I have created a model for object detection in Python Tensorflow and then converted it in Tensorflow JS so as to use in browser. The model works perfectly in python. Now, when I give an input image to browser, there is major difference between prediction results in python and in Tensorflow JS. I am sharing the prediction results for both python and JS.
Results for Python :
And Results for JS :
I have given the same image as input to both python and JS but still the big difference specially for Scores where python predicts with 99% and JS predicts with just 16%.
What could be the reason for this ? Have I inadvertently committed some mistake while converting to Tensorflow JS or is there some other reason for this ?
I went through this and other resources on the internet but couldn't find any specific reason for the difference in results.
Any help will be grateful. Thanks a lot.
Update 1 :
Here is my Python Code :
def load_image_into_numpy_array(image_path):
return np.array(Image.open(image_path))
image_path = random.choice(TEST_IMAGE_PATHS)
image_np = load_image_into_numpy_array(image_path)
input_tensor = tf.convert_to_tensor(
np.expand_dims(image_np, 0), dtype=tf.float32)
detections, predictions_dict, shapes = detect_fn(input_tensor)
label_id_offset = 1
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'][0].numpy(),
(detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
detections['detection_scores'][0].numpy(),
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
#Set min_score_thresh accordingly to display the boxes
min_score_thresh=.5,
agnostic_mode=False
)
plt.figure(figsize=(12,25))
plt.imshow(image_np_with_detections)
plt.show()
And here is model call in JS :
async function run() {
//Loading the Model :
model = await tf.loadGraphModel(MODEL_URL);
console.log("SUCCESS");
let img = document.getElementById("myimg");
console.log("Predicting....");
//Image PreProcessing
var example = tf.browser.fromPixels(img);
example = example.expandDims(0);
//model call
const output = await model.executeAsync(example);
console.log(output);
const boxes = output[4].arraySync();
const scores = output[5].arraySync();
const classes = output[1].arraySync();
console.log(boxes);
console.log(scores);
console.log(classes);
}
Update 2 :
import pathlib
filenames = list(pathlib.Path('/content/train/').glob('*.index'))
filenames.sort()
print(filenames)
#recover our saved model
pipeline_config = pipeline_file
#generally you want to put the last ckpt from training in here
model_dir = str(filenames[-1]).replace('.index','')
configs = config_util.get_configs_from_pipeline_file(pipeline_config)
model_config = configs['model']
detection_model = model_builder.build(
model_config=model_config, is_training=False)
# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(
model=detection_model)
ckpt.restore(os.path.join(str(filenames[-1]).replace('.index','')))
def get_model_detection_function(model):
"""Get a tf.function for detection."""
@tf.function
def detect_fn(image):
"""Detect objects in image."""
image, shapes = model.preprocess(image)
prediction_dict = model.predict(image, shapes)
detections = model.postprocess(prediction_dict, shapes)
return detections, prediction_dict, tf.reshape(shapes, [-1])
return detect_fn
detect_fn = get_model_detection_function(detection_model)
