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Fix model referenced and results in documentation. Model mentioned was inaccessible (#24609)

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Rafael Padilla
2023-07-05 13:25:36 -03:00
committed by GitHub
parent 050ef14516
commit 3df3b9d4bf
2 changed files with 34 additions and 34 deletions
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34
docs/source/en/tasks/object_detection.md
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@@ -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} ... 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"]) >>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) >>> 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 >>> import evaluate
>>> from tqdm import tqdm >>> 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) >>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
>>> val_dataloader = torch.utils.data.DataLoader( >>> val_dataloader = torch.utils.data.DataLoader(
... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn ... 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... Accumulating evaluation results...
DONE (t=0.08s). DONE (t=0.08s).
IoU metric: bbox IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.280 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 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 ] = 0.036 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 ] = 0.182 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 ] = 0.166 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 ] = 0.317 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 ] = 0.335 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 ] = 0.104 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 ] = 0.146 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 ] = 0.382 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! 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" >>> url = "https://i.imgur.com/2lnWoly.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw) >>> 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) >>> obj_detector(image)
``` ```
You can also manually replicate the results of the pipeline if you'd like: You can also manually replicate the results of the pipeline if you'd like:
```py ```py
>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") >>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") >>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> with torch.no_grad(): >>> with torch.no_grad():
... inputs = image_processor(images=image, return_tensors="pt") ... inputs = image_processor(images=image, return_tensors="pt")

34
docs/source/ko/tasks/object_detection.md
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@@ -473,7 +473,7 @@ COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로
... return {"pixel_values": pixel_values, "labels": target} ... 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"]) >>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) >>> 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 >>> import evaluate
>>> from tqdm import tqdm >>> 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) >>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
>>> val_dataloader = torch.utils.data.DataLoader( >>> val_dataloader = torch.utils.data.DataLoader(
... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn ... 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... Accumulating evaluation results...
DONE (t=0.08s). DONE (t=0.08s).
IoU metric: bbox IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.280 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 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 ] = 0.036 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 ] = 0.182 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 ] = 0.166 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 ] = 0.317 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 ] = 0.335 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 ] = 0.104 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 ] = 0.146 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 ] = 0.382 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! 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" >>> url = "https://i.imgur.com/2lnWoly.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw) >>> 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) >>> obj_detector(image)
``` ```
만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다: 만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
```py ```py
>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") >>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5") >>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> with torch.no_grad(): >>> with torch.no_grad():
... inputs = image_processor(images=image, return_tensors="pt") ... inputs = image_processor(images=image, return_tensors="pt")