 What is an API that can be used to classify sports activities in videos?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Identify an API capable of converting spoken language in a recording to text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'RoBERTa\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/fairseq\', model=\'roberta.large\', pretrained=True)", \'api_arguments\': ["\'pytorch/fairseq\'", "\'roberta.large\'"], \'python_environment_requirements\': [\'regex\', \'requests\', \'hydra-core\', \'omegaconf\'], \'example_code\': [\'import torch\', "roberta = torch.hub.load(\'pytorch/fairseq\', \'roberta.large\')", \'roberta.eval()\', "tokens = roberta.encode(\'Hello world!\')", \'last_layer_features = roberta.extract_features(tokens)\', \'all_layers = roberta.extract_features(tokens, return_all_hiddens=True)\', "roberta = torch.hub.load(\'pytorch/fairseq\', \'roberta.large.mnli\')", \'roberta.eval()\', "tokens = roberta.encode(\'Roberta is a heavily optimized version of BERT.\', \'Roberta is not very optimized.\')", "prediction = roberta.predict(\'mnli\', tokens).argmax().item()", "tokens = roberta.encode(\'Roberta is a heavily optimized version of BERT.\', \'Roberta is based on BERT.\')", "prediction = roberta.predict(\'mnli\', tokens).argmax().item()", "roberta.register_classification_head(\'new_task\', num_classes=3)", "logprobs = roberta.predict(\'new_task\', tokens)"], \'performance\': {\'dataset\': \'MNLI\', \'accuracy\': \'N/A\'}, \'description\': "RoBERTa is a robustly optimized version of BERT, a revolutionary self-supervised pretraining technique that learns to predict intentionally hidden (masked) sections of text. RoBERTa builds on BERT\'s language masking strategy and modifies key hyperparameters, including removing BERT\'s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time, allowing it to generalize even better to downstream tasks."}', metadata={})]


 To analyze street photos, I need to segment different objects like pedestrians, vehicles, and buildings from a given image. Provide an API able to perform semantic segmentation on images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 To implement a lightweight object detection, I'm looking for a pre-trained model API that can detect and classify objects within an image in real-time.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 I need an image classification API that can handle millions of public images with thousands of hashtags. Please recommend one.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 Developers of a Virtual Reality event want to create a realistic digital crowd. Can you suggest a pretrained model to generate faces of celebrities?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-to-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Speech Synthesis\', \'api_name\': \'WaveGlow\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_waveglow\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'NVIDIA/DeepLearningExamples:torchhub\', \'model\': \'nvidia_waveglow\', \'model_math\': \'fp32\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': {\'load_waveglow_model\': "waveglow = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_waveglow\', model_math=\'fp32\')", \'prepare_waveglow_model\': [\'waveglow = waveglow.remove_weightnorm(waveglow)\', "waveglow = waveglow.to(\'cuda\')", \'waveglow.eval()\'], \'load_tacotron2_model\': "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp32\')", \'prepare_tacotron2_model\': ["tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\'], \'synthesize_speech\': [\'text = "hello world, I missed you so much"\', "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'save_audio\': \'write("audio.wav", rate, audio_numpy)\', \'play_audio\': \'Audio(audio_numpy, rate=rate)\'}, \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': None}, \'description\': \'The Tacotron 2 and WaveGlow model form a text-to-speech system that enables users to synthesize natural-sounding speech from raw transcripts without any additional prosody information. The Tacotron 2 model produces mel spectrograms from input text using encoder-decoder architecture. WaveGlow is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 I need an API to classify images from a dataset with a high accuracy rate. Provide an appropriate API and the performance on the ImageNet dataset.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A tourism website is building a feature to categorize photos into classes of landmarks. Recommend a machine learning API that will take an image and output which class the image falls into.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 A photographer at National Geographic is finding photos for the monthly magazine cover. They need a model to classify a picture of a cheetah running in the wild from other images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'HarDNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'PingoLH/Pytorch-HarDNet\', model=\'hardnet68\', pretrained=True)", \'api_arguments\': [{\'name\': \'hardnet68\', \'type\': \'str\', \'description\': \'HarDNet-68 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'PingoLH/Pytorch-HarDNet\', \'hardnet68\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'hardnet68\': {\'Top-1 error\': 23.52, \'Top-5 error\': 6.99}}}, \'description\': \'Harmonic DenseNet (HarDNet) is a low memory trafficCNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet).\'}', metadata={})]


 DXmart needs to build a product image classification system for their e-commerce site. Provide an API that can classify product images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 Identify an API to perform efficient animal classification from user provided images without sacrificing model accuracy for a biodiversity conservation project.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SqueezeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'squeezenet1_1\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_1\'], \'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'squeezenet1_1\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'squeezenet1_1\': {\'Top-1 error\': 41.81, \'Top-5 error\': 19.38}}}, \'description\': \'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\'}', metadata={})]


 Recommend an API to build an Image Classifier that would better classify images with minimal computational resources.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'RoBERTa\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/fairseq\', model=\'roberta.large\', pretrained=True)", \'api_arguments\': ["\'pytorch/fairseq\'", "\'roberta.large\'"], \'python_environment_requirements\': [\'regex\', \'requests\', \'hydra-core\', \'omegaconf\'], \'example_code\': [\'import torch\', "roberta = torch.hub.load(\'pytorch/fairseq\', \'roberta.large\')", \'roberta.eval()\', "tokens = roberta.encode(\'Hello world!\')", \'last_layer_features = roberta.extract_features(tokens)\', \'all_layers = roberta.extract_features(tokens, return_all_hiddens=True)\', "roberta = torch.hub.load(\'pytorch/fairseq\', \'roberta.large.mnli\')", \'roberta.eval()\', "tokens = roberta.encode(\'Roberta is a heavily optimized version of BERT.\', \'Roberta is not very optimized.\')", "prediction = roberta.predict(\'mnli\', tokens).argmax().item()", "tokens = roberta.encode(\'Roberta is a heavily optimized version of BERT.\', \'Roberta is based on BERT.\')", "prediction = roberta.predict(\'mnli\', tokens).argmax().item()", "roberta.register_classification_head(\'new_task\', num_classes=3)", "logprobs = roberta.predict(\'new_task\', tokens)"], \'performance\': {\'dataset\': \'MNLI\', \'accuracy\': \'N/A\'}, \'description\': "RoBERTa is a robustly optimized version of BERT, a revolutionary self-supervised pretraining technique that learns to predict intentionally hidden (masked) sections of text. RoBERTa builds on BERT\'s language masking strategy and modifies key hyperparameters, including removing BERT\'s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time, allowing it to generalize even better to downstream tasks."}', metadata={})]


 I need to recognize dogs and cats from images. What API should I use to perform this task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 I need a suitable PyTorch API that can classify a wide range of images. Please provide me with instructions on how to load the pretrained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 I need to build an image classifier to identify objects in a photo. Suggest a suitable model that I can use for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A developer is building a mobile app to identify objects using the mobile camera. Suggest an API to classify object types given an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A wildlife organization is looking to classify photos taken on their CCTV cameras into 100 different animal species. Suggest an API to achieve this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SNNMLP\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/Efficient-AI-Backbones\', model=\'snnmlp_b\', pretrained=True)", \'api_arguments\': [{\'name\': \'snnmlp_b\', \'type\': \'str\', \'description\': \'SNNMLP Base model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/Efficient-AI-Backbones\', \'snnmlp_b\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'print(torch.nn.functional.softmax(output[0], dim=0))\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'SNNMLP Base\', \'top-1\': 85.59}}, \'description\': \'SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively.\'}', metadata={})]


 A self-driving car company is developing an autonomous vehicle that requires detecting objects, drivable area segmentation, and lane detection in real-time. Suggest an appropriate API for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I want an ML library that can determine the object distances in a photo without inputting more than one photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 I would like a simple method to turn spoken user commands into text, which AI API would you recommend?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 Write me an API to use as a pretrained model for classifying images into categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Semi-supervised and semi-weakly supervised ImageNet Models\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/semi-supervised-ImageNet1K-models\', model=\'resnet18_swsl\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/semi-supervised-ImageNet1K-models\', \'model\': \'resnet18_swsl\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/semi-supervised-ImageNet1K-models\', \'resnet18_swsl\', pretrained=True)", \'model.eval()\'], \'performance\': {\'description\': \'Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.\'}, \'description\': "Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the \'Billion scale semi-supervised learning for image classification\' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library."}', metadata={})]


 A company wants to segment objects in the images for its e-commerce website. Give an API that can segment objects in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 I'm working on a medical app and I want to classify images of skin lesions. Show me an API that can classify images with high efficiency and accuracy.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 What is an API that can classify an image of a dog into its specific breed from a list of 120 unique breeds?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 Can you give me an API that can classify food dishes in restaurant menus using image classification?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 For my mobile app, I need an efficient and light-weight model that can classify animals, plants, landmarks, etc. in an image fed via the device's camera. Suggest an API.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 For a wildlife photography website, suggest an API that can classify the animal species in a given photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 Please suggest an API that can detect and count the number of birds in an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Identify an API that can classify images and works with spiking neural networks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 What is an efficient API that can be used to categorize images and has a much lighter model with fewer parameters than AlexNet?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SqueezeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'squeezenet1_0\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_0\'], \'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'squeezenet1_0\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'squeezenet1_0\': {\'Top-1 error\': 41.9, \'Top-5 error\': 19.58}}}, \'description\': \'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\'}', metadata={})]


 Find me an API which will help identifying animals in a given image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Fine-grained image classifier\', \'api_name\': \'ntsnet\', \'api_call\': "torch.hub.load(repo_or_dir=\'nicolalandro/ntsnet-cub200\', model=\'ntsnet\', pretrained=True, **{\'topN\': 6, \'device\':\'cpu\', \'num_classes\': 200})", \'api_arguments\': {\'pretrained\': \'True\', \'topN\': \'6\', \'device\': \'cpu\', \'num_classes\': \'200\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': "from torchvision import transforms\\nimport torch\\nimport urllib\\nfrom PIL import Image\\n\\ntransform_test = transforms.Compose([\\n transforms.Resize((600, 600), Image.BILINEAR),\\n transforms.CenterCrop((448, 448)),\\n transforms.ToTensor(),\\n transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\\n])\\n\\nmodel = torch.hub.load(\'nicolalandro/ntsnet-cub200\', \'ntsnet\', pretrained=True, **{\'topN\': 6, \'device\':\'cpu\', \'num_classes\': 200})\\nmodel.eval()\\n\\nurl = \'https://raw.githubusercontent.com/nicolalandro/ntsnet-cub200/master/images/nts-net.png\'\\nimg = Image.open(urllib.request.urlopen(url))\\nscaled_img = transform_test(img)\\ntorch_images = scaled_img.unsqueeze(0)\\n\\nwith torch.no_grad():\\n top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images)\\n\\n_, predict = torch.max(concat_logits, 1)\\npred_id = predict.item()\\nprint(\'bird class:\', model.bird_classes[pred_id])", \'performance\': {\'dataset\': \'CUB200 2011\', \'accuracy\': \'Not provided\'}, \'description\': \'This is an nts-net pretrained with CUB200 2011 dataset, which is a fine-grained dataset of birds species.\'}', metadata={})]


 My company is building a chatbot for a car dealership and we need a machine learning model that can classify cars from images. Can you suggest one?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A wildlife conservationist wants to classify animals in their natural habitat with a high accuracy. Recommend an API that can assist in this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A software engineer working at a computer vision company is looking for a model that can classify images efficiently on NVIDIA GPUs. Provide an API recommendation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Recommend an API to translate an English ebook to French.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MobileNet v2\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'mobilenet_v2\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision\', \'mobilenet_v2\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 28.12, \'top-5_error\': 9.71}}, \'description\': \'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\'}', metadata={})]


 In an attempt to streamline content moderation, Facebook is implementing an AI-enabled tool to identify potentially inappropriate images. Suggest an API that can recognize objects within an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 The weatherman needs an AI which could read out the daily weather information. Tell me an API that generates spoken weather information from a written weather forecast.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 A developer needs to classify images using a model that does not require additional tricks for high accuracy. Recommend an API with a high top-1 accuracy without using any tricks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 I need an API that can help me identify the type of a cucumber. It should be able to tell me whether it's pickling, slicing, or burpless cucumber.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 I need to develop a self-driving car which can simultaneously recognize objects, drivable areas, and lanes. Recommend me an API to handle these tasks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I'd like to detect voice activity in an audio file. What API can help me perform this task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 We wish to create an app to make coloring books from images. Recommend an API to extract the regions that should be colored.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Imagine you were given a set of images and you need to tell what objects are on the pictures. Indicate an API that can classify the objects in the images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 My friend recommended the Densenet-201 model to classify images. Find an API that I can use for this model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Provide me with an API that can segment objects within an image into separate categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Looking for a fast and efficient image classification API to suit my low-end device. What would you recommend?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Generative Adversarial Networks\', \'api_name\': \'DCGAN\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorch_GAN_zoo:hub\', model=\'DCGAN\', pretrained=True, useGPU=use_gpu)", \'api_arguments\': {\'pretrained\': \'True\', \'useGPU\': \'use_gpu\'}, \'python_environment_requirements\': \'Python 3\', \'example_code\': {\'import\': [\'import torch\', \'import matplotlib.pyplot as plt\', \'import torchvision\'], \'use_gpu\': \'use_gpu = True if torch.cuda.is_available() else False\', \'load_model\': "model = torch.hub.load(\'facebookresearch/pytorch_GAN_zoo:hub\', \'DCGAN\', pretrained=True, useGPU=use_gpu)", \'build_noise_data\': \'noise, _ = model.buildNoiseData(num_images)\', \'generate_images\': \'with torch.no_grad(): generated_images = model.test(noise)\', \'plot_images\': [\'plt.imshow(torchvision.utils.make_grid(generated_images).permute(1, 2, 0).cpu().numpy())\', \'plt.show()\']}, \'performance\': {\'dataset\': \'FashionGen\', \'accuracy\': \'N/A\'}, \'description\': \'DCGAN is a model designed in 2015 by Radford et. al. in the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. It is a GAN architecture both very simple and efficient for low resolution image generation (up to 64x64).\'}', metadata={})]


 I need a model that can help identify which domain an image belongs to, such as artistic style or natural scenery. Recommend me an API that can do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 I want to know which dog breed a given image belongs to. Tell me an API that is capable of identifying dog breeds.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 I need to classify images into various categories based on their content. Can you suggest an API that can do this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 Recommend an API to automatically fine-tune a neural network's architecture for optimal performance on a specific graphics processing unit (GPU) platform.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A software engineer is trying to determine if an image contains a dog, cat or a horse. Identify an API that could be fine-tuned to achieve the objective.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 Can you suggest me an AI model that can classify images with 50x fewer parameters than AlexNet and better performance on a robotics project I'm working on?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SqueezeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'squeezenet1_0\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_0\'], \'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'squeezenet1_0\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'squeezenet1_0\': {\'Top-1 error\': 41.9, \'Top-5 error\': 19.58}}}, \'description\': \'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\'}', metadata={})]


 Recommend a way to recognize decorative and architectural elements in architectural design images using a pre-trained network.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'IBN-Net\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'XingangPan/IBN-Net\', model=\'se_resnet101_ibn_a\', pretrained=True)", \'api_arguments\': [{\'name\': \'se_resnet101_ibn_a\', \'type\': \'str\', \'description\': \'SE-ResNet-101-IBN-a model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'XingangPan/IBN-Net\', \'se_resnet101_ibn_a\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'se_resnet101_ibn_a\': {\'Top-1 acc\': 78.75, \'Top-5 acc\': 94.49}}}, \'description\': \'IBN-Net is a CNN model with domain/appearance invariance. Motivated by style transfer works, IBN-Net carefully unifies instance normalization and batch normalization in a single deep network. It provides a simple way to increase both modeling and generalization capacities without adding model complexity. IBN-Net is especially suitable for cross domain or person/vehicle re-identification tasks.\'}', metadata={})]


 Can you suggest an API that can automatically classify images for me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Suggest an API for classifying dog breeds given an image of a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Semi-supervised and semi-weakly supervised ImageNet Models\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/semi-supervised-ImageNet1K-models\', model=\'resnet18_swsl\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/semi-supervised-ImageNet1K-models\', \'model\': \'resnet18_swsl\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/semi-supervised-ImageNet1K-models\', \'resnet18_swsl\', pretrained=True)", \'model.eval()\'], \'performance\': {\'description\': \'Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.\'}, \'description\': "Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the \'Billion scale semi-supervised learning for image classification\' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library."}', metadata={})]


 Suggest an API designed for NVIDIA GPU and TensorRT performance optimization to classify images into different categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'GPUNet Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_gpunet\', pretrained=True, model_type=model_type, model_math=precision)", \'api_arguments\': {\'repository\': \'NVIDIA/DeepLearningExamples:torchhub\', \'model\': \'nvidia_gpunet\', \'pretrained\': \'True\', \'model_type\': \'GPUNet-0\', \'model_math\': \'fp32\'}, \'python_environment_requirements\': [\'torch\', \'validators\', \'matplotlib\', \'timm==0.5.4\'], \'example_code\': [\'import torch\', "model_type = \'GPUNet-0\'", "precision = \'fp32\'", "gpunet = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_gpunet\', pretrained=True, model_type=model_type, model_math=precision)", "device = torch.device(\'cuda\') if torch.cuda.is_available() else torch.device(\'cpu\')", \'gpunet.to(device)\', \'gpunet.eval()\'], \'performance\': {\'dataset\': \'IMAGENET\', \'description\': \'GPUNet demonstrates state-of-the-art inference performance up to 2x faster than EfficientNet-X and FBNet-V3.\'}, \'description\': \'GPUNet is a family of Convolutional Neural Networks designed by NVIDIA using novel Neural Architecture Search (NAS) methods. They are optimized for NVIDIA GPU and TensorRT performance. GPUNet models are pretrained on the IMAGENET dataset and are capable of classifying images into different categories. The models are provided by the NVIDIA Deep Learning Examples library.\'}', metadata={})]


 Translate the given English text to French using machine learning API.\n###Input: {\"text\": \"I like playing basketball.\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 Recommend an API to identify the breed of a dog from a picture input.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'Inception_v3\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'inception_v3\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'1.9.0\', \'torchvision\': \'0.10.0\'}, \'example_code\': {\'import_libraries\': \'import torch\', \'load_model\': "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'inception_v3\', pretrained=True)", \'model_evaluation\': \'model.eval()\'}, \'performance\': {\'dataset\': \'imagenet\', \'accuracy\': {\'top-1_error\': 22.55, \'top-5_error\': 6.44}}, \'description\': \'Inception v3, also called GoogleNetv3, is a famous Convolutional Neural Network trained on the ImageNet dataset from 2015. It is based on the exploration of ways to scale up networks to utilize the added computation as efficiently as possible by using suitably factorized convolutions and aggressive regularization. The model achieves a top-1 error of 22.55% and a top-5 error of 6.44% on the ImageNet dataset.\'}', metadata={})]