i try pre-train model but not good

[PhamMinhTien05102001]

i have image have table basic:

i have simple code:

image = Image.open('./imgs_test/3.jpg').convert("RGB")
width, height = image.size
image.resize((int(width0.5), int(height0.5)))
from transformers import DetrFeatureExtractor

feature_extractor = DetrFeatureExtractor()
encoding = feature_extractor(image, return_tensors="pt")
encoding.keys()

from transformers import TableTransformerForObjectDetection

model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection")

import torch

with torch.no_grad():
outputs = model(**encoding)

import matplotlib.pyplot as plt

colors for visualization

COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]

def plot_results(pil_img, scores, labels, boxes):
plt.figure(figsize=(16,10))
plt.imshow(pil_img)
ax = plt.gca()
colors = COLORS * 100
for score, label, (xmin, ymin, xmax, ymax),c in zip(scores.tolist(), labels.tolist(), boxes.tolist(), colors):
ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
fill=False, color=c, linewidth=3))
text = f'{model.config.id2label[label]}: {score:0.2f}'
ax.text(xmin, ymin, text, fontsize=15,
bbox=dict(facecolor='yellow', alpha=0.5))

rescale bounding boxes

width, height = image.size
results = feature_extractor.post_process_object_detection(outputs, threshold=0, target_sizes=[(height, width)])[0]
plot_results(image, results['scores'], results['labels'], results['boxes'])

THIS is result:

i set thres = 0, but no tables is correct, help me pls. reason pre-train model not good?