diff --git a/official/cv/resnet/README.md b/official/cv/resnet/README.md index 7570284e04807999f8f8f238b84a37a8ac872f5f..2e606ac978a45f10e046bc83d959fa0ac66541c4 100644 --- a/official/cv/resnet/README.md +++ b/official/cv/resnet/README.md @@ -616,12 +616,12 @@ epoch: [0/1] step: [100/5004], loss is 6.814013Epoch time: 3437.154 ms, fps: 148 ```bash # evaluation -Usage: bash run_eval.sh [DATASET_PATH] [CONFIG_PATH] [CHECKPOINT_PATH] +Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH] ``` ```bash # evaluation example -bash run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt --config_path /.yaml +bash run_eval.sh ~/cifar10-10-verify-bin /resnet50_cifar10/train_parallel0/resnet-90_195.ckpt config/resnet50_cifar10_config.yaml ``` > checkpoint can be produced in training process. @@ -726,7 +726,7 @@ Export on ModelArts (If you want to run in modelarts, please check the official ### Infer on Ascend310 Before performing inference, the mindir file must bu exported by `export.py` script. We only provide an example of inference using MINDIR model. -Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits. +Current batch_Size can only be set to 1. ```shell # Ascend310 inference @@ -813,7 +813,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 4 mins | 11 minds | | Parameters (M) | 11.2 | 11.2 | | Checkpoint for Fine tuning | 86M (.ckpt file) | 85.4 (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet18 on ImageNet2012 @@ -833,7 +833,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 110 mins | 130 mins | | Parameters (M) | 11.7 | 11.7 | | Checkpoint for Fine tuning | 90M (.ckpt file) | 90M (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet50 on CIFAR-10 @@ -853,7 +853,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 6 mins | 20.2 mins| | Parameters (M) | 25.5 | 25.5 | | Checkpoint for Fine tuning | 179.7M (.ckpt file) |179.7M (.ckpt file)| -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet50 on ImageNet2012 @@ -873,12 +873,12 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 114 mins | 260 mins| | Parameters (M) | 25.5 | 25.5 | | Checkpoint for Fine tuning | 197M (.ckpt file) |197M (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet34 on ImageNet2012 | Parameters | Ascend 910 | -| -------------------------- | -------------------------------------- |---------------------------------- | +| -------------------------- | -------------------------------------- | | Model Version | ResNet50-v1.5 | | Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | | uploaded Date | 07/05/2020 (month/day/year) 锛� | @@ -893,7 +893,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 112 mins | | Parameters (M) | 20.79 | | Checkpoint for Fine tuning | 166M (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet101 on ImageNet2012 @@ -913,7 +913,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 301 mins | 1100 mins| | Parameters (M) | 44.6 | 44.6 | | Checkpoint for Fine tuning | 343M (.ckpt file) |343M (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ResNet152 on ImageNet2012 @@ -933,7 +933,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 577 mins | | Parameters(M) | 60.19 | | Checkpoint for Fine tuning | 462M锛�.ckpt file锛� | -| Scripts | [Link](https://gitee.com/panpanrui/mindspore/tree/master/model_zoo/official/cv/resnet152) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### SE-ResNet50 on ImageNet2012 @@ -953,7 +953,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | Total time | 49.3 mins | | Parameters (M) | 25.5 | | Checkpoint for Fine tuning | 215.9M (.ckpt file) | -| Scripts | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| config | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | ### Inference Performance @@ -969,7 +969,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 32 | | outputs | probability | | Accuracy | 94.02% | -| Model for inference | 43M (.air file) | +| Model for inference | 43M (.mindir file) | #### ResNet18 on ImageNet2012 @@ -983,7 +983,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 256 | | outputs | probability | | Accuracy | 70.53% | -| Model for inference | 45M (.air file) | +| Model for inference | 45M (.mindir file) | #### ResNet34 on ImageNet2012 @@ -997,7 +997,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 256 | | outputs | probability | | Accuracy | 73.67% | -| Model for inference | 70M (.air file) | +| Model for inference | 70M (.mindir file) | #### ResNet50 on CIFAR-10 @@ -1011,7 +1011,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 32 | 32 | | outputs | probability | probability | | Accuracy | 91.44% | 91.37% | -| Model for inference | 91M (.air file) | | +| Model for inference | 91M (.mindir file) | | #### ResNet50 on ImageNet2012 @@ -1025,7 +1025,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 256 | 256 | | outputs | probability | probability | | Accuracy | 76.70% | 76.74% | -| Model for inference | 98M (.air file) | | +| Model for inference | 98M (.mindir file) | | #### ResNet101 on ImageNet2012 @@ -1039,7 +1039,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 32 | 32 | | outputs | probability | probability | | Accuracy | 78.53% | 78.64% | -| Model for inference | 171M (.air file) | | +| Model for inference | 171M (.mindir file) | | #### ResNet152 on ImageNet2012 @@ -1053,7 +1053,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 32 | | outputs | probability | | Accuracy | 78.60% | -| Model for inference | 236M (.air file) | +| Model for inference | 236M (.mindir file) | #### SE-ResNet50 on ImageNet2012 @@ -1067,7 +1067,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | batch_size | 32 | | outputs | probability | | Accuracy | 76.80% | -| Model for inference | 109M (.air file) | +| Model for inference | 109M (.mindir file) | # [Description of Random Situation](#contents) diff --git a/official/cv/resnet/README_CN.md b/official/cv/resnet/README_CN.md index 64d70484d17397de889819c379d5a7194330e683..c31941796ff7884d6f4b379f239da06b40a84f4a 100644 --- a/official/cv/resnet/README_CN.md +++ b/official/cv/resnet/README_CN.md @@ -587,7 +587,7 @@ Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH] ```bash # 璇勪及绀轰緥 -bash run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt --config_path /*.yaml +bash run_eval.sh ~/cifar10-10-verify-bin /resnet50_cifar10/train_parallel0/resnet-90_195.ckpt config/resnet50_cifar10_config.yaml ``` > 璁粌杩囩▼涓彲浠ョ敓鎴愭鏌ョ偣銆� @@ -689,7 +689,7 @@ ModelArts瀵煎嚭mindir ### 鍦ˋscend310鎵ц鎺ㄧ悊 鍦ㄦ墽琛屾帹鐞嗗墠锛宮indir鏂囦欢蹇呴』閫氳繃`export.py`鑴氭湰瀵煎嚭銆備互涓嬪睍绀轰簡浣跨敤minir妯″瀷鎵ц鎺ㄧ悊鐨勭ず渚嬨€� -鐩墠浠呮敮鎸乥atch_Size涓�1鐨勬帹鐞嗐€傜簿搴﹁绠楄繃绋嬮渶瑕�70G+鐨勫唴瀛橈紝鍚﹀垯杩涚▼灏嗕細鍥犱负瓒呭嚭鍐呭瓨琚郴缁熺粓姝€€� +鐩墠浠呮敮鎸乥atch_Size涓�1鐨勬帹鐞嗐€� ```shell # Ascend310 inference @@ -776,7 +776,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 4鍒嗛挓 | 11鍒嗛挓 | | 鍙傛暟(M) | 11.2 | 11.2 | | 寰皟妫€鏌ョ偣 | 86锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ImageNet2012涓婄殑ResNet18 @@ -796,7 +796,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 110鍒嗛挓 | 130鍒嗛挓 | | 鍙傛暟(M) | 11.7 | 11.7 | | 寰皟妫€鏌ョ偣| 90M锛�.ckpt鏂囦欢锛� | 90M锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### CIFAR-10涓婄殑ResNet50 @@ -816,7 +816,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 6鍒嗛挓 | 20.2鍒嗛挓| | 鍙傛暟(M) | 25.5 | 25.5 | | 寰皟妫€鏌ョ偣 | 179.7M锛�.ckpt鏂囦欢锛� | 179.7M锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ImageNet2012涓婄殑ResNet50 @@ -836,7 +836,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 114鍒嗛挓 | 500鍒嗛挓| | 鍙傛暟(M) | 25.5 | 25.5 | | 寰皟妫€鏌ョ偣| 197M锛�.ckpt鏂囦欢锛� | 197M锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ImageNet2012涓婄殑ResNet34 @@ -856,7 +856,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 112鍒嗛挓 | | 鍙傛暟(M) | 20.79 | | 寰皟妫€鏌ョ偣| 166M锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config)| #### ImageNet2012涓婄殑ResNet101 @@ -876,7 +876,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 301鍒嗛挓 | 1100鍒嗛挓| | 鍙傛暟(M) | 44.6 | 44.6 | | 寰皟妫€鏌ョ偣| 343M锛�.ckpt鏂囦欢锛� | 343M锛�.ckpt鏂囦欢锛� | -|鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +|閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ImageNet2012涓婄殑ResNet152 @@ -896,7 +896,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. |鎬绘椂闀� | 577鍒嗛挓 | |鍙傛暟(M) | 60.19 | | 寰皟妫€鏌ョ偣 | 462M锛�.ckpt鏂囦欢锛� | -| 鑴氭湰 | [閾炬帴](https://gitee.com/panpanrui/mindspore/tree/master/model_zoo/official/cv/resnet152) | +| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | #### ImageNet2012涓婄殑SE-ResNet50 @@ -916,7 +916,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522. | 鎬绘椂闀� | 49.3鍒嗛挓 | | 鍙傛暟(M) | 25.5 | | 寰皟妫€鏌ョ偣 | 215.9M 锛�.ckpt鏂囦欢锛� | -|鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | +|閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | # 闅忔満鎯呭喌璇存槑