Added HF Model and Space Links for Retinanet & GauGAN (keras-team#964)

* added HF Model and Space Links

* added HF Model and Space Links for GauGAN

* reformatted files using black-jupyter

examples/generative/gaugan.py

@@ -821,3 +821,10 @@ def plot_history(item):
 [here](https://blogs.nvidia.com/blog/2021/11/22/gaugan2-ai-art-demo/).
 
 """
+"""
+Example available on HuggingFace.
+
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/keras-io/GauGAN-Image-generation) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/spaces/keras-io/GauGAN_Conditional_Image_Generation) |
+"""

examples/generative/ipynb/gaugan.ipynb

@@ -1053,6 +1053,19 @@
     "* Recently GauGAN2 was also released. You can check it out\n",
     "[here](https://blogs.nvidia.com/blog/2021/11/22/gaugan2-ai-art-demo/)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "Example available on HuggingFace.\n",
+    "\n",
+    "| Trained Model | Demo |\n",
+    "| :--: | :--: |\n",
+    "| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/keras-io/GauGAN-Image-generation) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/spaces/keras-io/GauGAN_Conditional_Image_Generation) |"
+   ]
   }
  ],
  "metadata": {

examples/generative/md/gaugan.md

@@ -1,15 +1,14 @@
-
 # GauGAN for conditional image generation
 
 **Author:** [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://twitter.com/RisingSayak)<br>
 **Date created:** 2021/12/26<br>
 **Last modified:** 2022/01/03<br>
+**Description:** Implementing a GauGAN for conditional image generation.
 
 
 <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/generative/ipynb/gaugan.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/generative/gaugan.py)
 
 
-**Description:** Implementing a GauGAN for conditional image generation.
 
 ---
 ## Introduction
@@ -87,9 +86,9 @@ TensorFlow Addons.
 ```
 Downloading...
 From: https://drive.google.com/uc?id=1q4FEjQg1YSb4mPx2VdxL7LXKYu3voTMj
-To: /content/keras-io/scripts/tmp_2820468/facades_data.zip
-100% 26.0M/26.0M [00:00<00:00, 261MB/s]
-[K     |████████████████████████████████| 1.1 MB 8.5 MB/s 
+To: /content/keras-io/scripts/tmp_6251400/facades_data.zip
+100% 26.0M/26.0M [00:00<00:00, 69.1MB/s]
+[K     |████████████████████████████████| 1.1 MB 5.0 MB/s 
 [?25h
 
 ```
@@ -844,7 +843,7 @@ Downloading data from https://storage.googleapis.com/tensorflow/keras-applicatio
 Epoch 1/15
 WARNING:tensorflow:Gradients do not exist for variables ['conv2d_6/kernel:0', 'conv2d_7/kernel:0', 'instance_normalization_3/gamma:0', 'instance_normalization_3/beta:0', 'conv2d_8/kernel:0', 'instance_normalization_4/gamma:0', 'instance_normalization_4/beta:0', 'conv2d_9/kernel:0', 'instance_normalization_5/gamma:0', 'instance_normalization_5/beta:0', 'conv2d_10/kernel:0', 'instance_normalization_6/gamma:0', 'instance_normalization_6/beta:0', 'mean/kernel:0', 'mean/bias:0', 'variance/kernel:0', 'variance/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
 WARNING:tensorflow:Gradients do not exist for variables ['conv2d_6/kernel:0', 'conv2d_7/kernel:0', 'instance_normalization_3/gamma:0', 'instance_normalization_3/beta:0', 'conv2d_8/kernel:0', 'instance_normalization_4/gamma:0', 'instance_normalization_4/beta:0', 'conv2d_9/kernel:0', 'instance_normalization_5/gamma:0', 'instance_normalization_5/beta:0', 'conv2d_10/kernel:0', 'instance_normalization_6/gamma:0', 'instance_normalization_6/beta:0', 'mean/kernel:0', 'mean/bias:0', 'variance/kernel:0', 'variance/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
-75/75 [==============================] - ETA: 0s - disc_loss: 1.1359 - gen_loss: 114.6762 - feat_loss: 9.6107 - vgg_loss: 17.5540 - kl_loss: 87.3495
+75/75 [==============================] - ETA: 0s - disc_loss: 1.1523 - gen_loss: 116.4506 - feat_loss: 9.3978 - vgg_loss: 17.5297 - kl_loss: 89.3524
 
 ```
 </div>
@@ -865,17 +864,17 @@ WARNING:tensorflow:Gradients do not exist for variables ['conv2d_6/kernel:0', 'c
 
 <div class="k-default-codeblock">
 ```
-75/75 [==============================] - 69s 620ms/step - disc_loss: 1.1359 - gen_loss: 114.6762 - feat_loss: 9.6107 - vgg_loss: 17.5540 - kl_loss: 87.3495 - val_disc_loss: 0.9339 - val_gen_loss: 115.9361 - val_feat_loss: 12.0053 - val_vgg_loss: 17.6576 - val_kl_loss: 86.4241
+75/75 [==============================] - 108s 1s/step - disc_loss: 1.1523 - gen_loss: 116.4506 - feat_loss: 9.3978 - vgg_loss: 17.5297 - kl_loss: 89.3524 - val_disc_loss: 0.9178 - val_gen_loss: 116.5032 - val_feat_loss: 10.7875 - val_vgg_loss: 17.3193 - val_kl_loss: 88.4352
 Epoch 2/15
-75/75 [==============================] - 39s 522ms/step - disc_loss: 0.9191 - gen_loss: 115.4342 - feat_loss: 11.1384 - vgg_loss: 16.6970 - kl_loss: 87.1299 - val_disc_loss: 0.9073 - val_gen_loss: 117.0431 - val_feat_loss: 10.9293 - val_vgg_loss: 17.3108 - val_kl_loss: 87.1910
+75/75 [==============================] - 77s 1s/step - disc_loss: 0.9191 - gen_loss: 118.7338 - feat_loss: 11.0603 - vgg_loss: 16.6664 - kl_loss: 90.6103 - val_disc_loss: 0.6385 - val_gen_loss: 119.3599 - val_feat_loss: 11.3670 - val_vgg_loss: 17.1393 - val_kl_loss: 90.0082
 Epoch 3/15
-75/75 [==============================] - 40s 530ms/step - disc_loss: 0.7783 - gen_loss: 116.0476 - feat_loss: 11.2018 - vgg_loss: 16.4767 - kl_loss: 87.7456 - val_disc_loss: 0.7877 - val_gen_loss: 115.6750 - val_feat_loss: 11.3406 - val_vgg_loss: 16.9246 - val_kl_loss: 87.8907
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.7655 - gen_loss: 117.5064 - feat_loss: 11.5845 - vgg_loss: 16.4784 - kl_loss: 88.7713 - val_disc_loss: 0.4862 - val_gen_loss: 119.3581 - val_feat_loss: 12.1496 - val_vgg_loss: 16.9515 - val_kl_loss: 89.8430
 Epoch 4/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.6915 - gen_loss: 115.8905 - feat_loss: 10.7578 - vgg_loss: 16.3213 - kl_loss: 88.0270 - val_disc_loss: 0.7651 - val_gen_loss: 115.5427 - val_feat_loss: 11.5930 - val_vgg_loss: 17.0086 - val_kl_loss: 87.3675
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.6624 - gen_loss: 118.3517 - feat_loss: 11.3209 - vgg_loss: 16.3818 - kl_loss: 89.8082 - val_disc_loss: 0.5079 - val_gen_loss: 120.7913 - val_feat_loss: 11.5181 - val_vgg_loss: 17.0514 - val_kl_loss: 91.6182
 Epoch 5/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.6652 - gen_loss: 115.3557 - feat_loss: 10.7736 - vgg_loss: 16.3333 - kl_loss: 87.4493 - val_disc_loss: 0.9139 - val_gen_loss: 115.3157 - val_feat_loss: 11.3612 - val_vgg_loss: 17.0591 - val_kl_loss: 87.6537
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.6675 - gen_loss: 117.6569 - feat_loss: 11.1012 - vgg_loss: 16.3577 - kl_loss: 89.3192 - val_disc_loss: 0.8371 - val_gen_loss: 117.0856 - val_feat_loss: 11.8800 - val_vgg_loss: 16.9238 - val_kl_loss: 88.9101
 Epoch 6/15
-75/75 [==============================] - ETA: 0s - disc_loss: 0.6541 - gen_loss: 115.2529 - feat_loss: 10.6386 - vgg_loss: 16.2342 - kl_loss: 87.5053
+75/75 [==============================] - ETA: 0s - disc_loss: 0.6380 - gen_loss: 118.0546 - feat_loss: 10.9940 - vgg_loss: 16.3220 - kl_loss: 89.8497
 
 ```
 </div>
@@ -896,17 +895,17 @@ Epoch 6/15
 
 <div class="k-default-codeblock">
 ```
-75/75 [==============================] - 43s 573ms/step - disc_loss: 0.6541 - gen_loss: 115.2529 - feat_loss: 10.6386 - vgg_loss: 16.2342 - kl_loss: 87.5053 - val_disc_loss: 0.3999 - val_gen_loss: 116.1638 - val_feat_loss: 11.1031 - val_vgg_loss: 16.9759 - val_kl_loss: 87.5684
+75/75 [==============================] - 84s 1s/step - disc_loss: 0.6380 - gen_loss: 118.0546 - feat_loss: 10.9940 - vgg_loss: 16.3220 - kl_loss: 89.8497 - val_disc_loss: 0.5685 - val_gen_loss: 117.1406 - val_feat_loss: 10.9653 - val_vgg_loss: 16.9390 - val_kl_loss: 89.1890
 Epoch 7/15
-75/75 [==============================] - 40s 530ms/step - disc_loss: 0.6029 - gen_loss: 115.3866 - feat_loss: 10.6807 - vgg_loss: 16.2025 - kl_loss: 87.5448 - val_disc_loss: 0.4571 - val_gen_loss: 117.5491 - val_feat_loss: 10.7403 - val_vgg_loss: 16.8212 - val_kl_loss: 88.4036
+75/75 [==============================] - 81s 1s/step - disc_loss: 0.6146 - gen_loss: 117.7761 - feat_loss: 10.7700 - vgg_loss: 16.2307 - kl_loss: 89.8057 - val_disc_loss: 0.5274 - val_gen_loss: 119.1055 - val_feat_loss: 10.7547 - val_vgg_loss: 16.8180 - val_kl_loss: 90.3080
 Epoch 8/15
-75/75 [==============================] - 39s 522ms/step - disc_loss: 0.5798 - gen_loss: 115.1903 - feat_loss: 10.5906 - vgg_loss: 16.1720 - kl_loss: 87.4315 - val_disc_loss: 0.4470 - val_gen_loss: 114.3039 - val_feat_loss: 10.8104 - val_vgg_loss: 16.9426 - val_kl_loss: 86.1938
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.6255 - gen_loss: 117.6616 - feat_loss: 10.7103 - vgg_loss: 16.2302 - kl_loss: 89.7942 - val_disc_loss: 0.5411 - val_gen_loss: 116.6601 - val_feat_loss: 11.2193 - val_vgg_loss: 17.0372 - val_kl_loss: 88.2279
 Epoch 9/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.5412 - gen_loss: 115.6245 - feat_loss: 10.5598 - vgg_loss: 16.1985 - kl_loss: 87.8032 - val_disc_loss: 0.3365 - val_gen_loss: 116.6229 - val_feat_loss: 10.9437 - val_vgg_loss: 16.9026 - val_kl_loss: 87.6305
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.6166 - gen_loss: 117.8108 - feat_loss: 10.6085 - vgg_loss: 16.2162 - kl_loss: 89.9843 - val_disc_loss: 0.4528 - val_gen_loss: 117.9378 - val_feat_loss: 10.7798 - val_vgg_loss: 16.7823 - val_kl_loss: 89.9090
 Epoch 10/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.5822 - gen_loss: 115.2743 - feat_loss: 10.5127 - vgg_loss: 16.1609 - kl_loss: 87.5487 - val_disc_loss: 0.4711 - val_gen_loss: 116.2798 - val_feat_loss: 11.4271 - val_vgg_loss: 16.7643 - val_kl_loss: 87.5068
+75/75 [==============================] - 81s 1s/step - disc_loss: 0.5717 - gen_loss: 117.3905 - feat_loss: 10.6555 - vgg_loss: 16.0916 - kl_loss: 89.6282 - val_disc_loss: 0.5962 - val_gen_loss: 115.7530 - val_feat_loss: 10.7713 - val_vgg_loss: 16.7657 - val_kl_loss: 88.1683
 Epoch 11/15
-75/75 [==============================] - ETA: 0s - disc_loss: 0.5588 - gen_loss: 115.1784 - feat_loss: 10.5053 - vgg_loss: 16.0580 - kl_loss: 87.5601
+75/75 [==============================] - ETA: 0s - disc_loss: 0.5847 - gen_loss: 117.7846 - feat_loss: 10.8056 - vgg_loss: 16.0719 - kl_loss: 89.9362
 
 ```
 </div>
@@ -927,15 +926,15 @@ Epoch 11/15
 
 <div class="k-default-codeblock">
 ```
-75/75 [==============================] - 43s 571ms/step - disc_loss: 0.5588 - gen_loss: 115.1784 - feat_loss: 10.5053 - vgg_loss: 16.0580 - kl_loss: 87.5601 - val_disc_loss: 0.7086 - val_gen_loss: 116.6372 - val_feat_loss: 11.6103 - val_vgg_loss: 16.9735 - val_kl_loss: 88.3342
+75/75 [==============================] - 85s 1s/step - disc_loss: 0.5847 - gen_loss: 117.7846 - feat_loss: 10.8056 - vgg_loss: 16.0719 - kl_loss: 89.9362 - val_disc_loss: 0.4893 - val_gen_loss: 118.1462 - val_feat_loss: 11.4571 - val_vgg_loss: 16.7712 - val_kl_loss: 89.5385
 Epoch 12/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.5461 - gen_loss: 115.2417 - feat_loss: 10.5498 - vgg_loss: 16.0916 - kl_loss: 87.5209 - val_disc_loss: 0.5056 - val_gen_loss: 116.8908 - val_feat_loss: 10.3318 - val_vgg_loss: 16.8074 - val_kl_loss: 87.9361
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.5382 - gen_loss: 117.5233 - feat_loss: 10.6898 - vgg_loss: 16.0712 - kl_loss: 89.7246 - val_disc_loss: 1.1371 - val_gen_loss: 115.7767 - val_feat_loss: 11.3614 - val_vgg_loss: 16.8377 - val_kl_loss: 88.8200
 Epoch 13/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.5482 - gen_loss: 114.9858 - feat_loss: 10.3671 - vgg_loss: 16.0029 - kl_loss: 87.5513 - val_disc_loss: 0.5819 - val_gen_loss: 116.6370 - val_feat_loss: 11.3866 - val_vgg_loss: 17.0710 - val_kl_loss: 88.0975
+75/75 [==============================] - 80s 1s/step - disc_loss: 0.5503 - gen_loss: 117.2452 - feat_loss: 10.6088 - vgg_loss: 16.0694 - kl_loss: 89.4459 - val_disc_loss: 0.5598 - val_gen_loss: 117.8204 - val_feat_loss: 10.1941 - val_vgg_loss: 16.8888 - val_kl_loss: 88.7975
 Epoch 14/15
-75/75 [==============================] - 39s 522ms/step - disc_loss: 0.5596 - gen_loss: 114.5251 - feat_loss: 10.3841 - vgg_loss: 16.0361 - kl_loss: 86.9755 - val_disc_loss: 0.4472 - val_gen_loss: 115.9854 - val_feat_loss: 10.2750 - val_vgg_loss: 16.9934 - val_kl_loss: 87.2017
+75/75 [==============================] - 79s 1s/step - disc_loss: 0.5454 - gen_loss: 117.2473 - feat_loss: 10.5061 - vgg_loss: 16.0546 - kl_loss: 89.5726 - val_disc_loss: 0.5854 - val_gen_loss: 118.8546 - val_feat_loss: 10.1710 - val_vgg_loss: 16.7827 - val_kl_loss: 90.1311
 Epoch 15/15
-75/75 [==============================] - 39s 521ms/step - disc_loss: 0.5417 - gen_loss: 114.3627 - feat_loss: 10.1977 - vgg_loss: 15.9871 - kl_loss: 87.1026 - val_disc_loss: 0.4202 - val_gen_loss: 115.1249 - val_feat_loss: 10.2537 - val_vgg_loss: 16.8011 - val_kl_loss: 86.5379
+75/75 [==============================] - 81s 1s/step - disc_loss: 0.5522 - gen_loss: 117.1168 - feat_loss: 10.3688 - vgg_loss: 15.9824 - kl_loss: 89.6849 - val_disc_loss: 0.6948 - val_gen_loss: 119.7691 - val_feat_loss: 9.7619 - val_vgg_loss: 16.7731 - val_kl_loss: 91.1593
 
 ```
 </div>
@@ -1033,3 +1032,9 @@ this example) on a bigger dataset and then make the repository public. We welcom
 contributions!
 * Recently GauGAN2 was also released. You can check it out
 [here](https://blogs.nvidia.com/blog/2021/11/22/gaugan2-ai-art-demo/).
+
+Example available on HuggingFace.
+
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/keras-io/GauGAN-Image-generation) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-GauGAN%20Image%20Generation-black.svg)](https://huggingface.co/spaces/keras-io/GauGAN_Conditional_Image_Generation) |

examples/vision/ipynb/retinanet.ipynb

@@ -1293,6 +1293,19 @@
     "        detections.nmsed_scores[0][:num_detections],\n",
     "    )"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "Example available on HuggingFace.\n",
+    "\n",
+    "| Trained Model | Demo |\n",
+    "| :--: | :--: |\n",
+    "| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/keras-io/Object-Detection-RetinaNet) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/spaces/keras-io/Object-Detection-Using-RetinaNet) |"
+   ]
   }
  ],
  "metadata": {

examples/vision/md/retinanet.md

@@ -67,7 +67,8 @@ with zipfile.ZipFile("data.zip", "r") as z_fp:
 <div class="k-default-codeblock">
 ```
 Downloading data from https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip
-560529408/560525318 [==============================] - 304s 1us/step
+560529408/560525318 [==============================] - 7s 0us/step
+560537600/560525318 [==============================] - 7s 0us/step
 
 ```
 </div>
@@ -887,6 +888,14 @@ optimizer = tf.optimizers.SGD(learning_rate=learning_rate_fn, momentum=0.9)
 model.compile(loss=loss_fn, optimizer=optimizer)
 ```
 
+<div class="k-default-codeblock">
+```
+Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
+94773248/94765736 [==============================] - 0s 0us/step
+94781440/94765736 [==============================] - 0s 0us/step
+
+```
+</div>
 ---
 ## Setting up callbacks
 
@@ -981,11 +990,11 @@ model.fit(
 
 <div class="k-default-codeblock">
 ```
-100/100 [==============================] - ETA: 0s - loss: 4.0953
-Epoch 00001: saving model to retinanet/weights_epoch_1
-100/100 [==============================] - 68s 679ms/step - loss: 4.0953 - val_loss: 4.0821
+    100/Unknown - 290s 3s/step - loss: 4.0817
+Epoch 1: saving model to retinanet/weights_epoch_1
+100/100 [==============================] - 336s 3s/step - loss: 4.0817 - val_loss: 4.1082
 
-<tensorflow.python.keras.callbacks.History at 0x7f87005239d0>
+<keras.callbacks.History at 0x7f4c7e0428d0>
 
 ```
 </div>
@@ -1006,7 +1015,7 @@ model.load_weights(latest_checkpoint)
 
 <div class="k-default-codeblock">
 ```
-<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f86e0531910>
+<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f4c6823d0d0>
 
 ```
 </div>
@@ -1059,3 +1068,9 @@ for sample in val_dataset.take(2):
 
 ![png](/img/examples/vision/retinanet/retinanet_44_1.png)
 
+
+Example available on HuggingFace.
+
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/keras-io/Object-Detection-RetinaNet) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/spaces/keras-io/Object-Detection-Using-RetinaNet) |

examples/vision/retinanet.py

@@ -977,3 +977,11 @@ def prepare_image(image):
         class_names,
         detections.nmsed_scores[0][:num_detections],
     )
+
+"""
+Example available on HuggingFace.
+
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/keras-io/Object-Detection-RetinaNet) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Object%20Detection%20With%20Retinanet-black.svg)](https://huggingface.co/spaces/keras-io/Object-Detection-Using-RetinaNet) |
+"""