From 3df3b9d4bf006ab193b3c1257f3436b9fdb91759 Mon Sep 17 00:00:00 2001 From: Rafael Padilla <31217453+rafaelpadilla@users.noreply.github.com> Date: Wed, 5 Jul 2023 13:25:36 -0300 Subject: [PATCH] Fix model referenced and results in documentation. Model mentioned was inaccessible (#24609) --- docs/source/en/tasks/object_detection.md | 34 ++++++++++++------------ docs/source/ko/tasks/object_detection.md | 34 ++++++++++++------------ 2 files changed, 34 insertions(+), 34 deletions(-) diff --git a/docs/source/en/tasks/object_detection.md b/docs/source/en/tasks/object_detection.md index 457d96bfd3..563beb2742 100644 --- a/docs/source/en/tasks/object_detection.md +++ b/docs/source/en/tasks/object_detection.md @@ -481,7 +481,7 @@ Next, prepare an instance of a `CocoDetection` class that can be used with `coco ... return {"pixel_values": pixel_values, "labels": target} ->>> im_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"]) >>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) @@ -493,7 +493,7 @@ Finally, load the metrics and run the evaluation. >>> import evaluate >>> from tqdm import tqdm ->>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco) >>> val_dataloader = torch.utils.data.DataLoader( ... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn @@ -522,18 +522,18 @@ Finally, load the metrics and run the evaluation. Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.280 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.182 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.166 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590 ``` These results can be further improved by adjusting the hyperparameters in [`~transformers.TrainingArguments`]. Give it a go! @@ -549,15 +549,15 @@ for object detection with your model, and pass an image to it: >>> url = "https://i.imgur.com/2lnWoly.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) ->>> obj_detector = pipeline("object-detection", model="MariaK/detr-resnet-50_finetuned_cppe5") +>>> obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5") >>> obj_detector(image) ``` You can also manually replicate the results of the pipeline if you'd like: ```py ->>> image_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") ->>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") +>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> with torch.no_grad(): ... inputs = image_processor(images=image, return_tensors="pt") diff --git a/docs/source/ko/tasks/object_detection.md b/docs/source/ko/tasks/object_detection.md index cb573ed4e7..ca384d0381 100644 --- a/docs/source/ko/tasks/object_detection.md +++ b/docs/source/ko/tasks/object_detection.md @@ -473,7 +473,7 @@ COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 ... return {"pixel_values": pixel_values, "labels": target} ->>> im_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"]) >>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) @@ -485,7 +485,7 @@ COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 >>> import evaluate >>> from tqdm import tqdm ->>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco) >>> val_dataloader = torch.utils.data.DataLoader( ... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn @@ -514,18 +514,18 @@ COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 Accumulating evaluation results... DONE (t=0.08s). IoU metric: bbox - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.280 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.182 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.166 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590 ``` 이러한 결과는 [`~transformers.TrainingArguments`]의 하이퍼파라미터를 조정하여 더욱 개선될 수 있습니다. 한번 시도해 보세요! @@ -544,15 +544,15 @@ DETR 모델을 미세 조정 및 평가하고, 허깅페이스 허브에 업로 >>> url = "https://i.imgur.com/2lnWoly.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) ->>> obj_detector = pipeline("object-detection", model="MariaK/detr-resnet-50_finetuned_cppe5") +>>> obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5") >>> obj_detector(image) ``` 만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다: ```py ->>> image_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") ->>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") +>>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") +>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") >>> with torch.no_grad(): ... inputs = image_processor(images=image, return_tensors="pt")