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4f2034a7-ac6c-489a-881d-3aef4f1d0c0d | Industrial Language-Image Dataset (ILID):
Adapting Vision Foundation Models for Industrial Settings
Keno Moenck1,*Duc Trung Thieu1Julian Koch1Thorsten Sch ¨uppstuhl1
1Hamburg University of Technology, Institute of Aircraft Production Technology
github.com/kenomo/ilid
In recent years, the upstream of Large Language Mode... |
1db71561-d2b5-4705-9d1a-510b84df1992 | Here, fine-tuning the models or transfer learning
on domain-specific data is unavoidable when objecting to
adequate performance. In this work, we, on the one hand,
introduce a pipeline to generate the Industrial Language-
Image Dataset (ILID) based on web-crawled data; on the
other hand, we demonstrate effective self-s... |
bf8e7d1a-62eb-43ac-a9b5-16e6796b80e8 | In the scope of training deep mod-
els, industrial contexts1lack everyday objects and scenes,
typically covered by publicly available datasets, which is
why applications in these specialized domains here demand
*Corresponding author: keno.moenck@tuhh.de
1We define the industrial domain as follows: industrial activities... |
bbdcb2e9-d7eb-4157-959f-38d1e2a4c0f5 | The availability of curated, publicly accessible datasets
specific to industrial needs is exceedingly sparse, e.g., the
MVTec [5–8], VISION [9], or tool recognition [10] datasets
encapsulate only a limited spectrum of objects and sup-
port only a handful of trainable tasks based on the provided
ground truth annotations... |
2cd18c18-9a39-419d-99e6-a944263c0bb5 | These models,
e.g., BERT [13], the well-known GPT-n series [14–16], or
Llama [17–19], learn rich knowledge representations capa-
ble of transcending to various downstream tasks. The shift
in AI drives single tasks and single-modalities learners to a
paradigm encompassing diverse tasks and multimodalities,
which more cl... |
25991450-a7f3-4f85-a8be-0e2832a4b0fd | Be-
sides, given such large, partially unstructured datasets, only
self-supervised or unsupervised methods are able to learn
from the data effectively.
A self-supervised approach capable of learning from text
and image modalities is contrastive learning, in which a
model learns to distinguish between positive and negat... |
525e99cf-50bc-4f82-8020-5791a0cd2a22 | trastive learning by contrasting positive and negative sam-
ples in a batch, in the case of vision and language, is based
on a text and image encoder. The idea is that the encoders
are trained to output embeddings for the image and text, in-
creasing the similarities of positive samples by decreasing
the distance in th... |
29480e67-4a8c-45bd-8e09-382af24a9a92 | Since they are based on web-available data, not all cleaned,
post-processed, and curated datasets are published, as in the
case of CLIP.
„…
levelling
feet
round
“
0.64
„…
collet
“
0.24
„…
aluminium
profile
“
0.05
„…
button
“
0.03
„…“
…
„…
button
“
0.33
„…
collet
“
0.22
„…
magnetic
ball
joint
“
0.22
„…
axial
j... |
71f07a77-fb41-497a-ad90-e342aecd5cd6 | CLIP on the task of classification after (a) transfer learn-
ing on the Industrial Language-Image Dataset (ILID) and (b) the
zero-shot baseline results.
VFMs exhibit rich knowledge representations, are adapt-
able to various downstream tasks, and generalize better than
conventional models, but only to a certain extent ... |
e589ab17-f7e4-46b5-91da-6eeab7db4980 | In this
work, we try to make a step in the direction of utilizing
VFM capabilities in specialized industrial domains by con-
tributing three-folded:
• We propose a method to generate the IndustrialLanguage-Image Dataset (ILID) from web-crawled data
and release a version that covers objects from different
industrial-rel... |
4c4b78fe-25b8-45cb-9b4a-86b79f805c54 | Besides,
comparing only one established model on the data increases
the focus, clarity, and depth of the findings in the scope of
this work. Nevertheless, we encourage the reuse of ILID
with other strategies or also employ further fine-tuning and
transfer learning strategies.
The rest of this work is structured as foll... |
b4a9809a-e533-4111-88e8-ef4bd77d466d | VFMs in industrial applications
Code recognition, object or position recognition, complete-
ness, shape/dimension check, or quantitive or qualitative
inspection are typical vision applications in manufacturing
[26]. While in manufacturing, these are often suited toward
narrow fields of view and close to the object; in ... |
34dca95d-b4e3-4aa5-88da-46ff6d073f03 | 2Since the data from the web do not belong to us, we are not allowed
to publish the images and texts, but we provide the final post-processed
metadata, which can be used to reassemble the dataset. Please contact the
corresponding author.
2 |
b9b7dfea-5a24-475a-bb5a-490955f095f8 | (3) (Downstream) tasks
(1) Industrial Language
-
Image Dataset (ILID)
Web catalog crawling
Dataset processing
Image
Image
Classification
Segmentation
„…hinge…“
„…handle…“
„…
rod
end…“
„…“
Text
Encoder
Image
Encoder
maximizing the score for
non
-
contrasting samples
{
bores for counter
-
sunk screws
}
{
h
inge,
det... |
a806b244-2c38-443b-80f2-9697786a49e8 | detachable
, type R,
xmm
“
,
data:
[
„Hinges are distinguished by their
compact and robust construction.
The assortment of materials...”,
„Zinc die casting ...”,
„...“,
]
}, { … }, …
]
TextFigure 2. Overview of this work’s method: (1) generation of the Industrial Language-Image Dataset (ILID), (2) transfer learni... |
53a75ba1-4243-475f-9a64-ddb1fd3f6b2c | [28] discusses the
abilities of the Segment Anything Model (SAM), a class-
agnostic segmentation model that outstandingly generalizes
to unseen objects and scenes, in the context of vision ap-
plications in the aircraft industry, including manufacturing,
intralogistics, and MRO. [30] name two use cases in PCB
defect in... |
b875aca6-6228-4e2b-b387-4d0bd9d96e3a | It is not an entirely novel approach; however,the origin of the idea of learning from perceptions in natural
language is not exactly dated to specific research. In 1999,
[33] explored retrieving words for unknown images based
on statistical learning to predict nouns and adjectives. In
2007, [34] demonstrated learning i... |
aed90d94-616b-44e3-8402-7f73f488c913 | The encoder can be reused from other models
and training, e.g., demonstrated by OpenScene [38], which
employs a frozen text and 2D image encoder while training
a 3D point cloud encoder for language-based 2D/3D scene
understanding. The encoder models are trained to comple-
ment and comprehend each other fully by encodin... |
ad48f083-191b-4bb7-8906-d06fd1105b3a | images that are not connected, as shown in Fig. 3. Besides
prompting for the object’s name, a sufficiently trained text
encoder would encode, e.g., conceptual close activities near
the object’s name embedding (s. Fig. 3).
{photo of a
hinge}
{cat}
{house}
{gripping}
Figure 3. Joint embedding space of text and image repr... |
50dd7115-c548-4643-93c9-61ad337f8d0a | The embedding similarities between pairs are represented
by the cosine similarity metric, which is used to optimize
the cross-entropy loss in order to build the most optimized
versions of both the image and text encoder at the same
time.
2.2.2 Performance
Zero-shot CLIP achieves similar performance or even out-
perform... |
834949f2-3bdf-42af-a550-565095b05870 | When comparing CLIP
and other large pre-trained n-shot models such as BiT-M
[42] and SimCLRv2 [43], CLIP’s authors depict that the
zero-shot performance outperforms all other models on the
metric of average accuracy score up to 4-shot linear classi-
fiers trained on the same dataset [23]. The limitations arethat scalin... |
031e103c-a527-4ce7-9ff4-35108a47f1f4 | De-
CLIP employs supervision across modalities as well as self-
supervision within each modality, whereas ReCLIP at first
learns pseudo labels and then applies cross-modality self-
supervision. CoCA, on the other hand, skips cross-attention
in some decoder layers to encode unimodal text represen-
tations and cross-atte... |
99376fdf-7135-4b57-a274-4e64cd677111 | Since this work focuses mainly on the training data, we
will not evaluate all the individual strategies that aim to in-
crease performance. Instead, we use the vanilla CLIP model
and employ basic transfer learning methods that we can em-
ploy with limited hardware resources, which also demon-
strate the effectiveness i... |
8322bfaf-71c2-4ec1-b80a-9a498ec4476c | This process is usually more suitable for
adapting to small sets of data that are closely related to the
dataset CLIP was pre-trained on, such as everyday objects
and general concepts. On the other hand, in tasks where the
dataset is too specific, i.e., specialized knowledge, transfer-
learning is better suited, as it ... |
3925cbe7-e2c3-4e9b-a56c-d12057dfe45f | (2) Web crawling
(3) Pre
-
filtering
(4)
Processing
(5) Post
-
filtering
(6) Downloading
(1) Online catalogs
[
{…},
{…},
…
]
LLM
[
{…},
{…},
…
]
Figure 4. Dataset generation pipeline resulting in the Industrial
Language-Image Dataset (ILID).
the zero-shot model are preserved and optimized for gen-
eralization to novel... |
f1c46544-8d6d-41fa-8fd4-7e9dd99463b7 | Notable works in transfer-learning of CLIP are adapter-
styled tuning, e.g., CLIPAdapter [52], and prompt learning,
e.g., CoOp [53] and APEX [54]. CLIPAdapter (s. Fig. 5)
adds dense down- and up-sampling layers on top of CLIP
either to the image, text, or both encoders. Thereby, only
the most prominent features are com... |
475e70ad-9476-4c0f-9199-45f497467194 | Concretely, CoOp creates
a set of learnable vectors, initialized by random values or
given text embeddings, which, during training, the model
adapts to. APEX is the most recent approach that also eval-
uates adding learnable tokens to the residual transformer
blocks in the image encoder. Besides, APEX introduces a
resi... |
a08a7bd6-0dc8-49b2-a50b-c45bcff0d968 | Dataset generation pipeline
Following a typical data pipeline structure, including data
selection, transforming, and pre-/post-filtering (s., e.g.,[22]), we employed six steps (s. Fig. 4) to generate the In-
dustrial Language-Image Dataset (ILID). Each of the steps
results in a structured JSON document containing all t... |
fa851881-cb33-4fbe-8ec8-be2080599eeb | 2.Web crawling data from online catalogs follows two ba-
sic steps: getting the sitemap from robots.txt and writing
a crawler for the specific structure of the product pages.
The top-level robots.txt file delineates the Robots Exclu-
sion Protocol , which guides crawlers and other bots on
which sections of the website ... |
72e39c3d-be61-4221-bc66-a394bd766afd | Besides a
central label tag for each entry, we save an unstructured
list-typed data object, which can contain all other avail-
able information about the product, like materials, finish,
colors, etc. Using the sitemap as the initial crawling en-
try point is a common step in every online search engine.
3. In the pre-fi... |
0e657c86-7ced-4273-830b-b4c34fe59fa3 | We define these as (1) a
long label describing the product, (2) a short label that is
shorter than the long label, (3) a description of the prod-
uct, (4) the material, (5) the finish or color of the product
(s. also Fig. 2). In our study, we used Llama3-8B [19]
3Scrapy: A Fast and Powerful Scraping and Web Crawling Fr... |
efea53e0-fdc6-4cda-8645-4442bffde53c | in the fine-tuned instruct version (s. 6 for the respective
prompt). We ask the LLM not to output any numbers or
sizes; additionally, we remove them from the initial data
since, on the one hand, we do not expect that a 2D image
task can identify or recognize any dimensional quantities
given different camera positions a... |
4c4ab7e9-826f-4c0d-833c-a3e09d3c115a | With the given steps, we are able to extract a product’s
image and a structured set of five pieces of information. Be-
sides, we observed that even a small model such as Llama3-
8B in its instruct fine-tuned version is mostly able to extract
the demanded information from the bunch of unstructured
text. We show an excer... |
538c8a47-e1a5-470e-b698-5748ee193530 | While we estimate that the images we want to learn but
also infer from show similar characteristics as CLIP’s in-
distribution data compared to other fully out-of-distribution
image data as in the case of, e.g., PatchCamelyon [41] (s.
Sec. 2.2.2), we employ on the image stream only a sim-
ple trainable adapter as propo... |
1f5f327c-74ec-44ab-95d4-8cfae66b27af | In contrast, prompt engineering is a crucial task for
learning, as well as inference with textual, promptable mod-
els. In a preliminary study, we have already observed that
vanilla CLIP performs differently, given different prompt
templates like ”a photo of {}. ”compared to ”a photo of{}, an industrial product. ” The ... |
8b3feab4-6369-4f1c-bc12-6199de352bc7 | 3.2.2 Training
During the pre-training of CLIP, a very large minibatch
size of 32,768was used, which took for the largest Vision
Transformer (ViT) configuration (428M parameters) a to-
tal of 12 days on 256 V100 GPUs [23]. Compared to the
pre-training, during transfer learning with CoOp, we have a
total of cn×512traina... |
6176c154-1523-4a2a-b1aa-dede6f3b6c30 | In contrast, fine-tuning or transfer learning approaches typ-
ically contrast all possible class labels against a set of im-
ages [52–54, 56] during the benchmark studies on datasets
like ImageNet [57], which is why non-contrasting samples
are not possible as long as the classes are conceptually far
away from each othe... |
501e42cb-a4c1-4765-9bfb-b94773f45408 | We changed from
vanilla SGD to Adadelta [58], an SGD optimizer that adapts
learning rates over time using only first-order information.
4. Experiments
In this section, we present a series of studies utilizing ILID,
designed to evaluate the effectiveness of the dataset and
transfer learning approach for different tasks.... |
337bb4be-b7a5-40b3-ac5e-3ed9183af4c9 | Adapter
+
1
-
α
α
Adapter
Image
Encoder
Text
Encoder
Adapter
Prediction
„…hinge…“
„…handle…“
„…
rod
end…“
[P
1
]
[P
2
]
[
P
x
]
[..]
„hinge“
Image
Encoder
Text
Encoder
Prediction
Learnable
Frozen
(a) CLIP
Adapter
(b)
CoOp
Figure 5. The architectures used in this work: (a) CLIPAdapter [52] and (b) CoOp [53].
4.1.... |
0549c4e5-cd6e-49f3-b339-ab6d3971b3cc | steel
stainlessclampplungerclampingleverball
indexingadjustablewith
aluminumhandleknob
connectorspringhingelatchprofileplastictogglelinearhandswivelgrip
bearinggear
assemblyvalve
handlesstarfeet
handwheellevelingscrewlockplaterollersetjoint
aluminium103
Figure 6. Top- 40word occurrences in label label short .
Fig. 6 de... |
bb6c01d7-9118-4e87-8855-d7487f3c3ad3 | So, nearly every sample has a unique description , but only
two labels, on average, share the same label short . Since
we do not account for minor preposition words like a/an/the
in the labels, the labels are slightly more equal on the se-mantically level. However, we estimate a good diversity
in the dataset, and since... |
a7b29b64-6277-48a8-917d-eb2ee904c2b1 | Setup
We build upon the code base of Dassl [59, 60] and trained on
a single 4090 GPU. We chose random sampling, an image
input size of 224×224, and CLIP’s pre-trained ViT-B/16
as the image encoder instead of the ResNet version, as ViTs
have much less image-specific inductive bias than CNNs.
We initialized CoOp with a c... |
8056e75c-3cb2-47c3-89f5-e0214ca27a68 | We use Adadelta [58] with a learn-
ing rate of 0.15and a cosine learning rate scheduler with a
weight decay of 1e-3. Besides, we used 3warm-up epochs
with a constant learning rate of 1e-2to prevent rapid param-
7 |
40c40951-06d9-45f5-9af1-8afcaa0da539 | eter changes in the initial training stages, which can lead to
early overfitting.
4.3. Quantitative results
Since we do not have a different real-world language-
image dataset at hand, we used 6-fold cross-validation
during the evaluation of the different model architectures.
Fig. 7 depicts the validation results of tr... |
23165372-5e95-4fa7-84d6-b55040da54f6 | 1) is that all transfer learning
approaches effectively outperform CLIP’s zero-shot capa-
bilities, even the top-3 accuracies after training for ≈20
epochs, highlighting that the ILID is out-of-distribution.
Even training on the less information-rich label short out-
performs CLIP’s zero-shot capabilities.
CLIP highly ... |
409cd147-2f6e-4950-ae00-1b4e15ce643a | As expected, the more trainable weights we add, the
better the model adapts to the data, while the overall do-
main generalization to the in-distribution data achieves in
the case of label short andlabel long an accuracy of maxi-
mum 79.93% and84.31%, respectively, an image adapter is
crucial to effective transfer lear... |
93a6d58b-205f-4186-a25d-6a872dfa52be | To gain an understanding of how transfer learning af-
fects the embeddings further, we derived the image and
text embeddings after training on the full ILID given the
label label short for100 epochs. Fig. 8 visualizes thehigh-dimensional embeddings of the same 100 samples.
With each transfer learning method, adding mor... |
5b9e24eb-f3a0-4c94-a429-f92a8be0d001 | Prompting for materials
Besides training and testing on the label short and la-
bellong, we additionally trained CoOpIATA for 100epochs
on the material label with the initial prompt ”X X X X a
photo of an industrial product with material {}”. We then
evaluated the zero-shot and CoOpIATA performance on the
images depict... |
de75c89f-f3a9-454a-8a17-9d66c4efac40 | In-
terestingly, a prompt including {aluminum }results in lower
scores than using the word {aluminium }, which points out
that the subtleties or discrepancies of the language used in
an industrial context are not mapped after the transfer learn-
ing nor in the zero-shot case. That is why we added both
words in the prom... |
6febc2bb-52b7-4d86-a084-b70a1aea7cdd | These results
again underline a natural language supervised VFM’s rich
multimodal capabilities.
4.5. Language-guided segmentation
A typical downstream task is a language-guided segmen-
tation utilizing the Segment Anything Model (SAM) [61].
SAM is a class-agnostic point promptable image segmen-
tation model that output... |
80760d96-3b98-4193-8dd1-95de3b02da0b | 10 20 30 40 50 60 70 80 90 100
Epoch2030405060708090x-val/acc (%)(a) Label Short
CoOp (top-1)
CoOpIA (top-1)
CoOpIATA (top-1/3)
CLIPAdapter (top-1)
Zero-shot CLIP (top-1/3)
10 20 30 40 50 60 70 80 90 100
Epoch2030405060708090x-val/acc (%)(b) Label LongFigure 7. Results of 6-fold cross-validation during transfer learnin... |
65434ad0-b437-4657-a987-cacb6c2822f1 | sample a point grid and subsequently use Non-Maximum
Suppression (NMS) to diminish through merging a large
set of masks to form more precise proposals. In the sim-
plest form, language-guided image segmentation based on
SAM and CLIP can be employed by applying CLIP onto all
generated masks, which we cut out with a part... |
e61413d8-e564-4d8c-8155-334851f1a586 | For completeness, it should be
mentioned that we did not compare it against the other ap-
proaches, e.g., CLIPAdapter.
Fig. 10 depicts the segmentation results in a challenging
scene composed of multiple collets stacked on a trolley. The
zero-shot results do have many true positives, but overall,
we are not able to obs... |
232e949e-3543-421e-8ba4-a5194f23808a | Conclusion and Outlook
Using VFMs as a building block in an industrial vision ap-
plication is a promising and transforming technique, im-
proving systems’ accuracy, speed, and reliability, e.g., in-
volved in inspection, robotic control, parts identification,
and process control, leading to enhanced operational effi-
... |
502cc711-e1ad-46d0-90c9-68e606cfdb1e | (a)
(b)
(c)
(d)
(e)
Figure 9. Five different real-world images used for prompting material properties.
Input
Zero-shot CLIP
Ours
Figure 10. Language-guided segmentation results given prompt ”collet” compared to zero-shot CLIP under the same settings (segmentation
properties and thresholds).
Table 2. Scores on pre... |
b2896e49-1897-42de-bfa3-3687981694b2 | 097
”aluminum or aluminium” 0.043 0.143 0.166 0.238 0.094
”anodized aluminum or 0.030 0.143 0.070 0.064 0.023
aluminium”
”plastic” 0.352 0.244 0.099 0.107 0.280
”brass” 0.156 0.020 0.223 0.282 0.240
CoOpIATA trained on the material label
”steel” 0.007 0.033 0.950 0.829 0.137
”polyamide” 0.135 0.368 0.004 0.008 0.361
”t... |
39ce2dc5-052b-4398-b094-3eeeb721dacb | 020 0.011 0.001
”anodized aluminum or 0.007 0.374 0.003 0.007 0.001
aluminium”
”plastic” 0.694 0.135 0.008 0.041 0.077
”brass” 0.139 0.000 0.012 0.104 0.264
introducing the Industrial Language-Image Dataset (ILID)
to bring industrial context into CLIP and evaluating ef-
fective self-supervised transfer learning from th... |
618b1138-0a2c-4b55-bb2a-bc72dca91dad | One can argue that the bigger digital giants like OpenAI
or Meta can also incorporate industrial data during the train-
ing of their models; however, the overall proposed method
from dataset curation to fine-tuning CLIP also suits, e.g.,
companies with intellectual property constraints or limita-
tions in available com... |
bcb5b328-fb24-4104-97d8-fe4dcf0e0875 | The con-
fusion between the same concept but differently termed
in American (aluminium) and British (aluminum) English
shows that there is a need for pre-training of the text encoder
with broader natural language, e.g., even with extended con-
text, which would enable not only training on shorter image
labels. Further,... |
3dba94ab-c179-40e0-bf74-4b3bfc2ab21c | most limiting characteristic is including or inferencing with
dimensional quantities, which can hardly be solved when
training on images captured with different cameras and their
individual intrinsics.
With this work, we hope to encourage the industrial com-
munity to employ and work on using VFM in the industrial
doma... |
6d36576a-041a-4ef7-9743-ac41309a839f | 2, 3, 5, 11,
and 12) in this publication.
CRediT author statement
K. Moenck: Conceptualization, Methodology, Software,
Validation, Formal analysis, Investigation, Resources, Data
Curation, Writing – original draft, Writing - review &
editing, Visualization, Supervision, Project administration;
D.T. Thieu: Conceptualiza... |
b76eaa09-c104-4621-bd39-252dffdf7d7f | doi:10.1016/j.procir.2021.11.211 . 1
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59e422f0-5a1d-4530-8101-a848632edf1f | 6. Llama-3 prompt
We followed basic prompt assembly as described for
Llama-2 [18] because up to the date of this publication,
there has still been an in-depth explanation of Llama-3
missing. The Llama-2 chat version was trained with a va-
riety of system prompts following patterns like ”You are a
helpful, respectful an... |
8a35fc0b-1b11-4724-ab0a-56272e090675 | \n
Do n o t ask f o r f u r t h e r d e t a i l s or s t a t e a d d i t i o n a l
q u e s t i o n s . \n
Do n o t add a d d i t i o n a l i n f o r m a t i o n or d e t a i l s t h a t a r e
n o t g i v e n by t h e u s e r . \n
Listing 2. User prompt used in the ILID generation pipeline’s text
transformation step. |
019286ba-0536-437e-a264-caa82dda1922 | Summarize ’ Label : {{}} Text : {{}} ’\n
r e t u r n i n g t h e f o l l o w i n g i n f o r m a t i o n : \n
( 1 ) a lo ng l a b e l or name of t h e p r o d u c t w i t h o u t i d s ,
numbers , codes , or s i z e s
( 2 ) a s h o r t l a b e l or name of t h e p r o d u c t w it h a maximum
of 4 words and s h o r t e... |
b35c4027-8c31-4c57-97a3-21e8660cdb0e | or s i z e s
( 4 ) m a t e r i a l wi th a maximum of 5 words
( 5 ) m a t e r i a l f i n i s h / c o l o r w ith a maximum of 5 words
7. Excerpt from the dataset
Fig. 11 and Fig. 12 depict each two samples from the
ILID given the keywords ”hinge” and”locking assembly” .
Based on the language label, we can observe that... |
5c75097a-0975-4785-be35-fb32e69136f3 | 13, we prompted
for”socket” , whereas zero-shot CLIP does not predict any
mask as positive, while our approach segments all sockets.
In Fig. 14, the results of our most challenging scene are
depicted, in which we prompt for ”bracket for construction
profile” . |
22cea8fb-58d1-4306-8a2e-537883a30c34 | The brackets are imaged far differently than the
ones from catalog images, and sometimes they are barely
{ "id": "...", "image": "...",
"label_short": "clevis mounting hinge",
"label_long": "bracket hinge for clevis
mounting",
"description": "Rigid hinge for clevis
mounting ap... |
98de050c-0103-429f-a73a-4306f28510cd | { "id": "...", "image": "...",
"label_short": "locking qpq assembly",
"label_long": "locking assembly with qpq
coating",
"description": "High corrosion resistance and
improved fatigue strength for
food safe applications",
"material": "steel",
"material_f... |
16b66c62-6e31-4d8c-8910-cb58586b595b | At first sight, the results do not show good perfor-
mance, especially since we have a few non-detected brack-
ets and a few false positive predictions. We explain the false
positive on the top with the cropping strategy, while we
have no explanation for the false predictions on the lower
right. The false positives can... |
0aa5e298-dcff-4b44-80a3-1cb0265652f2 | Input
Zero-shot CLIP
OursFigure 13. Language-guided segmentation results given the
prompt ”socket” compared to zero-shot CLIP under the same set-
tings.
Input
Zero-shot CLIP
OursFigure 14. Language-guided segmentation results given the
prompt ”bracket for construction profile” compared to zero-shot
CLIP under the same ... |
This dataset was created using Corpus Creator. This dataset was created by paring a corpus of texts into chunks of sentences using Llama Index.
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