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dongliyue
e0cac1406b
|
1 year ago | |
|---|---|---|
| cpp_infer/src | 1 year ago | |
| infer | 1 year ago | |
| model_utils | 1 year ago | |
| modelarts | 1 year ago | |
| scripts | 1 year ago | |
| src | 1 year ago | |
| README.md | 1 year ago | |
| README_CN.md | 1 year ago | |
| cpu_default_config.yaml | 1 year ago | |
| data_split.py | 1 year ago | |
| default_config.yaml | 1 year ago | |
| eval.py | 1 year ago | |
| export.py | 1 year ago | |
| finetune_cpu_default_config.yaml | 1 year ago | |
| mindspore_hub_conf.py | 1 year ago | |
| postprocess.py | 1 year ago | |
| quick_start.py | 1 year ago | |
| requirements.txt | 1 year ago | |
| test.py | 1 year ago | |
| train.py | 1 year ago | |
| xml2coco.py | 1 year ago | |
README.md
Contents
- Contents
- YOLOv4 Description
- Model Architecture
- Pretrain Model
- Dataset
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- Description of Random Situation
- ModelZoo Homepage
YOLOv4 Description
YOLOv4 is a state-of-the-art detector which is faster (FPS) and more accurate (MS COCO AP50...95 and AP50) than all available alternative detectors.
YOLOv4 has verified a large number of features, and selected for use such of them for improving the accuracy of both the classifier and the detector.
These features can be used as best-practice for future studies and developments.
Paper:
Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. arXiv preprint arXiv:2004.10934, 2020.
Model Architecture
YOLOv4 choose CSPDarknet53 backbone, SPP additional module, PANet path-aggregation neck, and YOLOv4 (anchor based) head as the architecture of YOLOv4.
Pretrain Model
YOLOv4 needs a CSPDarknet53 backbone to extract image features for detection. The pretrained checkpoint trained with ImageNet2012 can be downloaded at hear.
Dataset
Dataset used: COCO2017
Dataset support: [COCO2017] or datasetd with the same format as MS COCO
Annotation support: [COCO2017] or annotation as the same format as MS COCO
-
The directory structure is as follows, the name of directory and file is user define:
├── dataset ├── YOLOv4 ├── annotations │ ├─ train.json │ └─ val.json ├─train │ ├─picture1.jpg │ ├─ ... │ └─picturen.jpg ├─ val ├─picture1.jpg ├─ ... └─picturen.jpg
we suggest user to use MS COCO dataset to experience our model,
other datasets need to use the same format as MS COCO.
Environment Requirements
- Hardware（Ascend）
- Prepare hardware environment with Ascend processor.
- Framework
- For more information, please check the resources below：
Quick Start
-
After installing MindSpore via the official website, you can start training and evaluation as follows:
-
Prepare the CSPDarknet53.ckpt and hccl_8p.json files, before run network.
-
Please refer to [Pretrain Model]
-
Genatating hccl_8p.json, Run the script of utils/hccl_tools/hccl_tools.py.
The following parameter "[0-8)" indicates that the hccl_8p.json file of cards 0 to 7 is generated.python hccl_tools.py --device_num "[0,8)"
-
-
Run on local
# The parameter of training_shape define image shape for network, default is [416, 416], [448, 448], [480, 480], [512, 512], [544, 544], [576, 576], [608, 608], [640, 640], [672, 672], [704, 704], [736, 736]. # It means use 11 kinds of shape as input shape, or it can be set some kind of shape. #run training example(1p) by python command (Training with a single scale) python train.py \ --data_dir=./dataset/xxx \ --pretrained_backbone=cspdarknet53_backbone.ckpt \ --is_distributed=0 \ --lr=0.1 \ --t_max=320 \ --max_epoch=320 \ --warmup_epochs=4 \ --training_shape=416 \ --lr_scheduler=cosine_annealing > log.txt 2>&1 & # standalone training example(1p) by shell script (Training with a single scale) bash run_standalone_train.sh dataset/xxx cspdarknet53_backbone.ckpt 0 # For Ascend device, distributed training example(8p) by shell script (Training with multi scale) bash run_distribute_train.sh dataset/xxx cspdarknet53_backbone.ckpt rank_table_8p.json # run evaluation by python command python eval.py \ --data_dir=./dataset/xxx \ --pretrained=yolov4.ckpt \ --testing_shape=608 > log.txt 2>&1 & # run evaluation by shell script bash run_eval.sh dataset/xxx checkpoint/xxx.ckpt 0 -
Train on ModelArts
# Train 8p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "data_dir='/cache/data/coco/'" on base_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_pretrain/'" on base_config.yaml file. # Set "pretrained_backbone='/cache/checkpoint_path/cspdarknet53_backbone.ckpt'" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "data_dir=/cache/data/coco/" on the website UI interface. # Add "checkpoint_url=s3://dir_to_your_pretrain/" on the website UI interface. # Add "pretrained_backbone=/cache/checkpoint_path/cspdarknet53_backbone.ckpt" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your pretrained model to S3 bucket. # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (5) Set the code directory to "/path/yolov4" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Train 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "data_dir='/cache/data/coco/'" on base_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_pretrain/'" on base_config.yaml file. # Set "pretrained_backbone='/cache/checkpoint_path/cspdarknet53_backbone.ckpt'" on base_config.yaml file. # Set "is_distributed=0" on base_config.yaml file. # Set "warmup_epochs=4" on base_config.yaml file. # Set "training_shape=416" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "data_dir=/cache/data/coco/" on the website UI interface. # Add "checkpoint_url=s3://dir_to_your_pretrain/" on the website UI interface. # Add "pretrained_backbone=/cache/checkpoint_path/cspdarknet53_backbone.ckpt" on the website UI interface. # Add "is_distributed=0" on the website UI interface. # Add "warmup_epochs=4" on the website UI interface. # Add "training_shape=416" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your pretrained model to S3 bucket. # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (5) Set the code directory to "/path/yolov4" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Eval 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "data_dir='/cache/data/coco/'" on base_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on base_config.yaml file. # Set "pretrained='/cache/checkpoint_path/model.ckpt'" on base_config.yaml file. # Set "is_distributed=0" on base_config.yaml file. # Set "per_batch_size=1" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "data_dir=/cache/data/coco/" on the website UI interface. # Add "checkpoint_url=s3://dir_to_your_trained_ckpt/" on the website UI interface. # Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface. # Add "is_distributed=0" on the website UI interface. # Add "per_batch_size=1" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your trained model to S3 bucket. # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (5) Set the code directory to "/path/yolov4" on the website UI interface. # (6) Set the startup file to "eval.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Test 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "data_dir='/cache/data/coco/'" on base_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on base_config.yaml file. # Set "pretrained='/cache/checkpoint_path/model.ckpt'" on base_config.yaml file. # Set "is_distributed=0" on base_config.yaml file. # Set "per_batch_size=1" on base_config.yaml file. # Set "test_nms_thresh=0.45" on base_config.yaml file. # Set "test_ignore_threshold=0.001" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "data_dir=/cache/data/coco/" on the website UI interface. # Add "checkpoint_url=s3://dir_to_your_trained_ckpt/" on the website UI interface. # Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface. # Add "is_distributed=0" on the website UI interface. # Add "per_batch_size=1" on the website UI interface. # Add "test_nms_thresh=0.45" on the website UI interface. # Add "test_ignore_threshold=0.001" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your trained model to S3 bucket. # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (5) Set the code directory to "/path/yolov4" on the website UI interface. # (6) Set the startup file to "test.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job.
Script Description
Script and Sample Code
└─yolov4
├─README.md
├─README_CN.md
├─mindspore_hub_conf.py # config for mindspore hub
├─scripts
├─run_standalone_train.sh # launch standalone training(1p) in ascend
├─run_distribute_train.sh # launch distributed training(8p) in ascend
└─run_eval.sh # launch evaluating in ascend
├─run_test.sh # launch testing in ascend
├─src
├─__init__.py # python init file
├─config.py # parameter configuration
├─cspdarknet53.py # backbone of network
├─distributed_sampler.py # iterator of dataset
├─export.py # convert mindspore model to air model
├─initializer.py # initializer of parameters
├─logger.py # log function
├─loss.py # loss function
├─lr_scheduler.py # generate learning rate
├─transforms.py # Preprocess data
├─util.py # util function
├─yolo.py # yolov4 network
├─yolo_dataset.py # create dataset for YOLOV4
├─eval.py # evaluate val results
├─test.py# # evaluate test results
└─train.py # train net
Script Parameters
Major parameters train.py as follows:
optional arguments:
-h, --help show this help message and exit
--device_target device where the code will be implemented: "Ascend", default is "Ascend"
--data_dir DATA_DIR Train dataset directory.
--per_batch_size PER_BATCH_SIZE
Batch size for Training. Default: 8.
--pretrained_backbone PRETRAINED_BACKBONE
The ckpt file of CspDarkNet53. Default: "".
--resume_yolov4 RESUME_YOLOV4
The ckpt file of YOLOv4, which used to fine tune.
Default: ""
--lr_scheduler LR_SCHEDULER
Learning rate scheduler, options: exponential,
cosine_annealing. Default: exponential
--lr LR Learning rate. Default: 0.001
--lr_epochs LR_EPOCHS
Epoch of changing of lr changing, split with ",".
Default: 220,250
--lr_gamma LR_GAMMA Decrease lr by a factor of exponential lr_scheduler.
Default: 0.1
--eta_min ETA_MIN Eta_min in cosine_annealing scheduler. Default: 0
--t_max T_MAX T-max in cosine_annealing scheduler. Default: 320
--max_epoch MAX_EPOCH
Max epoch num to train the model. Default: 320
--warmup_epochs WARMUP_EPOCHS
Warmup epochs. Default: 0
--weight_decay WEIGHT_DECAY
Weight decay factor. Default: 0.0005
--momentum MOMENTUM Momentum. Default: 0.9
--loss_scale LOSS_SCALE
Static loss scale. Default: 64
--label_smooth LABEL_SMOOTH
Whether to use label smooth in CE. Default:0
--label_smooth_factor LABEL_SMOOTH_FACTOR
Smooth strength of original one-hot. Default: 0.1
--log_interval LOG_INTERVAL
Logging interval steps. Default: 100
--ckpt_path CKPT_PATH
Checkpoint save location. Default: outputs/
--ckpt_interval CKPT_INTERVAL
Save checkpoint interval. Default: None
--is_save_on_master IS_SAVE_ON_MASTER
Save ckpt on master or all rank, 1 for master, 0 for
all ranks. Default: 1
--is_distributed IS_DISTRIBUTED
Distribute train or not, 1 for yes, 0 for no. Default:
1
--rank RANK Local rank of distributed. Default: 0
--group_size GROUP_SIZE
World size of device. Default: 1
--need_profiler NEED_PROFILER
Whether use profiler. 0 for no, 1 for yes. Default: 0
--training_shape TRAINING_SHAPE
Fix training shape. Default: ""
--resize_rate RESIZE_RATE
Resize rate for multi-scale training. Default: 10
--transfer_train TRANSFER_TRAIN
Transfer training on other dataset, if set it True set filter_weight True. Default: False
Training Process
YOLOv4 can be trained from the scratch or with the backbone named cspdarknet53.
Cspdarknet53 is a classifier which can be trained on some dataset like ImageNet(ILSVRC2012).
It is easy for users to train Cspdarknet53. Just replace the backbone of Classifier Resnet50 with cspdarknet53.
Resnet50 is easy to get in mindspore model zoo.
Training
For Ascend device, standalone training example(1p) by shell script
bash run_standalone_train.sh dataset/coco2017 cspdarknet53_backbone.ckpt 0
python train.py \
--data_dir=/dataset/xxx \
--pretrained_backbone=cspdarknet53_backbone.ckpt \
--is_distributed=0 \
--lr=0.1 \
--t_max=320 \
--max_epoch=320 \
--warmup_epochs=4 \
--training_shape=416 \
--lr_scheduler=cosine_annealing > log.txt 2>&1 &
The python command above will run in the background, you can view the results through the file log.txt.
After training, you'll get some checkpoint files under the outputs folder by default. The loss value will be achieved as follows:
# grep "loss:" train/log.txt
2020-10-16 15:00:37,483:INFO:epoch[0], iter[0], loss:8248.610352, 0.03 imgs/sec, lr:2.0466639227834094e-07
2020-10-16 15:00:52,897:INFO:epoch[0], iter[100], loss:5058.681709, 51.91 imgs/sec, lr:2.067130662908312e-05
2020-10-16 15:01:08,286:INFO:epoch[0], iter[200], loss:1583.772806, 51.99 imgs/sec, lr:4.1137944208458066e-05
2020-10-16 15:01:23,457:INFO:epoch[0], iter[300], loss:1229.840823, 52.75 imgs/sec, lr:6.160458724480122e-05
2020-10-16 15:01:39,046:INFO:epoch[0], iter[400], loss:1155.170310, 51.32 imgs/sec, lr:8.207122300518677e-05
2020-10-16 15:01:54,138:INFO:epoch[0], iter[500], loss:920.922433, 53.02 imgs/sec, lr:0.00010253786604152992
2020-10-16 15:02:09,209:INFO:epoch[0], iter[600], loss:808.610681, 53.09 imgs/sec, lr:0.00012300450180191547
2020-10-16 15:02:24,240:INFO:epoch[0], iter[700], loss:621.931513, 53.23 imgs/sec, lr:0.00014347114483825862
2020-10-16 15:02:39,280:INFO:epoch[0], iter[800], loss:527.155985, 53.20 imgs/sec, lr:0.00016393778787460178
...
Distributed Training
For Ascend device, distributed training example(8p) by shell script
bash run_distribute_train.sh dataset/coco2017 cspdarknet53_backbone.ckpt rank_table_8p.json
The above shell script will run distribute training in the background. You can view the results through the file train_parallel[X]/log.txt. The loss value will be achieved as follows:
# distribute training result(8p, dynamic shape)
...
2020-10-16 20:40:17,148:INFO:epoch[0], iter[800], loss:283.765033, 248.93 imgs/sec, lr:0.00026233625249005854
2020-10-16 20:40:43,576:INFO:epoch[0], iter[900], loss:257.549973, 242.18 imgs/sec, lr:0.00029508734587579966
2020-10-16 20:41:12,743:INFO:epoch[0], iter[1000], loss:252.426355, 219.43 imgs/sec, lr:0.00032783843926154077
2020-10-16 20:41:43,153:INFO:epoch[0], iter[1100], loss:232.104760, 210.46 imgs/sec, lr:0.0003605895326472819
2020-10-16 20:42:12,583:INFO:epoch[0], iter[1200], loss:236.973975, 217.47 imgs/sec, lr:0.00039334059692919254
2020-10-16 20:42:39,004:INFO:epoch[0], iter[1300], loss:228.881298, 242.24 imgs/sec, lr:0.00042609169031493366
2020-10-16 20:43:07,811:INFO:epoch[0], iter[1400], loss:255.025714, 222.19 imgs/sec, lr:0.00045884278370067477
2020-10-16 20:43:38,177:INFO:epoch[0], iter[1500], loss:223.847151, 210.76 imgs/sec, lr:0.0004915939061902463
2020-10-16 20:44:07,766:INFO:epoch[0], iter[1600], loss:222.302487, 216.30 imgs/sec, lr:0.000524344970472157
2020-10-16 20:44:37,411:INFO:epoch[0], iter[1700], loss:211.063779, 215.89 imgs/sec, lr:0.0005570960929617286
2020-10-16 20:45:03,092:INFO:epoch[0], iter[1800], loss:210.425542, 249.21 imgs/sec, lr:0.0005898471572436392
2020-10-16 20:45:32,767:INFO:epoch[1], iter[1900], loss:208.449521, 215.67 imgs/sec, lr:0.0006225982797332108
2020-10-16 20:45:59,163:INFO:epoch[1], iter[2000], loss:209.700071, 242.48 imgs/sec, lr:0.0006553493440151215
...
Transfer Training
You can train your own model based on either pretrained classification model or pretrained detection model. You can perform transfer training by following steps.
- Convert your own dataset to COCO style. Otherwise you have to add your own data preprocess code.
- Change
default_config.yaml:- Set argument
labelsaccording to your own dataset. - Set argument
transfer_traintoTrue, start to transfer training on other dataset. pretrained_checkpointis the path of pretrained checkpoint, it will download pretrained checkpoint automatically if not set it.- Set argument
run_evaltoTrueif you want get eval dataset mAP when training.
- Set argument
- Build your own bash scripts using new config and arguments for further convenient.
Evaluation Process
Valid
python eval.py \
--data_dir=./dataset/coco2017 \
--pretrained=yolov4.ckpt \
--testing_shape=608 > log.txt 2>&1 &
OR
bash run_eval.sh dataset/coco2017 checkpoint/yolov4.ckpt 0
The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:
# log.txt
=============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.635
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.479
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.274
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.545
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.418
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.717
Furthermore, set result_view as true, it will draw the result bbox, categories and score values to the original image and save them to the current eval path. Also can set recommend_threshold as true, which enables map calculation of every categories, recommends the best score threshold, and distinguishes between saving correct and negative prediction result.
Test-dev
python test.py \
--data_dir=./dataset/coco2017 \
--pretrained=yolov4.ckpt \
--testing_shape=608 > log.txt 2>&1 &
OR
bash run_test.sh dataset/coco2017 checkpoint/yolov4.ckpt 0
The predict_xxx.json will be found in test/outputs/%Y-%m-%d_time_%H_%M_%S/.
Rename the file predict_xxx.json to detections_test-dev2017_yolov4_results.json and compress it to detections_test-dev2017_yolov4_results.zip
Submit file detections_test-dev2017_yolov4_results.zip to the MS COCO evaluation server for the test-dev2019 (bbox) https://competitions.codalab.org/competitions/20794#participate
You will get such results in the end of file View scoring output log.
overall performance
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.447
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.642
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.487
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.267
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.335
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.584
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.627
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.711
Convert Process
Convert
If you want to infer the network, you should convert the model to MINDIR:
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --keep_detect [Bool]
The ckpt_file parameter is required,
FILE_FORMAT should be in ["AIR", "ONNX", "MINDIR"]
keep_detect keep the detect module or not, default: True
Inference Process
Before inference, please refer to MindSpore Inference with C++ Deployment Guide to set environment variables.
Usage
Before performing inference, the mindir file must be exported by export script on the 910 environment.
Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space.
bash run_infer_cpp.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] [ANN_FILE]
DEVICE_ID is optional, default value is 0.
result
Inference result is saved in current path, you can find result like this in acc.log file.
=============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.438
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.630
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.475
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.481
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.330
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.542
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.588
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.410
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
Model Description
Performance
Evaluation Performance
YOLOv4 on 118K images(The annotation and data format must be the same as coco2017)
| Parameters | YOLOv4 |
|---|---|
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G; System, Euleros 2.8; |
| uploaded Date | 10/16/2020 (month/day/year) |
| MindSpore Version | 1.0.0-alpha |
| Dataset | 118K images |
| Training Parameters | epoch=320, batch_size=8, lr=0.012,momentum=0.9 |
| Optimizer | Momentum |
| Loss Function | Sigmoid Cross Entropy with logits, Giou Loss |
| outputs | boxes and label |
| Loss | 50 |
| Speed | 1p 53FPS 8p 390FPS(shape=416) 220FPS(dynamic shape) |
| Total time | 48h(dynamic shape) |
| Checkpoint for Fine tuning | about 500M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/models/tree/master/official/cv/YOLOv4 |
Inference Performance
YOLOv4 on 20K images(The annotation and data format must be the same as coco test2017 )
| Parameters | YOLOv4 |
|---|---|
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
| uploaded Date | 10/16/2020 (month/day/year) |
| MindSpore Version | 1.0.0-alpha |
| Dataset | 20K images |
| batch_size | 1 |
| outputs | box position and sorces, and probability |
| Accuracy | map >= 43.8%(shape=608) |
| Model for inference | about 500M (.ckpt file) |
Description of Random Situation
In dataset.py, we set the seed inside create_dataset function.
In var_init.py, we set seed for weight initialization
ModelZoo Homepage
Please check the official homepage.
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