Add documented support for image-segmentation task

Author: xenova

README.md

@@ -6,7 +6,7 @@
 [![license](https://img.shields.io/github/license/xenova/transformers.js)](https://github.com/xenova/transformers.js/blob/main/LICENSE)
 
 
-Run 🤗 Transformers in your browser! We currently support [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [T5](https://huggingface.co/docs/transformers/model_doc/t5), [T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1), [FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5), [mT5](https://huggingface.co/docs/transformers/model_doc/mt5), [BART](https://huggingface.co/docs/transformers/model_doc/bart), [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian), [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen), [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper), [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), [Vision Transformer](https://huggingface.co/docs/transformers/model_doc/vit), [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder), and [DETR](https://huggingface.co/docs/transformers/model_doc/detr) models, for a variety of tasks including: masked language modelling, text classification, token classification, zero-shot classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, image-to-text, and object detection.
+Run 🤗 Transformers in your browser! We currently support [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [T5](https://huggingface.co/docs/transformers/model_doc/t5), [T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1), [FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5), [mT5](https://huggingface.co/docs/transformers/model_doc/mt5), [BART](https://huggingface.co/docs/transformers/model_doc/bart), [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian), [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen), [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper), [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), [Vision Transformer](https://huggingface.co/docs/transformers/model_doc/vit), [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder), and [DETR](https://huggingface.co/docs/transformers/model_doc/detr) models, for a variety of tasks including: masked language modelling, text classification, token classification, zero-shot classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, image-to-text, image segmentation, and object detection.
 
 ![teaser](https://user-images.githubusercontent.com/26504141/221056008-e906614e-e6f0-4e10-b0a8-7d5c99e955b4.gif)
 

index.html

@@ -144,6 +144,7 @@ <h2 class="fw-bolder">Demo</h2>
                         <option value="zero-shot-image-classification">
                             Zero-shot image classification w/ openai/clip-vit-base-patch16 (152 MB)
                         </option>
+                        <!-- TODO: add image-segmentation demo -->
                         <option value="object-detection">
                             Object detection w/ facebook/detr-resnet-50 (43 MB)
                         </option>
@@ -826,6 +827,16 @@ <h2 class="fw-bolder">Usage</h2>
                                 Zero-Shot Image Classification</a>.
                         </div>
                     </li>
+                    <li class="list-group-item d-flex justify-content-between align-items-start">
+                        <div class="ms-2 me-auto">
+                            <div class="fw-bold">image-segmentation</div>
+                            Supported models:
+                            <code>facebook/detr-resnet-50-panoptic</code>.
+                            For more information, check out the
+                            <a href="https://huggingface.co/tasks/image-segmentation">
+                                Image segmentation docs</a>.
+                        </div>
+                    </li>
                     <li class="list-group-item d-flex justify-content-between align-items-start">
                         <div class="ms-2 me-auto">
                             <div class="fw-bold">object-detection</div>

package.json

@@ -1,7 +1,7 @@
 {
   "name": "@xenova/transformers",
   "version": "1.4.1",
-  "description": "Run 🤗 Transformers in your browser! We currently support BERT, ALBERT, DistilBERT, MobileBERT, SqueezeBERT, T5, T5v1.1, FLAN-T5, mT5, BART, MarianMT, GPT2, GPT Neo, CodeGen, Whisper, CLIP, Vision Transformer, VisionEncoderDecoder, and DETR models, for a variety of tasks including: masked language modelling, text classification, token classification, zero-shot classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, image-to-text, and object detection.",
+  "description": "Run 🤗 Transformers in your browser! We currently support BERT, ALBERT, DistilBERT, MobileBERT, SqueezeBERT, T5, T5v1.1, FLAN-T5, mT5, BART, MarianMT, GPT2, GPT Neo, CodeGen, Whisper, CLIP, Vision Transformer, VisionEncoderDecoder, and DETR models, for a variety of tasks including: masked language modelling, text classification, token classification, zero-shot classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, image-to-text, image segmentation, and object detection.",
   "main": "./src/transformers.js",
   "directories": {
     "test": "tests"