and Mohammad Rastegari},
+year = {2022},
+URL = {https://arxiv.org/abs/2206.02680}
+}
+```

config.json

@@ -0,0 +1,76 @@
+{
+  "architectures": [
+    "MobileViTv2ForSemanticSegmentation"
+  ],
+  "aspp_dropout_prob": 0.1,
+  "aspp_out_channels": 512,
+  "atrous_rates": [
+    6,
+    12,
+    18
+  ],
+  "attn_dropout": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "conv_kernel_size": 3,
+  "expand_ratio": 2.0,
+  "ffn_dropout": 0.0,
+  "hidden_act": "swish",
+  "id2label": {
+    "0": "background",
+    "1": "aeroplane",
+    "2": "bicycle",
+    "3": "bird",
+    "4": "boat",
+    "5": "bottle",
+    "6": "bus",
+    "7": "car",
+    "8": "cat",
+    "9": "chair",
+    "10": "cow",
+    "11": "diningtable",
+    "12": "dog",
+    "13": "horse",
+    "14": "motorbike",
+    "15": "person",
+    "16": "pottedplant",
+    "17": "sheep",
+    "18": "sofa",
+    "19": "train",
+    "20": "tvmonitor"
+  },
+  "image_size": 512,
+  "initializer_range": 0.02,
+  "label2id": {
+    "aeroplane": 1,
+    "background": 0,
+    "bicycle": 2,
+    "bird": 3,
+    "boat": 4,
+    "bottle": 5,
+    "bus": 6,
+    "car": 7,
+    "cat": 8,
+    "chair": 9,
+    "cow": 10,
+    "diningtable": 11,
+    "dog": 12,
+    "horse": 13,
+    "motorbike": 14,
+    "person": 15,
+    "pottedplant": 16,
+    "sheep": 17,
+    "sofa": 18,
+    "train": 19,
+    "tvmonitor": 20
+  },
+  "layer_norm_eps": 1e-05,
+  "mlp_ratio": 2.0,
+  "model_type": "mobilevitv2",
+  "num_channels": 3,
+  "output_stride": 16,
+  "patch_size": 2,
+  "semantic_loss_ignore_index": 255,
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.0.dev0",
+  "width_multiplier": 1.0
+}

preprocessor_config.json

@@ -0,0 +1,16 @@
+{
+  "crop_size": {
+    "height": 512,
+    "width": 512
+  },
+  "do_center_crop": true,
+  "do_flip_channel_order": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_processor_type": "MobileViTv2ImageProcessor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 544
+  }
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3de4592cb143dd4eb10e4c031e3a7c6db4e626abcb41599899ab8c98a68305d3
+size 53468241