Image Classification
- Model
- Task
- Inference Time (ms)
- Throughput (FPS)
- Description
- File
- Repo or Website
Devices: Edge TPU
Tasks: Image Classification
https://github.com/neuralet/neuralet-models/blob/master/edge-tpu/OFMClassifier/OFMClassifier_edgetpu.tflite
https://github.com/neuralet/neuralet/tree/master/applications/facemask
Official classifier by Neuralet pre-trained on the Extended-Synthetic-Blurred dataset that classifies masked faces from unmasked ones.
30
Devices: Edge TPU
Tasks: Image Classification
http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz
https://coral.ai/models/
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 299x299 that recognizes 1000 classes.
15
Devices: Edge TPU
Tasks: Image Classification
http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz
https://coral.ai/models/
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 299x299 that recognizes 1000 classes.
18
Devices: Edge TPU
Tasks: Image Classification
http://download.tensorflow.org/models/inception_v2_224_quant_20181026.tgz
https://coral.ai/models/
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 224x224 that recognizes 1000 classes.
14
Devices: Edge TPU
Tasks: Image Classification
http://download.tensorflow.org/models/inception_v1_224_quant_20181026.tgz
https://coral.ai/models/
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 224x224 that recognizes 1000 classes.
14
Devices: Edge TPU
Tasks: Image Classification
https://github.com/google-coral/edgetpu/raw/master/test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite
https://coral.ai/models/
Official classifier by Coral pre-trained on the iNaturalist dataset (iNat birds) on an input size of 224x224 that recognizes more than 900 types of birds.
14
Devices: Edge TPU
Tasks: Image Classification
https://github.com/google-coral/edgetpu/raw/master/test_data/mobilenet_v2_1.0_224_inat_plant_quant_edgetpu.tflite
https://coral.ai/models/
Official classifier by Coral pre-trained on the iNaturalist dataset (iNat plants) on an input size of 224x224 that recognizes more than 2000 types of plants.
13
Devices: Edge TPU
Tasks: Image Classification
https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-edgetpu-L.tar.gz
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 300x300 that recognizes 1000 classes.
12
Devices: Edge TPU
Tasks: Image Classification
https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-edgetpu-M.tar.gz
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 240x240 that recognizes 1000 classes.
13
Devices: Edge TPU
Tasks: Image Classification
https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-edgetpu-S.tar.gz
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
Official classifier by Coral pre-trained on the ImageNet dataset on an input size of 224x224 that recognizes 1000 classes.
16
Object Detection
- Model
- Task
- Inference Time (ms)
- Throughput (FPS)
- Description
- File
- Repo or Website
Devices: Edge TPU
Tasks: Object Detection
https://github.com/google-coral/test_data/raw/master/ssd_mobilenet_v2_face_quant_postprocess_edgetpu.tflite
https://coral.ai/models/
Official object detector model by Coral pre-trained on the Open Images V4 dataset on an input size of 320x320 that recognizes human face.
19
Devices: Edge TPU
Tasks: Object Detection
https://github.com/google-coral/test_data/raw/master/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
https://coral.ai/models/
Official object detector model by Coral pre-trained on the MSCOCO dataset on an input size of 300x300 that recognizes 90 classes.
17
Devices: Edge TPU
Tasks: Object Detection
https://github.com/google-coral/test_data/raw/master/ssd_mobilenet_v1_coco_quant_postprocess_edgetpu.tflite
https://coral.ai/models/
Official object detector model by Coral pre-trained on the MSCOCO dataset on an input size of 300x300 that recognizes 90 classes.
17
Pose Estimation
- Model
- Task
- Inference Time (ms)
- Throughput (FPS)
- Description
- File
- Repo or Website
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/mobilenet/posenet_mobilenet_v1_075_721_1281_quant_decoder_edgetpu.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 1281x721 and a multiplier of 0.75.
10
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/mobilenet/posenet_mobilenet_v1_075_481_641_quant_decoder_edgetpu.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 641x481 and a multiplier of 0.75.
13
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/mobilenet/posenet_mobilenet_v1_075_353_481_quant_decoder_edgetpu.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 481x353 and a multiplier of 0.75.
14
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_960_736_32_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 960x736 and an output stride of 32.
14
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_928_672_16_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 928x672 and an output stride of 16.
16
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_864_624_32_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 864x624 and an output stride of 32.
12
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_768_496_32_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Coral pre-trained on the MSCOCO dataset on an input resolution of 768x496 and an output stride of 32.
12
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_640_480_16_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Google pre-trained on the MSCOCO dataset on an input resolution of 640x480 and an output stride of 16.
12
Devices: Edge TPU
Tasks: Pose Estimation
https://github.com/google-coral/project-posenet/blob/master/models/resnet/posenet_resnet_50_416_288_16_quant_edgetpu_decoder.tflite
https://github.com/neuralet/neuralet-models
Official pose estimation model by Google pre-trained on the MSCOCO dataset on an input resolution of 416x288 and an output stride of 16.
11
Semantic Segmentation
- Model
- Task
- Inference Time (ms)
- Throughput (FPS)
- Description
- File
- Repo or Website
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/test_data/raw/master/keras_post_training_unet_mv2_256_quant_edgetpu.tflite
https://coral.ai/models/
Official model by Coral that recognizes and segments pets using 3 classes: pixels belonging to a pet, pixels bordering a pet, and background pixels (it does not classify the type of pet), and is pre-trained on the Oxford-IIIT Pet dataset on an input size of 256x256.
13
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/test_data/raw/master/keras_post_training_unet_mv2_128_quant_edgetpu.tflite
https://coral.ai/models/
Official model by Coral that recognizes and segments pets using 3 classes: pixels belonging to a pet, pixels bordering a pet, and background pixels (it does not classify the type of pet), and is pre-trained on the Oxford-IIIT Pet dataset on an input size of 128x128.
13
DeepLabV3_MobileNetV2_513x513_1.0
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/edgetpu/raw/master/test_data/deeplabv3_mnv2_pascal_quant_edgetpu.tflite
https://coral.ai/models/
Official model by Coral that recognizes and segments 20 types of objects and is pre-trained on the PASCAL VOC 2012 dataset on an input size of 513x513 and depth multiplier of 1.0.
12
DeepLabV3_MobileNetV2_513x513_0.5
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/edgetpu/raw/master/test_data/deeplabv3_mnv2_dm05_pascal_quant_edgetpu.tflite
https://coral.ai/models/
Official model by Coral that recognizes and segments 20 types of objects and is pre-trained on the PASCAL VOC 2012 dataset on an input size of 513x513 and depth multiplier of 0.5.
16
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_960_736_32_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 960x736 and output stride of 32.
11
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_928_672_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 928x672 and output stride of 16.
10
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_864_624_32_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 864x624 and output stride of 32.
11
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_768_496_32_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 768x496 and output stride of 32.
10
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_640_480_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 640x480, depth multiplier of 0.75, and output stride of 16.
11
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_resnet_50_416_288_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 416x288, depth multiplier of 0.75, and output stride of 16.
11
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_mobilenet_v1_075_768_576_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 768x576, depth multiplier of 0.75, and output stride of 16.
10
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_mobilenet_v1_075_640_480_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 640x480, depth multiplier of 0.75, and output stride of 16.
11
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_mobilenet_v1_075_480_352_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 480x352, depth multiplier of 0.75, and output stride of 16.
12
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_mobilenet_v1_075_1280_720_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 1280x720, depth multiplier of 0.75, and output stride of 16.
9
Devices: Edge TPU
Tasks: Semantic Segmentation
https://github.com/google-coral/project-bodypix/blob/master/models/bodypix_mobilenet_v1_075_1024_768_16_quant_edgetpu_decoder.tflite
https://coral.ai/models/
Official model by Coral that allows for person and body-part segmentation and is pre-trained with an input size of 1024x768, depth multiplier of 0.75, and output stride of 16.
16